Badge earners who've successfully completed the General Assembly AI for Workplace Productivity Workshop have received hands-on practice to effectively use the power of AI to enhance their professional and personal productivity. They understand generative AI and can use it to summarize large text efficiently and effectively, as well as automate manual and mundane tasks.
Generative AI refers to algorithms that generate novel, varied, and realistic content by learning from extensive data sets, encompassing text, images, and audio.
Prompt Engineering refers to the process of crafting and refining input prompts for AI language models, aiming to improve their performance and output relevance in various use cases.
Prompt Tuning is the process of fine-tuning pre-trained language models by adjusting prompt inputs to optimize output for specific tasks without altering the model's core architecture or parameters.
ChatGPT is a language model AI designed to produce authentic human-like responses, engaging in dialogue, addressing inquiries, and performing diverse language tasks across various domains and applications.
AI strategy refers to the methodical planning and execution of incorporating artificial intelligence into a company to enhance its performance, creativity, and market position.
By the end of this workshop, successful participants will be able to: - Understand Generative AI fundamentals: learn about the impact AI has on various industries, its benefits and limitations, and what kind of tasks it should actually be used for. - Optimize prompts: learn about key factors that influence the performance of large language models, and practice testing different prompt strategies to improve their results. - Summarize content efficiently: learn to summarize large text efficiently and effectively, plus get best practices to ensure your summary is an accurate representation of the larger text. - Use LLMs for automation: Learn when and how to use generative AI to automate or augment mundane tasks, and build an automation plan for your own projects that you can start implementing right away. - Apply Generative AI to real-world situations: Evaluate the different ways that generative AI can be used in real-world business cases, and develop and implement your own simple AI solution to a real-world business problem.
Badge earners who've successfully completed the General Assembly AI for Data Analysis and Visualizations Workshop have received hands-on practice to leverage AI to automate their data analysis and build visualizations. They understand generative AI and can use it to effectively analyze data using different prompting techniques and create impactful visualizations—from maps and bar charts, to scatterplots and more.
Data Visualization is the process of converting data into visual forms like charts, graphs, and maps to effectively communicate information.
By the end of this workshop, successful participants will be able to: - Understand Generative AI fundamentals: learn about the impact AI has on various industries, its benefits and limitations, and what kind of tasks it should actually be used for. - Optimize prompts: learn about key factors that influence the performance of large language models, and practice testing different prompt strategies to improve their results. - Use Generative AI for data analysis: Get a feel for the power of AI as a data analytics tool. Learn to use advanced data analysis in ChatGPT to analyze data in spreadsheets, find and replace, edit Python files, and build a predictive model. - Identify trends and insights: learn about different types of trends and insights, and how to apply AI techniques to identify those trends and insights in data at scale — then use those insights to make more informed decisions. - Create data visualizations with AI: Learn about the different types of data visualization that can be generated using ChatGPT, and how to apply prompt and refinement techniques to generate interactive data visualizations.
Badge earners who've successfully completed the General Assembly IT & Cybersecurity Fundamentals Workshop have learned foundational knowledge to understand hardware, software, and the networks the power work forward. From installation, configuration, and troubleshooting, to cybersecurity best practices and operational procedures for device and data protection, they have a clear picture of what IT and cybersecurity professionals do and have received the hands-on experience necessary to move forward toward industry certifications in IT and cybersecurity fields.
Hardware encompasses the tangible parts of a computer or electronic system, such as processors, memory, circuits, and peripherals.
Hardware assembly is the process of assembling physical components to create functional electronic devices or computer systems by following specific instructions.
Hardware installation is the act of setting up and configuring computer hardware components to work together seamlessly within a system.
Storage hardware refers to the physical devices intended for storing digital information, comprising hard disks, solid-state drives, tape systems, and network-attached storage devices.
Hardware troubleshooting is the process of identifying and resolving issues with computer hardware components and peripherals to ensure optimal system performance.
Desktop Operating Systems are computer software responsible for managing resources and providing a user interface for personal computers, with examples including Windows, macOS, and Linux distributions.
Linux is a widely-used, open-source operating system known for its stability, security, and commitment to free software principles across various devices.
MacOS is a proprietary operating system created by Apple Inc. for Macintosh computers, featuring an intuitive interface, strong security measures, and smooth integration with other Apple products and services.
Windows 10 is a Microsoft operating system for PCs, featuring Cortana, Microsoft Edge, and an improved Start Menu to enhance user experience and productivity.
Mobile devices are compact, transportable electronic devices with computing functions, such as smartphones, tablets, and wearables, designed for communication, data access, and mobile digital engagement.
Mobile operating systems are software platforms created for smartphones and tablets, managing hardware functionality and providing an environment for running mobile applications.
Wireless networks are systems allowing devices to communicate and connect without physical cables, using electromagnetic signals to transmit data over varying distances.
Networking Basics encompass the essential skills and information necessary to comprehend computer network structures, such as protocols, routers, switches, and the OSI model.
Internet of Things (IoT) is the interconnected network of devices that can communicate and share data via the internet, turning ordinary objects into smart systems.
Internet of Things devices consist of interconnected appliances with embedded sensors, processors, and communication tools, enabling them to gather, transmit, and respond to data from their surroundings.
IT Infrastructure encompasses the physical and virtual components facilitating data management and analysis within a company.
Help desk support refers to the technical assistance offered to users facing problems with technology products or services, typically through telephonic, email, or chat-based interactions.
By the end of this workshop, successful participants will be able to: - Understand the symbiotic relationship between hardware and software. - Identify the impact of mobile devices on modern computing. - Understand what a network is, including their major hardware and software components, along with why we network devices. - Understand the role of IoT devices in enterprise environments.
Badge earners who've successfully completed the General Assembly UX Design Bootcamp committed themselves to an immersive educational experience where they received the best of live learning and expert-led practice in user experience design. They've learned in-demand UX skills in wireframing, prototyping, user research, usability testing, visual design, and user interface design—plus a 360-degree view of AI—from the foundational principles of generative AI to how to write effective prompts. General Assembly UX Design Bootcamp grads are motivated and ready to excel in UX careers.
User experience design is the practice of crafting products that deliver valuable and engaging experiences for users, emphasizing usability, accessibility, and user satisfaction throughout the interaction.
User research involves studying target users' needs, behaviors, and motivations to inform product and service design and development.
User interviews refer to structured conversations with users, aiming to gather insights on their experiences, behaviors, and needs for product or service design and improvement.
Competitive analysis entails the methodical examination of competitors' offerings, strategies, and market standing to uncover potential openings and hazards in a given sector or market.
Comparative analysis is a method that involves systematically comparing various subjects, datasets, or systems to highlight similarities and differences, ultimately to derive insightful conclusions and support decision-making.
Personas are imaginary individuals created to symbolize significant portions of a target market, guiding business, design, marketing, and product development decisions.
User Personas are research-based representations of target users, created to understand their needs, behaviors, goals, and motivations, informing design and development processes for improved user experience.
Sketching is the ability to create rapid, informal drawings to convey ideas, concepts, or designs using simple tools such as pencils or digital software.
Rapid prototyping is a process of creating and refining physical or digital models through iterative development, often using 3D printing or software simulation methods.
Figma is a cloud-based design tool that facilitates collaborative creation of interface designs, featuring real-time vector graphics editing and prototyping for various projects, including web, mobile, and product development.
Usability testing is an evaluation process where the user-friendliness and ease of use of a product or system are assessed by observing real users performing typical tasks.
Web Navigation is the efficient use of tools and techniques to locate, access, and retrieve information on the internet.
Typography refers to the skill of arranging type to enhance the legibility, readability, and visual appeal of written language in various contexts and mediums.
Color theory is the study and use of color principles to create visually appealing combinations and improve visual communication by strategically employing hues, saturation, and value.
Visual composition refers to the arrangement of elements within a space to achieve balance, harmony, and viewer engagement in visual media like photography, design, and fine art.
Responsive web design is the technique of designing websites that smoothly adjust to different screen sizes and devices, providing optimal layout and functionality on desktops, tablets, and smartphones.
Web Content Accessibility Guidelines are a set of international standards aimed at making web content more accessible to people with disabilities, improving usability for everyone and enhancing overall user experience.
UX Writing is the art of crafting simple, informative text for digital interfaces, aiming to improve user experience by guiding and informing users effectively within a product.
HTML is a standard language for creating and organizing web content, using elements and tags to define the meaning and layout of web pages.
CSS, or Cascading Style Sheets, is a language that specifies the visual presentation of HTML or XML documents, governing layout, colors, and typography.
Agile Product Development is a flexible, adaptive methodology that prioritizes customer value, collaboration, and iterative planning to efficiently deliver products.
Mind mapping is a method of visually organizing ideas, tasks, or concepts in a tree-like diagram around a central theme.
Information architecture refers to the structuring and organizing of digital content for improved user experience, usability, and manageability.
* UX Design Foundations * Understood the UX design process and its key phases, including discovery, planning, designing, testing, launching, and iterating. * Examined qualitative and quantitative UX methodologies to be applied throughout the design process. * User Research & Testing * Defined user problems and needs, refining assumptions through user interviews and synthesizing insights. * Analyzed content using audits and heuristic evaluations to determine areas of improvement. * Planned, executed, and synthesized user research and usability testing to inform design iterations. * Conducted competitive and comparative analyses to evaluate market positioning and user needs. * Design & Prototyping Skills * Developed rapid prototypes and design sketches using both digital and analog tools. * Applied visual design principles, including typography, color theory, and composition to create cohesive and appealing designs. * Built interactive UI components using tools such as Figma. * Designed user interfaces and web layouts using interaction design principles, responsive design, and accessibility standards. * Information Architecture * Developed intuitive navigation using sitemaps, journey maps, and clickable wireframes. * Tested the proposed organization of content through user card sorts and tree tests. * Advanced UX Techniques * Developed a content strategy, including UX writing, and balanced business objectives with user needs. * Incorporated generative AI tools to enhance design processes and efficiency.
Badge earners who've successfully completed the General Assembly Data Science Bootcamp committed themselves to an immersive educational experience where they received the best of live learning and expert-led practice in data science. They've learned in-demand skills in data analysis and visualization, statistical modeling, machine learning models, decision trees, natural language processing, neural networks, and Python—plus a 360-degree view of AI—from the foundational principles of generative AI to how to write effective prompts. General Assembly Data Science Bootcamp grads are motivated and ready to excel in data science careers.
Python is a versatile, high-level language, prioritizing readability and ease, with extensive libraries, commonly utilized for web development, data analysis, AI, and scripting tasks.
Data cleaning refers to the process of detecting and correcting errors, inconsistencies, and inaccuracies in datasets to enhance data quality and reliability for analysis.
Pandas is a Python library designed for data analysis and manipulation, offering specialized data structures and functions for handling numerical tables and time series.
Exploratory data analysis is the systematic examination of data sets to discover patterns, anomalies, and insights, using statistical graphics and data visualization methods.
Statistics represents the discipline of gathering, analyzing, and interpreting data to reveal patterns, trends, and connections, aiding in informed decision-making and forecasting.
Probability is a measure of the likelihood of an event happening, expressed as a number between 0 and 1, with 0 indicating impossibility and 1 representing certainty.
Linear regression is a statistical method used to establish a linear relationship between a dependent variable and one or more independent variables through fitting a linear equation to available data.
Decision trees are a method for decision-making and prediction, displaying choices, results, and related costs in a tree-structured format.
Logistic regression is a statistical technique that estimates the likelihood of a binary outcome based on one or more predictor variables, often used for classification and event prediction.
Ensemble models are a machine learning approach that merges various predictive algorithms to enhance precision and consistency through averaging or voting on their outputs.
Optimization Models are mathematical constructs that ascertain the most efficient solution by optimizing a function, subject to constraints and resource limitations, spanning diverse sectors and uses.
Web scraping refers to the automated extraction of data from websites, organizing it for analysis or use in different applications.
APIs consist of specifications and tools that facilitate the development and interaction of application software components, enabling seamless communication between various systems.
Unsupervised learning is a machine learning technique that recognizes patterns in unlabeled data, using algorithms to group information based on their similarities and differences.
Clustering is a statistical approach that categorizes data points or objects into clusters based on similar properties, facilitating pattern recognition and data analysis.
Principal Component Analysis (PCA) is a statistical method used to reduce the complexity of datasets by transforming them into principal components that represent the majority of their variance.
Neural networks are computational models that mimic the brain's structure, designed to learn patterns and make decisions through data-driven learning processes.
Natural language processing refers to the application of computational methods to interpret, comprehend, and produce human language, thus enabling machines to communicate with human language in a natural way.
Bayesian analysis is a statistical method based on Bayes' theorem that updates the likelihood of a hypothesis when new data or evidence is introduced.
A Naive Bayes classifier is a machine learning algorithm that uses Bayes' Theorem to classify data, assuming independence among features for efficient and scalable performance.
Time series analysis involves examining data sequences collected at regular intervals to discern patterns, trends, and cycles, facilitating future predictions and informed decision-making.
Recommendation systems are algorithms that predict and offer items users may like, improving user experience and engagement by providing personalized content, product suggestions, and customized services.
Git is a system for managing modifications to source code, using distributed version control to track changes and facilitate collaborative software development.
Github is an online platform for managing, hosting, and sharing code repositories, utilizing Git's distributed revision control system for collaboration and version control.
PySpark is an application programming interface (API) in Python for Apache Spark, created for handling massive data processing and analysis in distributed computing systems.
NumPy is a crucial Python library for scientific computing, offering tools for manipulating and performing mathematical operations on large arrays and matrices.
Matplotlib is a Python library that enables the creation of various types of high-quality visualizations, including static, interactive, and animated ones.
Seaborn is a Python library for data visualization, built on matplotlib, offering an intuitive interface to create visually appealing and informative statistical plots.
* Fundamentals * Mastered Python fundamentals, including variables, loops, functions, data structures (lists, dictionaries), and NumPy for navigating data sources and collections. * Utilized UNIX commands to navigate and modify file systems and managed code changes with git and GitHub. * Solved coding challenges and implemented solutions to task-based interview problems. * Exploratory Data Analysis * Applied key math concepts like statistical significance, probability distributions, p-values, confidence intervals, and correlation vs. causation using pandas, NumPy, SciPy and StatsModels. * Explored and visualized data through Matplotlib and Seaborn, leveraging pandas for cleaning, parsing, and reshaping data sets (indexing, grouping, and JOINing). * Designed experimental studies with clear problem statements and data frameworks. * Machine Learning and Algorithms * Implemented regression models (linear, decision tree) using scikit-learn and StatsModels, evaluating model fit and the bias-variance trade-off. * Applied classification models such as k-nearest neighbors (kNN, logistic). * Ran unsupervised learning models, including different types of clustering and also Principal Component Analysis (PCA). * Built ensemble models using decision trees, random forests, bagging, and boosting. * Practiced optimization techniques, including regularization and gradient descent, to fit and tune machine learning models. * Advanced Topics & Big Data * Explored neural network architectures, backpropagation, and their applications. * Built recommender systems to predict sample user data. * Conducted natural language processing (NLP) tasks, such as sentiment analysis, using Naive Bayes for supervised learning. * Modeled time series data with ARIMA models. * Practical Application * Scraped and processed website data for analysis, demonstrating proficiency in bootstrapping and resampling techniques to build data inferences. * Gathered data from APIs via Python. * Developed a professional portfolio with guidance from career coaches. * Completed data science projects, both individual and group, preparing for real-world scenarios and technical interviews.
Badge earners who've successfully completed the General Assembly Data Analytics Bootcamp committed themselves to an immersive educational experience where they received the best of live learning and expert-led practice in data analytics. They've learned in-demand skills in data acquisition and cleaning with SQL, along with Tableau, Python, and Power BI skills, statistics and math for data analysts, and analysis and interpretation with Excel—plus a 360-degree view of AI—from the foundational principles of generative AI to how to write effective prompts. General Assembly Data Analytics Bootcamp grads are motivated and ready to excel in data analytics careers.
SQL is a widely used programming language for managing relational databases, allowing efficient and accurate query execution, data retrieval, and administration.
Microsoft Excel is a versatile spreadsheet program created by Microsoft, employed for analyzing, presenting, and performing intricate calculations on numerical data in various fields and uses.
Power BI is a set of business analytics tools that transform data from multiple sources into dynamic dashboards and business intelligence reports, improving visualization and analytical abilities.
Tableau is a software for generating interactive, sharable visual representations of data, transforming complex raw information into comprehensible charts and graphs.
Plotly is a data visualization tool that enables the creation of interactive web-based graphs and charts using various programming languages, including Python, R, MATLAB, and Node.js.
Data governance is the coordinated management of data policies, practices, and standards to guarantee its quality, protection, and accessibility within a company, in adherence to regulatory requirements.
Data Analysis Expressions (DAX) are a set of functions, operators, and constants for performing complex calculations and analyzing data within Microsoft Power Pivot and Analysis Services.
Data storytelling is the skill of transforming intricate data insights into captivating, easily grasped stories that guide decision-making within a company.
Data modeling is the creation of simplified depictions of intricate data systems, enabling effective database development, storage, and precise data access for various organizational purposes.
Machine learning is a subset of AI concerned with developing systems that learn and enhance through experience, employing algorithms to analyze data, learn from it, and make decisions autonomously.
Sentiment analysis is a method used to determine an author's attitude towards a subject or product by examining and classifying opinions expressed in written text.
* Data Quality & Ethical Practices * Identified limitations and quality issues in unfamiliar datasets, including missing data or unreliable sources. * Diagnosed missing data types and assessed dataset reliability using an established checklist. * Explained ethical and regulatory issues in data acquisition, including GDPR and landmark data governance legislation. * Applied ethical data scraping principles to real-world scenarios. * Statistic Analysis & Modeling * Differentiate between key descriptive statistics, such as mean and median, and their appropriate use. * Applied linear regression principles, minimizing residuals and evaluating models using RMSE. * Chose and justified appropriate models to solve given problems, identifying independent variables and considering bias in data and models. * Conducted exploratory data analysis in Pandas and evaluated metric significance. * Tools & Techniques * Used tools such as SQL, Excel, Tableau, and Python to compute statistics, clean data, and analyze datasets. * Applied syntax and commands to clean, transform, and analyze data while debugging queries and applying Boolean logic in SQL. * Selected appropriate visualizations to communicate insights effectively * Created data visualizations using tools like Matplotlib, Seaborn, and Plotly. * Deployed a Python script on a cloud service to demonstrate continuous monitoring and analysis. * Analytical Workflow & Communication * Followed the analytics workflow to plan and implement full data analyses, selecting appropriate calculations and determining required data cleaning or manipulation. * Demonstrated algorithmic thinking by breaking down complex analytical questions into smaller steps. * Delivered a concise technical presentation to a non-technical audience. * Data Governance & Agile Methodologies * Identified advantages and disadvantages of relational and non-relational databases. * Explored agile working frameworks, including Scrum and Kanban, and their applications in collaborative projects. * Demonstrated understanding of continuous analysis processes and data governance best practices.
Badge earners who've successfully completed the General Assembly IT Bootcamp committed themselves to an immersive educational experience where they received the best of live learning and expert-led practice in information technology. They've learned the foundational IT skills needed to become A+ and Network+ certified, including hardware component knowledge, networking basics, computer and operating system fundamentals, software troubleshooting, security, and scripting, network design and management, wireless protocol and data security skills—plus a 360-degree view of AI—from the foundational principles of generative AI to how to write effective prompts. General Assembly IT Bootcamp grads are motivated and ready to excel in IT careers.
Computer hardware refers to the physical parts of a computer system, such as the CPU, memory, storage devices, and peripherals, that enable its functioning.
Network protocols are standardized guidelines for data transmission and routing, ensuring secure and efficient communication among devices in digital networks.
Network configurations refer to the setup and management of hardware and software components within a computer network, aiming to ensure reliable communication and efficient data exchange.
Network design is the strategic planning and structuring of a network to ensure efficient connectivity, communication, management, and scalability within an organization or between multiple sites.
Software troubleshooting is identifying and fixing problems in software applications to ensure they work correctly and efficiently.
Cloud computing refers to the provision of internet-based services, such as data storage, computing power, databases, and software, which utilize scalable resources and operate on a pay-as-you-go model.
Ticketing systems are software applications that enable organizations to manage and monitor customer inquiries, issues, and tasks, streamlining problem-solving and communication processes.
Windows Operating System refers to a Microsoft-developed software suite that manages computer hardware, runs applications, and offers services to PC users.
Encryption is the act of transforming data into a coded form, utilizing algorithms to secure the privacy and accuracy of information against unauthorized access.
Access Control is the management of regulating and granting permission to users or systems for resource utilization in a computing environment, while preventing unauthorized access.
Incident management is a structured approach to addressing unexpected IT disruptions, aiming to restore normal service and minimize their impact on business operations efficiently.
Change management is a structured method for shifting groups and organizations from their present state to a planned future state, maximizing acceptance and reducing opposition.
Business continuity involves planning and readiness strategies to guarantee an organization's ability to function through disruptions, preserving critical operations and services.
Disaster recovery refers to the strategies and procedures implemented to prepare for, react to, and manage the consequences of disruptive incidents, preserving business operations and data integrity.
Remote support refers to the provision of technical aid and troubleshooting services via internet connection, without the need for an on-site presence.
Bash serves as the default Unix shell and command language for executing scripts, automating tasks, and system administration through command-line interfaces on Linux and macOS systems.
Windows PowerShell is a command-line shell and scripting language for system administration and automation tasks in the Windows operating system.
Infrastructure as Code refers to the practice of handling IT infrastructure through configuration files or scripts, allowing for automated and consistent deployment across various environments.
Wireless technologies refer to communication methods that transfer information without using cables, such as mobile networks, Wi-Fi, Bluetooth, and satellite communications.
Network virtualization is the process of consolidating hardware and software resources into a single, software-based administrative entity, creating a virtual network.
Network management is the administration, maintenance, and operation of network devices and communications to ensure optimal performance, reliability, and security of data and resources.
Document management encompasses the organized administration of records within a company, facilitating accessibility, safety, retrieval, and version control for both digital and physical files.
Asset management is a structured approach to managing assets, ensuring they are efficiently utilized, maintained, and disposed of, in line with an organization's goals and strategy.
Docker is a software platform that simplifies the process of deploying, scaling, and managing applications within containers, facilitating efficient development to delivery workflows across various environments.
Information security refers to the safeguarding of data, both digital and physical, from unauthorized access, use, disclosure, interference, alteration, or destruction, upholding its confidentiality, integrity, and accessibility.
* Hardware & Software Fundamentals * Identified and differentiated computer hardware components, including CPUs, RAM, storage devices, and peripherals. * Supported and resolved issues with mobile devices, such as smartphones, tablets, and laptops. * Developed skills to troubleshoot and resolve common and advanced hardware and software problems. * Explored operating system administration across Windows, Linux, and macOS platforms. * Networking & Infrastructure * Gained knowledge of basic and advanced networking topologies, protocols, and network device configuration. * Designed efficient network layouts and mastered wireless technologies for secure deployments. * Explored Infrastructure as Code (IaC) principles and tools to automate network configurations. * Analyzed networking protocols, essential services, and network management practices. * Cloud & Emerging Technologies * Explored cloud and virtualization concepts from leading cloud providers. * Gained familiarity with AI fundamentals, including Natural Language Processing (NLP). * Investigated networking with cloud computing and network virtualization. * Security Best Practices * Learned and applied the CIA Triad (Confidentiality, Integrity, Availability) to implement security measures. * Applied encryption, access control, and incident response procedures to fortify network infrastructure. * Developed expertise in infrastructure security to protect networks from emerging threats. * IT Operations & Documentation * Applied IT documentation, change management, and disaster recovery planning for operational resilience. * Created and maintained network diagrams, asset inventories, and IT governance policies. * Familiarized with remote support tools, such as remote desktop and assistance technologies. * Programming & Automation * Learned the basics of Python programming and used it to automate simple IT tasks. * Gained proficiency in shell scripting (Bash and PowerShell) and command-line interfaces for system management and task automation. * Version Control & Collaboration * Applied Git and GitHub for version control, mastering basic commands and GitHub flow. * Used ticketing systems to manage and collaborate on IT projects. * Operational Resilience * Devised strategies for disaster recovery and business continuity to ensure system and network resilience.
Badge earners who've successfully completed the General Assembly Software Engineering Bootcamp committed themselves to an immersive educational experience where they received the best of live learning and expert-led practice in software engineering. They've learned in-demand coding languages like Python and JavaScript, Agile and Scrum project management, testing and debugging, version control, and cloud computing—plus a 360-degree view of AI—from the foundational principles of generative AI to how to write effective prompts. General Assembly Software Engineering Bootcamp grads are motivated and ready to excel in software engineering careers.
Command-line interfaces (CLI) are text-based systems where users input commands to interact with computer programs through a terminal or console.
JavaScript is a programming language that primarily powers interactivity in web pages, enabling the execution of sophisticated functions such as dynamic content, interactive forms, and animations.
Document Object Model is a programming interface representing the structure of HTML and XML documents as a hierarchical tree of objects, enabling manipulation through scripting languages.
Express.js is a lightweight framework for Node.js, designed for developing scalable APIs and web applications, featuring efficient routing, middleware support, and server-side logic.
Node.js is a cross-platform, open-source JavaScript runtime developed for executing JavaScript outside web browsers, enabling the creation of scalable network applications.
REST APIs facilitate communication between software applications through standard HTTP methods, following principles such as statelessness, cacheability, and client-server architecture.
MongoDB is a cloud-optimized, open-source NoSQL database that handles various data types, offers dynamic schemas, and provides high performance, availability, and automatic scaling.
Database operations encompass the management, control, and maintenance of a database, involving activities such as data storage, retrieval, updating, and backup to uphold data integrity and availability.
Relational databases are organized systems that manage data in table format, defining relationships for efficient SQL querying, updating, and maintenance of sizable datasets.
Entity-relationship models are visual tools used to organize and represent data elements and their relationships, depicting real-world entities in a database schema.
Authentication is the procedure of confirming a user or system's identity, using methods like passwords, tokens, biometrics, or other validation techniques to grant secure access to resources.
React is a JavaScript library that facilitates the creation of user interfaces, utilizing a component-based approach to enable dynamic content updates and web interactivity.
PostgreSQL is a sophisticated, open-source relational database management system renowned for its dependability, robustness, and efficient handling of intricate data tasks.
Django is a Python-based web framework designed for swift development and practical, elegant architecture, suited for creating scalable web applications.
Mongoose is a MongoDB modeling tool that operates in asynchronous environments, offering schema validation and management of relationships for MongoDB databases.
Postman is an API testing tool that assists developers in creating, testing, and modifying APIs, leading to improved software application performance and stability.
JSX is a JavaScript syntax extension used with React, allowing HTML-like structures within JavaScript code for creating dynamic user interfaces.
React Router is a library for React that facilitates dynamic routing, enabling management of navigation and component rendering based on URLs.
Hashing is a method that transforms information into a fixed-length numerical value (hash) for indexing and checking data integrity in computing systems.
Search algorithms are methodical procedures used to locate desired information in vast data collections, employing techniques that consider complexity and various search strategies for efficiency.
Algorithms consist of step-by-step procedures to execute computations, address problems, and optimize tasks and processes, featuring systematic application in diverse fields.
Binary search is a method for locating a specific item in a sorted list, by continually dividing the search range in half and comparing the midpoint with the target value.
Flask is a lightweight Python web framework for creating scalable web applications with ease and elegance, featuring a modular design and minimal setup requirements.
* Front-End Development * Built modern, responsive user interfaces using React, incorporating components, state management (useState), interactive forms, and server communication (useEffect). * Structured and styled web pages with semantic HTML and responsive CSS, ensuring accessibility and SEO optimization. * Created dynamic, interactive experiences using JavaScript and DOM Events, demonstrating mastery of programming fundamentals like data types, variables, functions, control flow, and scope. * Back-End Development * Designed and developed secure server-side applications using Node.js, Express, and Django or Flask, following RESTful principles. * Implemented database operations (CRUD) and relationships using PostgreSQL and MongoDB, including data optimization and Entity Relationship Diagrams (ERDs). * Enabled API communication between applications and integrated token-based authentication for secure interactions. * Development Practices * Use the UNIX command-line and complete tasks like navigating the file system and manipulating files. * Used Git and GitHub for version control, including committing, branching, and collaboration. * Applied MVC architecture, asynchronous programming, the request-response cycle, environment variables, and test-driven development (TDD) for code reliability. * Debugged code, read technical documentation, and solved programmatic challenges. * Additional Skills * Gained foundational Computer Science knowledge in algorithms, data structures, and problem-solving, analyzing efficiency with Big O Notation.
Badge earners who've successfully completed the General Assembly Data Analytics Short Course have learned in-demand skills in popular data tools. They can effectively use Excel, SQL, and Tableau to perform robust analyses, communicate insights, and complete end-to-end data projects. General Assembly Data Analytics Short Course grads are empowered to confidently tackle data analysis as part of their day-to-day work.
Data analysis involves examining, cleaning, transforming, and modeling data to reveal valuable insights, inform decisions, and support conclusions.
Excel Pivot Tables and Charts are dynamic tools for summarizing and visually presenting complex data to reveal patterns, trends, and insights.
Data aggregation entails compiling and refining data from multiple sources into a coherent, structured form, suitable for analysis or reporting.
pgAdmin is an open-source, graphical PostgreSQL database management and administration tool for tasks like querying, design, and performance tuning.
* Data Analytics Workflow & Sourcing Data * Explained the value of data and described how the data analytics workflow is used by analysts. * Identified and evaluated data sources and datasets for relevance to solving business problems. * Formulated specific and testable questions based on given scenarios. * Data Cleaning & Preparation * Applied industry-standard data cleaning techniques in Excel and SQL, including handling missing data, identifying duplicates, and managing outliers. * Used advanced functions such as XLOOKUP and INDEX / MATCH to organize and transform data efficiently. * Selected appropriate data cleaning strategies for diverse scenarios. * Data Analysis Using Excel * Conducted exploratory analyses using common Excel formulas and best practices. * Built relationships between data points with referencing and lookup functions. * Aggregated and summarized data using PivotTables, and resolved common challenges with data structures. * Data Visualization & Communication * Created analytics visuals such as bar charts, pie charts, line graphs, histograms, and scatterplots in Excel. * Designed effective data narratives using Tableau dashboards, applying visual analytics best practices. * Presented findings and challenges through peer-reviewed projects in both Excel and Tableau. * Advanced Querying with SQL * Navigated relational databases and wrote advanced SQL queries using SELECT, WHERE, GROUP BY, and HAVING clauses. * Combined data from multiple sources with JOINs (INNER, LEFT, FULL, OUTER) and UNION operations. * Practiced query optimization, handled NULLs, and implemented subqueries and Common Table Expressions (CTEs). * Applied advanced string, math, and date functions to solve business problems and prepare data for analysis. * Tableau Proficiency * Prepared and connected data to Tableau for visualization. * Built interactive dashboards with parameters, advanced filters, and layout containers. * Created Tableau stories to support data-driven decisions and finalized them.
Badge earners who've successfully completed the General Assembly Product Management Short Course have learned in-demand project management skills to balance business viability, technical feasibility, and customer desire. They can effectively work through product lifecycle navigation, planning and prioritization, metric monitoring, and more. General Assembly Product Management Short Course grads have the confidence and credibility to lead cross-functional initiatives and bring physical and digital products to life.
Product management involves the strategic oversight of a product's development, determining its purpose, timeline, and features to be created by the engineering department.
Product development is the process of creating and introducing new or improved products to the market, encompassing stages such as ideation, design, engineering, testing, and marketing.
Strategic planning entails determining an organization's course by establishing objectives, devising tangible measures, and designating resources for lasting targets.
Customer research entails a methodical examination of customer preferences, behaviors, needs, and motivations to guide product development, marketing strategies, and enhance customer experience and satisfaction.
Market analysis is the process of researching a particular market to identify trends, opportunities, and competitors for informed strategic planning.
Competitive Benchmarking involves comparing a company's offerings and processes against industry leaders to identify best practices, improve performance, and gain a competitive edge.
Risk identification is the methodical recognition of potential threats that may endanger the success of a project or the achievement of organizational objectives.
Minimum Viable Product is a basic version of a product with essential features to test market viability and gather user feedback for future iterations.
Business roadmaps are tactical blueprints outlining a company's objectives, key priorities, and the actions required to attain goals within a defined schedule, directing focus and resource distribution.
Agile frameworks are adaptable methodologies that facilitate teams to respond to changing situations by utilizing incremental, iterative work processes and empirical feedback, promoting flexibility and customer focus in product development.
Feature-driven development is an agile method that centers on delivering client-valued features via iterative increments, emphasizing feature scope, domain modeling, and organized teams.
Prioritization refers to the process of evaluating the significance of tasks and allocating time and resources accordingly, focusing on the most urgent goals first.
Product Life Cycle Management is the oversight of a product's development, from conception to retirement, ensuring efficiency and competitiveness.
Stakeholder engagement is a strategic approach to interacting with, involving, and managing relationships with various stakeholders, promoting mutual understanding and beneficial collaboration.
Stakeholder management entails recognizing, ranking, and efficiently interacting with those who have a stake in a project or organization's results.
Communication is the process of exchanging thoughts, data, and emotions via speech, writing, and non-verbal cues, promoting comprehension and productive cooperation.
Collaboration refers to the process of cooperating with others to accomplish a shared objective, utilizing different viewpoints, abilities, and expertise via open communication and teamwork.
Design validation is the evaluation of a product's design to confirm it adheres to specifications, operates correctly, and successfully achieves its intended purpose.
* Introduction to Product Management * Defined what a product is, the role of a product manager, and the key relationships PMs must manage. * Explored the product development cycle, stakeholder collaboration, and data-driven decision-making. * Strategic Planning & Market Understanding * Connected product planning and prioritization to business needs and Objectives and Key Results (OKRs). * Analyzed competitor behavior, market trends, and customer research to identify opportunities and gaps. * Customer Research & Insights * Conducted and prioritized customer research to understand goals, needs, and pain points. * Summarized research findings to extract actionable insights for the product development process. * Product Development & Validation * Validated assumptions and identified risks using prototypes and Minimum Viable Products (MVPs). * Partnered with UX designers to leverage their deliverables in support of product goals. * Developed a product roadmap, organized epics and user stories, and prioritized features for frequent value delivery. * Agile & Collaboration with Developers * Learned the fundamentals of Agile frameworks, including key workflows and technologies used by development teams. * Collaborated effectively with developers by understanding their processes and how to support their work as a PM. * Continuous Discovery & Product Lifecycle Management * Gathered customer insights through ongoing experiments and interviews to continuously improve products. * Used product health data to evaluate lifecycle stages and guide future product directions. * Effective Communication & Stakeholder Management * Applied best practices for working with stakeholders, including clear and persuasive communication. * Prepared and delivered final presentations using professional presentation techniques.
Badge earners who've successfully completed the General Assembly UX & UI Design Fundamentals in Figma Workshop have learned why Figma is the top tool for UX and UI design in every aspect of the design process and gotten familiar with the basics of Figma’s interface. They can create low-fidelity wireframes, graduate low-fidelity designs to mid-fidelity, and bring mid-fidelity wireframes to life as high-fidelity prototypes for usability testing.
Wireframing refers to the creation of basic, schematic visuals that outline the layout and functionality of a website, application, or product interface.
Prototyping is the creation of early-stage models to test and refine ideas before production.
Visual designs are the art of creating and arranging visual elements to convey messages, express ideas, or enhance visual appeal in different mediums.
Design thinking is an iterative, human-focused approach to problem-solving, encompassing empathy, ideation, prototyping, and testing for innovative solutions.
- Navigate the Figma interface with ease and understand its value as a powerful tool for every step of the design process. - Build low- to mid-fidelity wireframes by utilizing Figma’s basic tools, such as shapes and frames, to create modern interface patterns, and add interactions to produce a clickable wireframe. - Advance design concepts by enhancing your mid-fidelity wireframe with tools like images, color, and typeface to create high-fidelity wireframes, and produce a clickable prototype for design usability testing.
Product Management Fundamentals Workshop
Badge earners who've successfully completed the General Assembly Product Management Fundamentals Workshop have learned the skills to better understand user needs and behaviors to build effective products. They can craft problem statements, execute user interviews, use affinity mapping to structure data, and test and experiment to determine market fit.
Hypothesis testing is a statistical technique for drawing conclusions about a population based on a sample, by forming and testing hypotheses.
Product roadmaps are strategic outlines detailing a product's vision, priorities, and progress throughout its development and market entry stages.
Problem solving refers to the ability to identify, analyze, and creatively resolve issues through systematic and critical thinking, overcoming obstacles to achieve effective solutions.
- Explain the basic role and responsibilities of a product manager, and understand the career possibilities within the field of product management. - Collect and analyze user feedback through techniques such as user interviews and affinity mapping to better understand user needs and behaviors, enabling you to build products that solve their problems. - Create and test hypotheses by applying appropriate testing methods to gather the necessary information to validate or disprove your assumptions.
Programming with Python Fundamentals Workshop
Badge earners who've successfully completed the General Assembly Programming with Python Fundamentals Workshop have learned hands-on fundamental Python programming skills. They understand Python basics, can use conditional statements and data dictionaries, and define and build custom functions.
Data structures represent methods for arranging data, providing efficient means to access, alter, and govern information within algorithms and computing operations.
Programming Concepts encompass essential principles for designing and implementing computer programs, such as algorithms, data structures, syntax, and problem-solving techniques.
Control flow refers to the sequence in which program statements, instructions, or function calls are executed, determining the behavior of conditional branching, looping, and direct jumps within the code.
- Understand Python basics by learning why Python is a popular, user-friendly programming language, navigating a code editor, and understanding different variables and data types. - Use conditional statements and data dictionaries to harness the power of Python, gaining hands-on practice with data structures like lists and dictionaries, and making decisions using conditionals. - Define and build custom functions to organize and simplify code, utilizing loops and conditional logic to generate specific return values.
Badge earners who've successfully completed the General Assembly Excel Fundamentals Workshop have learned how to use Excel to enhance the way they analyze and restructure data. They can effectively navigate Excel features and functions, prepare and analyze data, and build visualizations from their data analysis.
- Understand Excel basics by learning why Excel is the leading data tool, and how to use it to enhance data analysis and restructuring. Practice basic Excel navigation and explore a dataset to determine the right questions to ask for analysis. - Prepare and analyze data in Excel by applying basic data cleaning functions, using references and ranges to manipulate data, connecting data from multiple spreadsheets, and creating PivotTables to quickly summarize your data. - Build visualizations from data analysis by learning when to use different types of visualizations to accurately convey your findings, and practicing with charts and conditional formatting to make your analysis easy to interpret.
Coding Fundamentals: HTML, CSS, & JavaScript Workshop
Badge earners who've successfully completed the General Assembly Coding Fundamentals: HTML, CSS, & JavaScript Workshop have learned how to effectively use HTML, CSS, and JavaScript to build functional, interactive websites. They understand the basics of software engineering, can build simple websites using HTML and CSS, and utilize JavaScript to manipulate the DOM.
Software engineering applies engineering principles to create, test, and maintain software systems in a structured, disciplined, and measurable way.
Document Object Model (DOM) Scripting refers to the technique of utilizing JavaScript to access, modify, and control the content, structure, and appearance of web documents.
Debugging is the methodical process of locating, examining, and fixing issues within software or electronic systems to guarantee their proper and effective functioning.
Version control is a system for managing and tracking changes to files, enabling collaboration, history tracking, and efficient recovery.
- Understand the basics of software engineering by learning the difference between front-end and back-end development, understanding what a server is, and grasping the request-response cycle between servers and clients (computers). - Build a simple website using basic HTML & CSS by applying HTML syntax to create headers, paragraphs, and hyperlinks, then enhancing the design with CSS for color, alignment, and fonts. Practice spotting errors and debugging your code. - Utilize JavaScript to manipulate the DOM by learning fundamental programming concepts like defining variables, applying control flow, and writing functions. Practice creating dynamic webpages by handling user interactions and manipulating the DOM.
Badge earners who've successfully completed the General Assembly Tableau Fundamentals Workshop have learned how to use Tableau to enhance the way they analyze and visualize data to solve problems. They can effectively navigate Tableau features, build dashboards, create reports, and visualize data to tell stories.
Dashboard reporting involves generating visual data summaries that combine essential performance measures, facilitating the comprehension and communication of business insights through an interactive display.
- Build various charts and visuals in Tableau, using techniques to highlight trends and patterns in data, even as a beginner. - Apply advanced Tableau features like table calculations, calculated fields, and parameters to deepen data insights and enhance interactivity in dashboards. - Construct clear and effective Tableau dashboards, incorporating Story Points to tell compelling, data-driven narratives. - Apply a structured, step-by-step approach to data projects, including formulating questions, analyzing data, and presenting insights.
Badge earners who've successfully completed the General Assembly Power BI Fundamentals Workshop have learned how to use Power BI to help transform data into visual insights. They can effectively connect to data from multiple sources, transform and model data, create charts and graphs, and build interactive dashboard and reports.
Power Query is a technology for data connectivity in Excel and Power BI, facilitating users to find, combine, clean, and reshape data prior to analysis.
Data transformations is the process of changing data from one form to another to make it suitable for various purposes, such as analysis or visualization.
ETL is a data integration process involving extracting, transforming, and loading information to transfer data between systems, ensuring its proper formatting and cleanliness for analysis and reporting.
- Understand and navigate Power BI’s interface (Data View, Report View, and Model View). - Clean, transform, and prepare datasets using Power Query Editor. - Build dashboards with foundational and advanced visualizations. - Enhance dashboards with interactivity using slicers, filters, and drill-throughs. - Use DAX to create calculated columns and measures for custom metrics and deeper analysis.
Badge earners who've successfully completed the General Assembly AI for Leaders Workshop understand AI and generative AI use cases and have received hands-on practice with prompt-writing techniques that bolster confidence in making decisions related to AI deployment. They can strategically address where GenAI can serve as a pivotal tool and guide their team's efforts toward the most valuable applications of AI.
- Summarize the AI landscape by exploring its history, current state, and potential future trends, including advancements in Generative AI (GenAI). - Identify and discuss key ethical and legal considerations related to generative AI, ensuring an understanding of the responsibilities and impacts associated with its use. - Demonstrate how to write effective prompts for generative AI models, equipping leaders with the skills to leverage AI technology. - Strategize the deployment of GenAI within teams by aligning AI capabilities with business goals, and planning for integration, scalability, and long-term impact. - Understand practical applications and use cases for GenAI, recognizing opportunities for real-world implementation. Skills Include: Generative AI, AI Governance, AI Strategy, and Prompt Engineering.
Badge earners who've successfully completed the General Assembly Applied AI Engineering Workshop have learned how to use prompt engineering techniques to improve the performance and reliability of AI models. They can work with LangChain, create and work with vector databases, implement question and answer retrieval systems, and build AI agents for various applications.
- Leverage real-time code generation using Github Co-pilot to enhance engineering or data team performance. - Implement prompt engineering techniques to improve team efficiency and problem-solving abilities. - Gain hands-on experience with LangChain and LLM to develop expertise in language model technologies. - Complete a capstone project to demonstrate applied learning and mastery of AI competencies. Skills Include: Generative AI, AI Engineering, Prompt Engineering, Large Language Models, LangChain, and Code Generation.
Badge earners who've successfully completed the General Assembly Modern Engineering Short Course have received hands-on training on crucial modern engineering concepts. They can apply practical modern engineering skills to achieve better product outcomes.
- Understand the principles of twelve-factor design by learning its benefits and how it supports cloud-based infrastructure. - Build and deploy containers using Docker and Kubernetes, learning how they streamline modern development workflows. - Explain the advantages of microservice architecture and apply it to create scalable applications that can handle growth and changes. - Automate testing and deployment processes using Jenkins continuous integration pipelines, ensuring smoother development cycles. - Increase test coverage in your code by implementing unit and integration tests to maintain confidence in software quality. Skills Include: Cloud Infrastructure, Cloud Computing, Docker, Kubernetes, Microservices, Continuous Integration - Continuous Deployment (CI/CD). Jenkins, and Test-Driven Development.
Badge earners who've successfully completed the General Assembly Advanced Analytics Short Course have learned advanced analytics skills to understand, evaluate, and create data—then put their skills to work to drive results. They can bring true data transformation to life through data analysis confidence and competence.
- Wrangle and prepare large datasets from relational databases using SQL, ensuring data is ready for analysis. - Use Tableau to analyze data from multiple sources and create interactive dashboards to present insights. - Build programming skills in Python to apply it as the primary language for data applications. - Enhance analytics workflows by utilizing Python and its libraries to tackle complex problems efficiently. Skills Include: SQL, Data Cleaning, Data Visualization, Python, Pandas, Tableau, Statistical Analysis, and Data Analysis.
Badge earners who've successfully completed the General Assembly Data Science Short Course have the confidence to create robust predictive models with statistics and Python programming and tackle complex machine learning problems on the job. They can synthesize large data sets, tell compelling stories to stakeholders, and achieve new levels of data-driven success.
Statistical analysis is the methodical study of data to uncover patterns and trends using software and mathematical approaches.
Beautiful Soup is a Python library used for extracting data from web pages by parsing HTML and XML files, enabling easier navigation of complex web structures.
- Perform visual and statistical analysis on datasets using Python and relevant Python libraries and frameworks. - Explain the tradeoffs around feature selection, model accuracy, and data quality. - Apply supervised learning techniques, including regression and classification. - Create visualizations and interactive notebooks to present findings and recommendations to technical stakeholders. Skills Include: Data Analysis, Statistical Analysis, Data Visualization, Linear Regression, Data Governance, APIs, Natural Language Processing, Python, Pandas, NumPy, matplotlib, Seaborn, and Beautiful Soup.
Badge earners who've successfully completed the General Assembly Spring Boot Short Course have learned the fundamentals and features of Spring Boot—from auto-configuration and internals, to Spring Initializr and more. They can create their own Spring Boot based projects with confidence.
- Explain why the Spring framework is useful and how it simplifies application development. - Identify the key components of a Spring Boot application and understand their roles in building web applications. - Use Spring Data with Postgres to manage database interactions in a Spring Boot application. - Authenticate and authorize users using Spring Security to ensure application security. - Test Spring Boot applications to ensure functionality and reliability through comprehensive testing. Skills Include: Sprint Boot, Spring Data (JPA), Spring Security, and Spring Framework.
Generative AI Frameworks & Tools Short Course
Badge earners who've successfully completed the General Assembly Generative AI Frameworks Short Course have learned foundational skills to leverage LLMs and GenAI. They can apply and analyze generative models to overcome business challenges and optimize prompt engineering and LLM interactions in real-world scenarios.
AI governance refers to the systematic oversight and regulation of AI systems, aiming to ensure their ethical, transparent, and compliant functioning.
LangChain is a sophisticated language model platform, offering cutting-edge natural language processing and generation features for building AI applications.
Large Language Models are sophisticated AI systems that utilize deep learning and extensive language data to comprehend, create, and translate text similar to human expression in multiple languages.
Deep learning is a branch of machine learning where neural networks with numerous layers identify patterns and classify data from extensive datasets independently.
Machine learning methods are algorithms and statistical models that allow computers to learn and make decisions from data without explicit programming.
Generative Models are computational techniques that utilize neural networks, like GANs or VAEs, to learn patterns from data and generate new, comparable data, such as images or text.
Model accuracy is the extent to which a model's predictions align with real-world results, indicating its predictive capability.
- Describe the fundamentals of Generative AI and distinguish between discriminative and generative models. - Apply transformer models for large dataset processing, sequence prediction, and text categorization tasks. - Implement self-attention mechanisms and multi-head attention in transformers for NLP tasks like sentiment analysis. - Design and fine-tune transformers for real-world text classification and task-specific applications. - Create effective prompts for Generative AI applications and apply advanced prompting techniques. - Build and deploy RAG workflows for Q&A systems and multi-modal data processing. - Manage multi-agent workflows using tools like LangChain, LangFlow, and LangGraph for integrated solutions. - Evaluate and interpret transformer model outputs for use cases like personalized content delivery and model performance optimization. - Identify business applications of Generative AI and leverage large language models (LLMs) to drive business value.
Badge earners who've successfully completed the General Assembly Generative AI Strategist program have explored core concepts and delved into real-world applications through hands-on learning using a variety of GenAI models. They can improve productivity, automate repetitive work, and unlock the value of data through harnessing the power of GenAI.
Generative AI Solutions Architect Short Course
Machine learning models are computational methods that empower computers to autonomously learn and make data-driven predictions or decisions, without specific programming for individual tasks.
AI architecture refers to the structural design of Artificial Intelligence systems, specifying how AI components should be combined for optimal performance, scalability, and dependability.
Machine learning development is the creation, implementation, and improvement of algorithms and models that allow computers to learn and improve from data without explicit programming.
Data pipelines are structured systems created to facilitate the smooth transfer of data between various platforms, guaranteeing dependable data manipulation, storage, and analysis for decision-making purposes.
Apache Airflow is a freely accessible, scalable system that simplifies the creation, scheduling, and tracking of workflows for data engineering processes through a user-friendly interface.
MLOps refers to the practice of automating and streamlining machine learning workflows to enhance collaboration, efficiency, and scalability in production environments.
MLflow is an open-source platform that focuses on managing the machine learning lifecycle, encompassing experimentation, reproducibility, deployment, and a central model registry.
- Explore different types of machine learning models (supervised, unsupervised, and reinforcement learning) and understand their use cases. - Evaluate metrics for supervised machine learning models to measure performance across regression and classification tasks. - Implement end-to-end machine learning workflows by applying best practices such as EDA, feature engineering, and cross-validation. - Build, train, and evaluate machine learning models using appropriate evaluation metrics and techniques. - Design and optimize data pipelines using Apache Airflow. - Manage machine learning workflows with MLOps tools like MLflow for experiment tracking, model versioning, and deployment. - Deploy AI models using hyperscaler vendor offerings, considering cost and performance for scalable solutions. - Build AI agent workflows using Langgraph to create autonomous systems that can perform tasks based on AI-driven logic.
Badge earners who've successfully completed the General Assembly Applied Product Management program have learned modern product management skills to confidently lead teams through the product lifecycle and solve customer problems. They can leverage product management best practices in key areas like research and development, stakeholder management, and more.
- Understand and articulate the roles and responsibilities of a Product Manager, differentiating them from roles like Product Owner and Product Marketer. - Prioritize research and discovery work by identifying key product risks, assumptions, and opportunities through competitive research and market analysis. - Validate hypotheses and product decisions by gathering user feedback via interviews, experiments, and testing. - Leverage AI tools to improve product quality, efficiency, and decision-making while ensuring ethical use of data. - Apply metrics, OKRs, and agile methodologies to measure success, manage the product lifecycle, and maintain continuous product delivery. Skills Include: Product Management, Product Strategy, Agile Product Management, Stakeholder Management, Prioritization, Generative AI, and User Research.
This badge signifies mastery in using AI tools to streamline and enhance creative workflows within Adobe Creative Cloud (including Adobe Firefly, Express, Photoshop, Illustrator, and Premiere Pro.) Badge holders have successfully applied AI for innovative design solutions, branding, and content creation across digital platforms. They have demonstrated the ability to integrate AI into their creative processes to improve efficiency, creativity, and problem-solving, creating high-quality designs that meet client needs and industry standards.
Adobe Creative Cloud consists of a collection of graphic, video, web, and photography applications, along with cloud services, designed for professional content creation and collaboration.
Adobe Illustrator is a vector graphics editing software used for designing illustrations, logos, and intricate elements with accuracy and scalability across diverse media platforms.
Adobe Photoshop is a software program for graphic design, photo editing, and creating complex visual compositions using layered effects, manipulation tools, and color correction capabilities.
Premiere Pro is a professional video editing software developed by Adobe, offering advanced tools for creating high-quality visual content with audio and visual effects, transitions, and color correction capabilities.
- Integrate AI into creative processes to enhance efficiency, speed, and quality of deliverables. - Use Adobe tools and AI-driven features to create professional-level designs, demonstrating advanced design skills. - Apply AI to generate creative design solutions that address client needs and challenges. - Use AI to design and adapt content for various platforms, ensuring it’s optimized for each, including resizing and branding consistency.
This badge recognizes participants who have demonstrated the ability to integrate AI tools into digital marketing strategies using Adobe Creative Cloud. Holders of this badge have shown proficiency in optimizing campaigns, creating personalized content, analyzing data, and maximizing ROI through AI-powered insights. They are skilled in leveraging AI for smarter marketing decisions, content creation, and performance tracking across multiple platforms.
- Use AI tools to enhance marketing campaigns, improve targeting, and demonstrate measurable ROI improvement. - Utilize data analytics to predict customer behavior and trends, applying insights to guide marketing decisions. - Create and automate marketing content (ads, social media posts) using AI, ensuring brand consistency and effective performance. - Leverage AI for personalized customer experiences, optimize social media strategies, and demonstrate improvements in engagement and reach.
Badge earners who've successfully completed the General Assembly Agentic AI Fundamentals Workshop have learned agentic AI foundations and the knowledge and frameworks to turn theory into real-world strategy. They understand how AI agents operate, make decisions, and can be deployed effectively across business units and use cases, and can confidently navigate compliance and security challenges to stay ahead of emerging AI trends shaping the future.
Agentic AI is a subset of artificial intelligence that focuses on creating self-aware systems that can perceive, decide, and act independently to achieve objectives without human involvement.
By the end of this workshop, successful participants will be able to: - Differentiate between traditional Generative AI and Agentic AI concepts. - Understand agent system architecture and how to define effective AI agents. - Build and deploy a functioning multi-agent system. - Test and refine agent behavior and system logic. - Identify high-ROI agentic use cases and strategize for adoption.
Badge earners who've successfully completed the General Assembly AI for Data Analysis Fundamentals Workshop have received hands-on practice to leverage AI to automate their data analysis and build visualizations. They understand generative AI and can use it to effectively analyze data using different prompting techniques and create impactful visualizations—from maps and bar charts, to scatterplots and more.
AI for Workplace Efficiency Fundamentals
Badge earners who've successfully completed the General Assembly AI for Workplace Efficiency Fundamentals Workshop have received hands-on practice to effectively use the power of AI to enhance their professional and personal productivity. They understand generative AI and can use it to summarize large text efficiently and effectively, as well as automate manual and mundane tasks
By the end of this workshop, successful participants will be able to: - Understand Generative AI fundamentals: learn about the impact AI has on various industries, its benefits and limitations, and what kind of tasks it should actually be used for. - Optimize prompts: learn about key factors that influence the performance of large language models, and practice testing different prompt strategies to improve their results. - Summarize content efficiently: learn to summarize large text efficiently and effectively, plus get best practices to ensure your summary is an accurate representation of the larger text. - Use LLMs for automation: Learn when and how to use generative AI to automate or augment mundane tasks, and build an automation plan for your own projects that you can start implementing right away. - Apply Generative AI to real-world situations: Evaluate the different ways that generative AI can be used in real-world business cases, and develop and implement your own simple AI solution to a real-world business problem. "
By the end of this program, successful participants will be able to: - Manage AI as a digital collaborator, advancing prompt engineering skills to choose models and create project contexts. - Engineer prompts and projects in leading LLM interfaces to automate tasks and streamline communications. - Prototype workflows through setting up a multi-step sequence of AI contexts and prompts to solve a sequence of tasks. - Build projects that store custom instructions, data files, and chat history for repeatable analysis. - Develop effective prompts to summarize sentiment, visualize trends, and recommend actions from engagement data. - Mitigate bias, privacy, and governance risks using the CLAP rubric and other ethics frameworks. - Prototype imaginative workflows that blend HR, finance, and operations data to accelerate strategic business outcomes. - Assess privacy, bias, and change-management risks when scaling AI solutions organization-wide. - Select the right model and tool for a specific task. Skills Include: Prompt Engineering, LLM Model Selection & Management, AI Workflow Design & Prototyping, AI-Powered Project Management, Data Analysis & Visualization (AI-assisted), AI Risk Mitigation (Bias, Privacy, Governance), Cross-Functional AI Collaboration
Badge earners who’ve successfully completed the General Assembly AI-Enabled Sales Professional program understand how to use AI to streamline lead qualification, sharpen forecasting accuracy, and personalize outreach at scale. They can strategically enhance their sales process with automation, intelligent insights, and improved decision-making.
By the end of this program, successful participants will be able to: - Developed an effective AI workflow for sales, understanding AI's capabilities and limitations to leverage its outputs for increased speed and depth of work while reducing risk. - Generated tailored, high-converting outreach messages with AI tools like ChatGPT, Claude, and Gemini, creating variations for different channels and optimizing them based on prospect-specific insights. - Applied data-driven analysis with AI tools to identify and prioritize the most qualified leads, moving away from intuition-based decisions to articulate a clear rationale for qualification. - Leveraged various AI solutions in conjunction with standard sales platforms (e.g., CRM systems, email copywriting assistants) to address pain points like generic messaging and time-consuming manual research. Skills Include: Prompt Engineering for Sales, AI Workflow Management, Data-Driven Lead Prioritization, AI Tool Proficiency, Ethical AI Use, Personalized Messaging
By the end of this program, successful participants will be able to: - Understand what AI can and cannot do in HR. - Practice using HR-specific AI platforms (e.g., Workday AI, LinkedIn AI) to identify their strengths and use cases. - Apply ethical principles like CLAP and other guardrails to mitigate bias and ensure privacy. - Evaluate the quality of AI outputs to reduce risk and increase leverage. - Leverage the Levels-of-Prompting framework to craft high-quality prompts. - Practice specific prompt strategies (e.g., Clear & specific, Few-shot, Chain-of-thought) to create HR documents efficiently. - Understand how to leverage AI to draft job descriptions, screening questions, and other HR documents. - Apply hallmarks of effective surveys to leverage AI for creating targeted and engaging surveys. - Practice contextual prompting with uploaded files to generate diverse, bias-checked question banks. - Leverage LLMs to create and update engagement surveys for specific employee segments. Skills Include: AI Foundations Knowledge, HR AI Platform Proficiency, AI Ethics & Compliance, Prompt Engineering, AI-Driven Documentation, AI-Powered Survey Design
Badge earners who've successfully completed the General Assembly AI-Enabled Data Professional program have gained foundational AI knowledge and learned to apply AI tools in their daily workflows, automating tasks and making data-driven decisions. They gained role-specific AI skills and can confidently leverage AI for immediate wins in productivity and operational efficiency.
Badge earners who've successfully completed the General Assembly AI-Augmented Data Professional program have progressed beyond simple AI tasks and can leverage AI to enhance decision-making, streamline operations, and drive strategic outcomes. They received advanced training on scaling AI across departments, ethical decision-making, and strategic implementation—all leading to a deeper understanding of AI’s role in broader business goals.
Badge earners who've successfully completed the General Assembly AI-Superpowered Data Professional program have learned to lead the way in AI-driven innovation. They gained the skills to proactively develop new AI solutions, optimize business processes, and stay ahead of data industry curves through deploying, managing, and scaling advanced AI systems.
Badge earners who've successfully completed the General Assembly AI for Sales Workshop understand how to use AI to streamline lead qualification, sharpen forecasting accuracy, and personalize outreach at scale. They can strategically enhance their sales process with automation, intelligent insights, and improved decision-making.
- TK - TK - TK - TK - TK Skills Include: Generative AI, TK, TK
Badge earners who've successfully completed the General Assembly AI-Superpowered Leader program have learned to lead the way in AI-driven innovation. They gained the skills to proactively develop new AI solutions, optimize business processes, and stay ahead of industry curves through deploying, managing, and scaling advanced AI systems.
By the end of this program, successful participants will be able to: - Demonstrate the ability to apply AI solutions (e.g., intelligent workflows) to address a specific business bottleneck. - Analyze a case study to show how AI improves data-driven decision-making and enhances forecasting or risk assessment. - Propose a new AI-driven product or service that could create a new revenue stream. - Outline a strategy for leveraging AI to personalize customer experiences and drive innovation. - Formulate an AI integration strategy to support a company’s sustainability goals. - Articulate the ethical considerations and governance frameworks needed for responsible AI. - Constructed a strategic plan for an AI-first organization to gain a competitive advantage. - Evaluate the impact of AI on a competitive landscape, identifying potential threats and opportunities. - Justify a “build vs. buy” decision for an AI system based on an analysis of popular platforms. - Describe the key stages of the AI model lifecycle and their importance. - Design an MLOps framework to manage the deployment and maintenance of AI models. - Explain best practices for handling model drift and ensuring the effectiveness of AI systems at scale. - Conceptualize an autonomous AI agent for a business workflow. - Demonstrate an understanding of the risks and governance required for implementing Agentic AI. - Participate in a hands-on exercise to build a basic AI agent. Skills Include: AI-driven data analysis, AI technology fluency, AI ethics and reasoning, AI governance and compliance, AI competitive analysis, AI scenario planning, AI platform evaluation, AI technical communication, AI system design, AI quality assurance, AI data management, AI troubleshooting, AI programming logic, AI systems thinking, AI rapid prototyping, Agentic AI
Badge earners who've successfully completed the General Assembly AI-Enabled Leader program have gained foundational AI knowledge and learned to apply AI tools in their daily workflows, automating tasks and making data-driven decisions. They gained role-specific AI skills and can confidently leverage AI for immediate wins in productivity and operational efficiency.
By the end of this program, successful participants will be able to: - Articulate how foundational AI concepts (ML, Gen AI) and emerging trends (Agentic AI) are reshaping various industries. - Distinguish between incremental and transformational business change driven by AI. - Identify how AI is being leveraged for competitive advantage across different business functions. - Formulate a strategy for scaling AI adoption across an enterprise. - Address organizational change and AI implementation challenges, and develop a roadmap with clear business objectives. - Describe key AI regulations (e.g., GDPR, EU AI Act) and best practices for AI governance and compliance. - Apply prompt engineering techniques to craft effective prompts for decision-making. - Demonstrate how to get AI tools to produce desired outputs that are unique to a leadership role. - Explain how AI models process prompts and generate responses. Skills Include: AI Governance & Compliance, Prompt Engineering, Organization Change Management, Strategic Planning, AI Landscape Knowledge (ML, Gen AI, Agentic AI)
Badge earners who've successfully completed the General Assembly AI-Augmented Leader program have progressed beyond simple AI tasks and can leverage AI to enhance decision-making, streamline operations, and drive strategic outcomes. They received advanced training on scaling AI across departments, ethical decision-making, and strategic implementation—all leading to a deeper understanding of AI’s role in broader business goals.
By the end of this program, successful participants will be able to: - Formulate a framework for evaluating AI investments, including cost structures and ROI metrics. - Identify common AI adoption pitfalls and strategies to avoid them, and justify an AI investment to stakeholders with a clear business case. - Demonstrate the ability to identify a business problem that can be effectively solved by AI. - Outline a plan for an AI-powered workflow or decision-making tool that addresses a specific business need. - Evaluate data considerations such as availability, quality, and ethical use for an AI initiative. - Develop a strategy for reskilling a workforce to adapt to a changing AI-driven environment. - Describe how to foster an AI-resilient culture within an organization and explain the principles of effective human-AI collaboration. - Articulate the impact of emerging AI technologies (e.g., foundation models, AI agents) on an industry. - Propose a strategy for a company to ""future-proof"" its technology roadmap against rapid AI advancements. - Assess how accelerating AI adoption impacts competition. Skills Include: AI Workflow Design, AI Data Strategy & Ethics, AI-driven Financial Analysis, AI Problem Framing
Badge earners who've successfully completed the General Assembly AI for HR Workshop have learned to confidently collaborate with AI for recruiting, policy building, and training content. They can write high-quality job descriptions, generate compelling presentations and reports, implement time-saving techniques, and use AI ethically and responsibly.
Badge earners who've successfully completed the General Assembly User Experience Design Short Course have learned the skills to translate user wants and needs into intuitive digital experiences that power revenue, loyalty, and product success. They can tackle complex UX design challenges with confidence through user research, content strategy, wireframing, and UX tools.
Journey mapping is a technique that illustrates a user's experience with a product or service, identifying areas for improvement and pinpointing potential issues.
Design Systems is the practice of creating and maintaining reusable components, guidelines, and standards to develop consistent user interfaces across digital products.
By the end of the course, successful participants will be able to: - Demonstrate UX design fundamentals: Learn to identify, ideate, and develop design solutions for UX challenges. - Document the UX process: Develop and document personas, journey maps, user flows, and annotated wireframes. - Apply industry-standard tools: Utilize industry-standard tools to propose and refine design decisions. - Create a portfolio project: Execute the full UX design process from start to finish on a real-world problem by designing a new product or redesigning an existing one. The final project culminates in a presentation and a portfolio-ready design. - Understand the professional landscape: Describe how UX designers collaborate with product managers, developers, and visual designers.
Badge earners who've successfully completed the General Assembly Python Programming Short Course have learned the skills to use the world’s fastest-growing programming language with confidence. They can leverage object-oriented programming and Python skills in both data science and web development applications.
Object-oriented programming is a programming approach that structures code into objects, emphasizing modularity, reusability, and abstraction to create efficient software systems.
Web frameworks consist of pre-built libraries that form a basis for creating reliable, scalable, and well-maintained web applications, managing tasks like routing, session control, and security.
API integration is the process of connecting applications through their interfaces, enabling them to exchange data and functionalities to enhance and expand system capabilities.
Server-side development is the process of building and maintaining software applications that operate on servers, managing data, logic, and interactions for web-based services.
By the end of this course, successful participants will be able to: - Implement foundational programming concepts in Python, including the use of basic syntax, data structures (lists, dictionaries, etc.), conditionals, and loops. - Design and write modular, reusable code by creating and using functions, modules, and adhering to Object-Oriented Programming (OOP) principles (classes, objects, and inheritance). - Write robust code by identifying, handling, and debugging common errors using Python's error handling techniques. - Perform core data preprocessing and analysis using the Pandas library, including data cleaning, manipulation, merging, and basic exploratory analysis. - Create informative data visualizations and charts using Python libraries to effectively present data findings. - Interact with external services by making API requests and handling responses using Python. - Develop basic server-side web applications and manage content layout using the Flask framework and its templating engine.
Badge earners who've successfully completed the General Assembly Visual Design Short Course have learned the skills to combine form and function and solve design challenges with confidence. They can create beautiful, responsive designs through visual design fundamentals in typography, color, and layout, along with UX design principles and visual design tools like Sketch and Figma.
Accessibility standards are rules and methods to create inclusive products and services for people with disabilities, promoting equal access and opportunities in both digital and physical settings.
Design Elements And Principles are the foundational aspects and guidelines used to create visually appealing and functional designs, achieved through balance, contrast, emphasis, movement, and unity.
Design research entails conducting systematic studies on users, markets, and trends to guide the creation of innovative design solutions that tackle real-world issues and improve user experience.
Content strategy is the process of creating and delivering content to achieve business objectives and satisfy audience requirements across multiple channels.
High-fidelity prototyping involves crafting realistic, interactive models that closely resemble the final product, enabling design evaluation and user experience testing prior to actual development.
By the end of this course, successful participants will be able to: - Conduct research to inform content strategy and design decisions and develop a persona based on brand and user research. - Create a digital design solution that balances client needs and user goals. - Design digital layouts with industry-standard tools such as Figma and Sketch. - Implement visual design best practices in composition, typography, imagery, and colors. - Ensure readability and establish strong hierarchical systems using typography best practices. - Create color palettes that convey brand themes and aid in guiding user interactions. - Convey emotional meaning and guide content flow through strong compositional layouts that adhere to grid systems. - Implement iterative design throughout the creative process, from researching the company and understanding its problem, to coming up with solutions and final production-ready page design for desktop. - Communicate design decisions to stakeholders and collaborators by presenting the decision-making process of their design work.
Badge earners who've successfully completed the General Assembly Digital Marketing Short Course have learned the skills to drive growth with multichannel campaigns powered by data and customer insight. They can execute successful digital marketing campaigns through thoughtful customer acquisition strategies, agency collaboration, social media and search strategy, and conversion optimization with Google Analytics.
Digital marketing is a strategic approach that utilizes various digital channels, such as SEO, social media, email, and content creation, to promote brands, engage audiences, and foster business growth.
Customer acquisition refers to the process of gaining new clients through marketing, sales, and business development strategies to increase revenue.
Audience segmentation refers to the partitioning of a market into separate segments, each sharing common attributes, requirements, and patterns, for optimized communication approaches.
Social media marketing involves using social platforms to promote products, engage audiences, and increase brand visibility through strategic content sharing.
Search Engine Marketing is the process of boosting website visibility in search results through paid ads and keyword optimization.
Paid search marketing refers to the tactic of displaying ads on search engines to attract relevant users to a website, utilizing a cost-per-click payment structure.
Paid advertising refers to the practice of paying for the placement of promotional content on various media platforms to reach target audiences and enhance brand visibility, engagement, and conversions.
Search Engine Optimization (SEO) is the practice of improving a website's ranking and visibility in organic search results.
Marketing Metrics are quantitative measures that evaluate marketing effectiveness, track performance, analyze consumer behavior, and guide strategic decision-making to optimize ROI and drive business growth.
Marketing Optimization refers to the process of enhancing marketing strategies by analyzing data, with the goal of increasing engagement, conversion rates, and return on investment.
Conversion optimization is the improvement of a website's or digital platform's performance to boost the frequency of users completing targeted actions like purchases or sign-ups.
Content marketing involves purposefully crafting and disseminating valuable, pertinent, and consistent material to appeal to a specific audience, ultimately aiming to stimulate profitable consumer response.
Email marketing is the targeted distribution of promotional or informative content via electronic mail, aiming to nurture leads, foster relationships, and boost engagement and sales.
By the end of this course, successful participants will be able to: - Design and execute comprehensive, multichannel marketing plans through channels such as social media, search, email, and paid advertising. - Apply the Objective-First Marketing Framework to develop a campaign strategy, selecting appropriate channels, tactics, and KPIs. - Execute marketing strategy that's powered by data and customer insight. - Leverage platforms like Google AdWords and Facebook to structure and segment paid advertising campaigns, choosing optimal bidding types and targeting customer groups. - Measure and optimize campaign performance using data from tools like Google Analytics, including calculating key metrics and designing statistically sound A/B tests. - Prepare and deliver a final presentation that effectively communicates the marketing challenge, objective, execution plan, and optimization strategy to stakeholders or employers.
Badge earners who've successfully completed the General Assembly Front-End Web Development Short Course have learned the skills to build interactive, responsive websites with HTML, CSS, JavaScript, and more. They can flex their skills in HTML5, CSS3, JavaScript (ES6), responsive design and APIs, and version control with Git and GitHub.
HTML5 is the latest iteration of the HTML standard, facilitating the development of web content with multimedia, graphics, and enhanced user interactions.
Flexbox is a CSS module used for creating adaptable, responsive layouts without relying on floats or positioning.
CSS Grid is a robust CSS layout system used to create intricate, two-dimensional grids for web design, offering precise control over columns, rows, and webpage areas.
CSS frameworks offer pre-written, reusable code to expedite web design, ensuring consistency and efficiency through grids, pre-set styling, and interactive elements.
Web hosting refers to the service that supplies the required storage, connectivity, and support to make a website accessible online.
By the end of this course, successful participants will be able to: - Create the structure and style of a website using HTML and CSS fundamentals. - Apply interactivity to a site with JavaScript programming fundamentals. - Build a dynamic, responsive website from scratch that is compatible with modern browsers and devices. - Implement responsive design using modern CSS techniques, including Flexbox, CSS Grid, and media queries. - Utilize JavaScript to access the DOM, respond to user actions with event listeners, and define conditional statements, arrays, and loops for logic-driven programs. - Incorporate advanced concepts such as evaluating and making HTTP requests to external APIs for data. - Use GitHub for version control and code collaboration. - Learn the basic technical vocabulary to communicate confidently with front-end web developers and other technical stakeholders.
Badge earners who've successfully completed the General Assembly JavaScript Development Short Course have learned the skills to build rich, interactive websites and applications with JavaScript. They can flex their skills in JavaScript (ES6) fundamentals, object-oriented programming, jQuery, browser and DOM management, and third-party APIs.
Object-oriented modeling is a software design approach that employs objects, classes, inheritance, encapsulation, and polymorphism to model and depict real-world entities, concepts, and their interactions.
Firebase, a Google product, is a versatile platform for creating and managing web and mobile applications, offering services for hosting, databases, analytics, messaging, and crash tracking.
jQuery is a lightweight JavaScript library designed for quick web development, simplifying tasks such as HTML manipulation, event handling, animation, and Ajax interactions.
AJAX is a web development method that enables interactive applications, facilitating the exchange of data between client and server asynchronously without requiring a full page refresh.
Command line refers to a text-based interface where users input commands to execute tasks, handle file systems, and communicate directly with system or software functionalities.
Single-page applications are web apps that load one initial HTML page and subsequently update content dynamically, eliminating the need for full page refreshes during user interaction.
Application Deployment is the procedure of making a software application functional by installing, configuring, and activating it on a server or client platform.
Web development encompasses the creation and upkeep of websites, incorporating web design, programming, database management, and web server configuration to provide engaging online experiences.
By the end of this course, successful participants will be able to: - Gain fluency in JavaScript fundamentals and the essentials of object-oriented programming. - Work with JavaScript, jQuery, the browser, and the Document Object Model (DOM) to build interactive applications. - Consume data from APIs and persist user data using a back-end-as-a-service provider like Firebase. - Build a modern, single-page web application (SPA) utilizing common design patterns and industry best practices. - Utilize version control with Git/GitHub and deploy their single-page application using services like GitHub Pages.
Badge earners who've successfully completed the General Assembly Java Development Short Course have learned the fundamental Java skills to build on the programming experience they already have. They can read and write object-oriented code, debug, and run unit tests to ensure proper behavior in Java applications.
Java is a comprehensive, object-oriented programming language designed for creating diverse applications, spanning from mobile to enterprise solutions, distinguished by its platform-independent nature.
Test-Driven Development is a methodology where tests are created prior to writing the actual code, guaranteeing that the code adheres to specified criteria and promoting more efficient, error-free results.
Unit testing is a method of validating individual units of software, by checking their functionality independently from other components.
Memory management is the process of controlling and coordinating computer memory, allocating space, and optimizing system efficiency by managing program memory needs and data storage.
By the end of this course, successful participants will be able to: - Read and write object-oriented code by grasping the principles and roles of classes and objects. - Understand and apply the fundamentals of Java programming, including data types, variables, control flow, loops, and methods. - Instantiate objects, build constructors, manage fields and methods, and use subclasses to enhance object functionality. - Debug Java applications by setting breakpoints, examining application states, and interpreting stack traces and handling exceptions using try-catch blocks. - Apply Test-Driven Development (TDD) principles, including gaining hands-on experience by creating a calculator guided by specific unit tests. - Explore and apply key design patterns, including Singleton, Builder, Factory, and Abstract Factory, focusing on their benefits and limitations. - Demonstrate the foundational Java skills necessary to prepare for the Oracle 811 exam.
Badge earners who've successfully completed the General Assembly AI-Enabled Finance Professional program have gained foundational AI knowledge and learned to apply AI tools in their daily workflows, automating tasks and making data-driven decisions. They gained role-specific AI skills and can confidently leverage AI for immediate wins in productivity and operational efficiency.
By the end of this course, successful participants will be able to: - Identify AI applications that enhance market research, data analysis, and reporting efficiency. - Master prompt engineering techniques for financial data processing and insight generation. - Apply AI solutions to daily tasks like market tracking, data visualization, report generation, and investment analysis. - Validate and refine AI-generated financial analysis and recommendations. - Establish systematic approaches to AI collaboration, prompt engineering fundamentals, and quality evaluation frameworks. - Apply task decomposition and advanced prompting for ongoing daily market monitoring and report updates. - Use prompting strategies and scenario-based simulation methods to build custom GPTs for company analysis and stock evaluation. Skills Include: Generative AI, AI Governance, Prompt Engineering, Financial Data, Scenario Modeling, Market Research, Financial Analysis, and Risk Management.
Badge earners who've successfully completed the General Assembly AI-Augmented Finance Professional program have progressed beyond simple AI tasks and can leverage AI to enhance decision-making, streamline operations, and drive strategic outcomes. They received advanced training on scaling AI across departments, ethical decision-making, and strategic implementation—all leading to a deeper understanding of AI’s role in broader business goals.
By the end of this course, successful participants will be able to: - Design efficient AI workflows that process data from multiple sources and create automated reports. - Build AI pipelines for daily data updates, analysis, and insight generation. - Use AI to prioritize research tasks, identify risks and opportunities, and streamline due diligence processes. - Create AI-assisted systems for ongoing market monitoring and report generation. - Learn to set up multi-step AI contexts and prompts to solve sequential financial tasks. - Build pipelines that generate reports with updates from new data inputs using specialized AI models for different tasks with error checking methods. - Use AI to prioritize reports, identify areas needing attention for risk or opportunity investigation, ensuring analysts focus on the most important areas first. Skills Include: Generative AI, AI Sequencing, Workflow Automation, Data Pipeline Development, Advanced Prompt Engineering, Financial Data Processing, Risk Assessment, Market Monitoring, Investment Research
Badge earners who've successfully completed the General Assembly AI-Superpowered Finance Professional program have learned to lead the way in AI-driven innovation. They gained the skills to proactively develop new AI solutions, optimize business processes, and stay ahead of finance industry curves through deploying, managing, and scaling advanced AI systems.
By the end of this course, successful participants will be able to: - Build custom financial AI agents for market forecasting, company analysis, and investment research. - Develop AI-powered question-answer systems for processing vast amounts of company and market data. - Design and Implement AI systems for governance, compliance monitoring, and regulatory reporting. - Create and Deploy comprehensive AI solutions for investment evaluation and portfolio management. - Apply agent architecture and autonomous system design principles to overcome treating agents like simple tools. - Demonstrate knowledge provisioning techniques and instruction design patterns for agent clarity and consistency. - Construct question-answer systems for internal and publicly available documents using trusted, testable architectures with custom data integration. - Build compliance monitoring systems that identify risks, suggest solutions, and track regulatory updates for responsive governance. - Develop task maps, break into user stories, leverage AI to iterate on designs, and draft roadmaps with technical dependencies for strategic financial AI solution development. - Execute a comprehensive public company evaluation using all learned AI techniques, demonstrating mastery of AI-enhanced investment analysis and decision-making processes . Skills Include: Generative AI, Agentic AI, AI Sequencing, Workflow Automation, Advanced Prompt Engineering, Financial Data Processing, Investment Research, Risk Assessment, Marketing Monitoring, Agent Architecture, Knowledge Provisioning, Technical Roadmap Drafting, Task Decomposition.
Badge earners who've successfully completed the General Assembly AI-Enabled Legal Professional program have gained foundational AI knowledge and learned to apply AI tools in their daily workflows, automating tasks and making data-driven decisions. They gained role-specific AI skills and can confidently leverage AI for immediate wins in productivity and operational efficiency.
Badge earners who've successfully completed the General Assembly AI Foundations workshop have gained foundational AI knowledge and learned to apply AI tools in their daily workflows, automating tasks and making data-driven decisions. They gained role-specific AI skills and can confidently leverage AI for immediate wins in productivity and operational efficiency.
Badge earners who've successfully completed the General Assembly AI Workplace Fundamentals Course have gained foundational AI knowledge and learned to apply AI tools in their daily workflows, automating tasks and making data-driven decisions. They gained fundamental AI skills and can confidently navigate advanced prompt engineering, automated content and data analysis, and building custom AI workflows.
- Construct advanced prompts to generate accurate, high-quality, and relevant outputs. - Automate and enhance the creation of written and visual content for business communications. - Analyze data from spreadsheets and documents using plain-language queries to extract key insights. - Design and implement a custom, multi-step AI workflow to solve a specific business problem. - Apply a framework for the responsible, secure, and ethical use of generative AI in a professional context. - Streamline the creation of essential business documentation like project plans and SOPs. - Use AI as a strategic partner for brainstorming, competitive analysis, and risk mitigation.
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Badge earners who've successfully completed the General Assembly Project Management Skills with AI Course have gained skills in AI-enhanced project management frameworks, risk mitigation, and team leadership. They can confidently integrate AI into project frameworks, predict and mitigate risks with AI, and lead distributed teams with AI collaboration.
- Explore how AI is transforming project management roles, skills, and responsibilities. - Select and adapt project management frameworks using AI-driven insights. - Develop project plans and workflows that integrate AI tools with stakeholder needs. - Differentiate between human oversight and AI automation to optimize collaboration. - Evaluate project performance by connecting metrics, outcomes, and organizational goals. - Apply predictive AI tools to strengthen risk management and mitigation strategies. - Design adaptive project approaches that respond to changing contexts with AI support. - Communicate effectively with stakeholders using AI-enabled reporting and facilitation tools. - Lead distributed and hybrid teams by integrating AI collaboration tools into coordination and accountability practices. - Formulate responsible and ethical strategies for AI adoption in project workflows.
Badge earners who've successfully completed the General Assembly AI-First Product Management Course have learned to integrate AI throughout the product lifecycle—from customer discovery to delivery—so they can build better products faster and lead with confidence in an AI-powered world. They gained skills in conducting AI-enhanced customer research, building data-driven product strategies, and delivering compelling product pitches.
- Define the role, responsibilities, and career pathways of a product manager in the AI era. - Apply a product management mindset grounded in empathy, teamwork, and ethical decision-making. - Conduct customer research using interviews, data synthesis, and AI tools to uncover meaningful insights. - Develop personas, empathy maps, and customer journeys that illustrate customer needs and experiences. - Translate customer insights into product stories, goals, and measurable success metrics. - Design product visions and roadmaps that align with business strategy, supported by AI-driven analysis. - Plan and execute experiments to validate assumptions, using AI to prototype, analyze, and refine solutions. - Manage product development cycles with Agile frameworks, integrating AI into backlog, sprint, and QA processes. - Address risks, constraints, and ethical challenges in product decisions, ensuring responsible use of AI. - Deliver compelling product pitches and presentations that showcase customer insights, data, and AI integration.
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Badge earners who've successfully completed the General Assembly Applied AI and Deep Learning in Action Course have gained the practical skills and confidence to bring applied AI into their role and career path. They mastered PyTorch, transformers, and deployment practices while building labs and a capstone project that demonstrates real AI engineering capability.
- Review the foundations by using Git/GitHub, Python, and core machine learning methods like logistic regression and gradient descent. - Explain neural networks conceptually and trace their evolution from simple models to state-of-the-art deep learning systems. - Apply deep learning toolkits like PyTorch, Hugging Face, and torchvision to implement and optimize models. - Experiment with advanced architectures, including CNNs, RNNs, transfer learning, and regularization strategies to improve model performance. - Analyze text data using NLP methods, embeddings, pipelines, and transformers for real-world tasks such as classification, summarization, and translation. - Evaluate models critically by benchmarking performance, costs, risks, and bias while selecting appropriate solutions for business contexts. - Engineer AI systems responsibly by considering fairness, transparency, lightweight deployment options, and security threats such as adversarial inputs. - Deliver an end-to-end AI solution by planning, developing, evaluating, and presenting a complete project that demonstrates both technical skill and responsible AI practices.
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Badge earners who've successfully completed the General Assembly AI Software Engineering Pathway have gained practical skills to build the applications used by people every day. They can confidently create interactive, responsive websites that work across all devices, build server-side applications using Node.js and Express, integrate AI services into web applications using Next.js and cloud-based APIs, and deploy, monitor, and maintain AI systems in production.
In order to qualify for a pathway badge, the learner must successfully complete all courses in the pathway, confirming they gained comprehensive, role-specific AI skills and learned to apply AI tools in their daily workflows for immediate improvements in productivity and operational efficiency.
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Badge earners who've successfully completed the General Assembly AI & Machine Learning Pathway have gained practical skills to design, deploy, and maintain the AI systems that drive modern business transformation. They can confidently build real-world AI solutions from concept to deployment, including scalable, enterprise-grade data infrastructure that powers AI applications.
Badge earners who've successfully completed the General Assembly AI Fundamentals Pathway have gained foundational AI knowledge and learned to apply AI tools in their daily workflows, automating tasks and making data-driven decisions. They gained role-specific AI skills and can confidently leverage AI for immediate wins in productivity and operational efficiency.
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Badge earners who've successfully completed the General Assembly AI Experience & Design Pathway have gained practical skills to design the AI-powered experiences you see every day. They can confidently create effective, accessible interfaces that solve real user problems, build adaptive interfaces using human-centered design principles with AI-driven systems thinking, master scalable design systems, responsive layouts, and data visualization for AI products, and craft professional portfolios that showcase their AI design expertise.
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Badge earners who've successfully completed the General Assembly AI Product Management Pathway have gained practical skills to lead product strategy, manage cross-functional teams, and drive business outcomes in the AI era. They can confidently lead successful products, coordinate complex projects and distributed teams using AI-powered tools, design and execute product strategies that leverage AI capabilities responsibly, and transform raw data into actionable product insights using AI and analytics tools.
Badge earners who've successfully completed the General Assembly AI Data Analytics Pathway have gained practical skills to analyze data and build the AI-powered insights that drive modern business decisions. They can confidently transform raw data into business intelligence, build powerful analytical workflows using Python's most essential libraries, design and manage modern cloud-based database systems that support AI and machine learning workloads, and step into advanced AI by building neural networks, working with transformers, and deploying real-world AI solutions.
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Badge earners who've successfully completed the General Assembly Data Analytics and Visualization Course have gained skills to extract meaningful insights from complex datasets. They have learned essential data analysis and visualization skills and can confidently query databases, create compelling dashboards, and communicate findings that influence strategic outcomes.
- Apply core principles of data analysis and visual communication to interpret and present insights. - Use SQL to extract, filter, and combine data from relational databases. - Clean, transform, and validate datasets to ensure analytical accuracy and consistency. - Connect SQL databases to Tableau and prepare data for visualization. - Design clear, accessible, and well-structured dashboards in Tableau. - Add interactivity and calculated fields to enhance analytical exploration and user experience. - Apply storytelling techniques to communicate findings effectively to diverse audiences. - Integrate SQL and Tableau skills to deliver a complete, insight-driven analytical project.
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Badge earners who've successfully completed the General Assembly UX Research and Strategy with AI Course have gained skills in cross-functional collaboration and ethical AI research leadership. They can confidently execute AI-enhanced approaches to qualitative analysis, quantitative synthesis, and strategic planning that strengthen rather than compromise research quality and user advocacy.
- Integrate AI tools into UX research and strategy to enhance how you gather, interpret, and apply user insights across the product lifecycle. - Apply design thinking and agile methods with AI-driven workflows to speed iteration and strengthen collaboration across product teams. - Develop and execute AI-informed research plans that align user needs, business goals, and technical opportunities. - Use AI to analyze complex data and turn research findings into clear, actionable design recommendations. - Communicate insights effectively through AI-assisted visualizations, storytelling, and tailored stakeholder presentations. - Uphold research quality, inclusivity, and ethics by mitigating bias and ensuring accessible, responsible AI use. - Collaborate effectively in interdisciplinary AI product teams, balancing user advocacy with technical and strategic priorities. - Demonstrate leadership in ethical, data-driven UX research through a capstone project showcasing responsible experience design with AI.
Badge earners who've successfully completed the General Assembly Python for AI and Data Course have gained skills to automate data tasks, build machine learning models, and deliver end-to-end analytical solutions that drive strategic decision making. They can confidently bridge the gap between business acumen and technical capability to solve real-world data and AI challenges using industry-standard libraries including Pandas, NumPy, SciPy, and scikit-learn.
- Configure and manage a Python environment using Jupyter for analytical work. - Write and debug clean, structured code to automate data tasks. - Ingest, clean, and transform datasets using Pandas and NumPy. - Apply statistical and computational techniques to identify and interpret patterns. - Create visualizations using Matplotlib and Seaborn to communicate insights clearly. - Train and evaluate basic machine learning models using scikit-learn and deploy them via cloud AI tools. - Implement debugging, refactoring, and version-control practices using Git and GitHub. - Design and deliver a complete, reproducible data analysis project demonstrating professional analytical standards.
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Badge earners who've successfully completed the General Assembly UX Design for AI Experiences Course have gained the skills to lead next-generation experience design in the AI era. They can confidently develop journey mapping methodologies that capture how AI transforms user flows, decision points, and interaction patterns across the entire experience lifecycle
- Analyze how emerging AI capabilities transform the principles, methods, and impact of modern experience design. - Evaluate opportunities for meaningful human–AI collaboration that uphold ethical, transparent, and user-centered design values. - Design adaptive and conversational interfaces that dynamically respond to user behavior, intent, and context across platforms and modalities. - Integrate AI-driven insights into the development of information architectures and user journeys to optimize personalization and interpretability. - Construct intelligent content strategies that adapt tone, message, and style to diverse audiences and contexts while maintaining brand coherence. - Synthesize multi-device and cross-platform design frameworks to deliver consistent, accessible, and contextually aware experiences. - Formulate innovative solutions to complex, data-informed design challenges using systems thinking and iterative experimentation. - Produce and defend an end-to-end AI-enabled experience ecosystem that demonstrates ethical reasoning, adaptability, and strategic design leadership.
Badge earners who've successfully completed the General Assembly AI Product Strategy Course have gained skills to drive AI innovation at scale while creating AI product visions that align with business objectives. They can confidently bridge the gap between AI possibilities and business outcomes, identify high-impact opportunities, design data-driven roadmaps, and lead organizational AI transformation.
- Explain how AI is transforming product management roles, decision-making, and innovation strategies. - Select and adapt strategic frameworks that integrate AI insights to drive product development. - Develop adaptive product roadmaps and prioritization models using AI-enhanced tools. - Differentiate between human judgement and AI automation to optimize product outcomes. - Evaluate product performance by linking data-driven metrics, market outcomes, and organizational goals. - Apply predictive analytics and AI insights to identify new opportunities for product innovation. - Design responsible and ethical governance models that balance ROI, transparency, and accountability. - Communicate and present AI strategy effectively to stakeholders through data-driven storytelling and visualization.
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Badge earners who've successfully completed the General Assembly Database Management with AI Integration Course have gained skills to design, manage, and automate data systems in today's cloud-based and AI-driven environments. They can confidently bridge the gap between traditional database management and modern AI-powered data infrastructure while optimizing data systems for scalability, performance, and intelligent automation.
- Explain the principles and architecture of modern cloud-based relational and non-relational databases. - Design and manage efficient cloud database solutions that address scalability, performance, and reliability requirements. - Develop and automate data pipelines and workflows using orchestration and ETL/ELT tools. - Construct and optimize scalable data warehouses that support advanced analytics and machine learning workloads. - Integrate and evaluate AI and automation tools within data processing and database systems to improve efficiency. - Implement and refine DevOps and CI/CD practices for continuous deployment and management of data infrastructures. - Monitor, assess, and optimize data systems using logging, performance analysis, and observability strategies. - Design and justify secure, compliant, and resilient distributed data environments using modern platforms and tools such as Snowflake, Airflow, Docker, and MongoDB Atlas.
Badge earners who've successfully completed the General Assembly UI Design for AI Products Course have gained skills to craft scalable, accessible, and intelligent interface systems. They can confidently navigate the intersection of visual design craft and AI-driven product requirements—from adaptive typography and data visualization through component libraries and design-to-development collaboration.
- Analyze how modular design systems and adaptive components support scalable, data-informed AI product ecosystems. - Design responsive and accessible interfaces that translate complex AI logic into clear, human-centered visual experiences. - Integrate motion, feedback, and data visualization to communicate AI system behavior, transparency, and confidence. - Apply technical literacy in HTML, CSS, and documentation practices to strengthen collaboration between design and development teams. - Evaluate inclusivity, accessibility, and governance strategies to ensure ethical, sustainable evolution of AI interface systems. - Synthesize advanced visual design, responsive layout, and system documentation principles into cohesive multi-platform design frameworks. - Formulate innovative interface solutions that adapt intelligently across devices, modalities, and user contexts. - Produce and defend a unified AI interface system that demonstrates scalability, ethical reasoning, and human–AI collaboration.
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Badge earners who've successfully completed the General Assembly Business Intelligence with AI Course have gained skills to turn raw data into meaningful stories and actionable recommendations using AI tools and methodologies. They can confidently transform raw data into strategic insights, build compelling analytical narratives, and integrate AI tools into business workflows that guide product strategy, performance measurement, and customer understanding.
- Explain how AI and data analytics support product strategy and business decision-making. - Apply ethical and responsible AI principles when interpreting and sharing product data. - Collect and prepare data to identify trends, patterns, and user insights that inform product decisions. - Use and evaluate simple AI and machine learning tools to generate business insights. - Analyze product and customer data to identify performance gaps, opportunities, and risks. - Create and interpret dashboards and visual reports that communicate key metrics and insights. - Develop and present compelling data stories that support stakeholder alignment and product strategy. - Integrate AI-driven insights into product management workflows to enhance decision-making and innovation.
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Badge earners who've successfully completed the General Assembly UX Portfolio Storytelling with AI Course have gained the skills to craft professional portfolios that showcase both creative identity and AI-era design fluency. They can confidently curate, structure, and present compelling portfolio narratives with AI collaboration.
- Analyze how storytelling and curation shape professional identity and credibility in AI-era design practice. - Design cohesive, visually structured portfolios that communicate design intent, iteration, and AI collaboration with clarity and integrity. - Integrate written reflection, narrative framing, and case study composition to express process depth and design rationale. - Construct adaptive wireframes and interaction flows that visualize user journeys, logic, and decision-making within AI-driven projects. - Apply branding and visual hierarchy principles to develop a consistent, authentic personal design identity across platforms. - Evaluate communication, presentation, and networking strategies to strengthen professional presence and industry engagement. - Synthesize ethical reasoning and transparency practices to represent AI contributions responsibly within design work. - Produce and defend a comprehensive AI-powered design portfolio that demonstrates creative growth, professional readiness, and reflective storytelling.
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Badge earners who've successfully completed the General Assembly Front-End Web Development with HTML & CSS Course have gained the foundational skills to design, build, and optimize modern websites. They can confidently bring visual concepts to life while leveraging AI tools responsibly to debug code, enhance accessibility, and accelerate the development process—without replacing the core problem-solving skills that define a front-end developer.
○ Design and structure accessible, standards-based web pages using semantic HTML to ensure clarity, inclusivity, and compliance with modern web best practices. ○ Apply CSS styling and responsive design techniques to create visually consistent, mobile-friendly layouts that adapt seamlessly across devices and screen sizes. ○ Leverage AI tools to support debugging, code validation, and accessibility reviews while maintaining full authorship and understanding of your HTML and CSS. ○ Develop and implement cohesive design systems that apply typography, color, and layout principles to achieve visual harmony and scalability in front-end projects. ○ Evaluate website performance and accessibility through testing, optimization, and iterative refinement using both manual and AI-assisted methods. ○ Communicate design decisions and technical implementations effectively through documentation, annotated code, and visual storytelling. ○ Collaborate professionally within a front-end workflow, applying ethical and responsible use of AI to enhance problem-solving, creativity, and learning efficiency. ○ Demonstrate mastery of front-end development by designing, building, and optimizing a responsive, accessible portfolio website that reflects professional standards and AI-informed craftsmanship.
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Badge earners who've successfully completed the General Assembly Back-End Development with JavaScript Course have gained skills to design, build, and deploy robust server-side applications using one of the world's most in-demand programming languages. They can confidently bridge the gap between front-end interfaces and the data-driven infrastructure that powers modern web applications.
- Understand how server-side JavaScript operates, including runtime environments and module systems. - Build and configure back-end servers using modern JavaScript environments. - Create and document RESTful APIs for structured communication between front-end and back-end applications. - Implement database models, queries, and persistence layers to manage and retrieve application data. - Integrate authentication and authorization to protect data and maintain secure user sessions. - Deploy and monitor server applications to cloud or platform-as-a-service environments. - Debug, test, and refactor code using industry-standard workflows and version control systems. - Demonstrate mastery by building and deploying a full-stack capstone project with a functional back-end API and database integration.
Badge earners who've successfully completed the General Assembly Build AI Web Applications Course have gained skills to design, develop, and deploy intelligent, data-driven web applications that leverage modern AI technologies. They can confidently bridge web development expertise with AI implementation to build responsive interfaces that connect to powerful AI APIs, run machine learning models in the browser, and deploy production-ready intelligent applications.
- Explain how AI technologies enhance modern web applications. - Configure a local and cloud-based AI development environment. - Develop front-end components using frameworks such as Next.js. - Integrate external AI APIs for text, image, or data generation. - Apply fundamental machine-learning concepts for web development. - Implement pre-trained models and SDKs to add intelligent behaviors. - Design and maintain data pipelines and storage systems for AI applications. - Deploy and present a fully functional AI web application as a capstone project.
Data Engineering & Automation with AI badge image
Badge earners who've successfully completed the General Assembly Data Engineering and Automation with AI Course have gained skills to build, automate, and optimize data systems that drive AI and machine learning applications. They can confidently bridge traditional data engineering with AI-powered automation to create production-ready AI data infrastructure and intelligent automation systems.
- Understand the role of data engineering in the AI lifecycle. - Design and deploy cloud-based data infrastructures. - Ingest, transform, and model data using scalable pipelines. - Orchestrate workflows and manage features for machine learning. - Connect enterprise data to Generative AI systems using vector databases and retrieval-augmented generation (RAG). - Automate infrastructure, deployment, and monitoring. - Ensure data governance, security, and cost efficiency. - Complete and present a fully automated, production-ready data pipeline as a capstone project.
AI Systems Engineering & Reliability badge image
Badge earners who've successfully completed the General Assembly AI Systems Engineering and Reliability Course have gained skills to deploy, monitor, scale, and maintain production-grade AI systems in real-world cloud environments. They can confidently bridge the gap between building AI models and keeping them running reliably while maintaining performance under pressure, implementing proactive monitoring, and responding to incidents with precision and data-informed decision-making.
- Deploy and manage AI-enabled systems in distributed, cloud-based environments (AWS, GCP, Azure). - Implement observability, monitoring, and alerting systems to track performance, drift, and uptime. - Apply reliability engineering principles, including redundancy, failover, and automated recovery. - Build and maintain CI/CD pipelines that support continuous integration and model updates. - Integrate testing and validation workflows for production-ready AI systems. - Diagnose and resolve incidents using root cause analysis and automated remediation tools. - Optimise infrastructure performance, scalability, and cost-efficiency across compute and storage resources. - Collaborate across engineering, data, and AI teams to maintain resilient, compliant, and secure operations.
Badge earners who've successfully completed the General Assembly MLOps and AI Infrastructure Course have gained skills to design, deploy, and govern enterprise-grade AI systems at scale. They can confidently move beyond model building and focus on the complete AI lifecycle—from pipeline architecture and data infrastructure management to governance, security, and strategic AI operation leadership.
- Design and implement scalable AI pipelines that connect data ingestion, model training, deployment, and monitoring in a production environment. - Manage data infrastructure across cloud platforms, applying principles of storage design, lineage tracking, and compliance to ensure reliability and transparency. - Apply containerization and orchestration tools such as Docker and GCP Cloud Composer to automate workflows and support reproducible machine learning operations. - Deploy and monitor AI models using CI/CD pipelines and cloud services, establishing systems for drift detection, retraining, and performance optimization. - Evaluate and mitigate risks through governance frameworks that address security, privacy, and ethical considerations in AI operations. - Optimize compute and resource utilization by analyzing performance metrics and balancing scalability, cost, and environmental impact. - Lead cross-functional collaboration between data scientists, IT teams, and business stakeholders to align AI infrastructure with organizational strategy. - Produce and defend a comprehensive AI Infrastructure Roadmap that demonstrates technical fluency, governance awareness, and strategic leadership in managing enterprise-scale AI systems.
Badge earners who've successfully completed the General Assembly AI for Marketers Workshop understand how to leverage AI for data-driven decision-making and process optimization, ensuring their daily workflows are built to enable AI-enhanced market research, content creation, automation, and analytics. They can streamline workflows, personalize marketing, and drive data-informed, high-impact strategies in a competitive landscape.
Attend and complete the AI for Marketers Workshop.
AI for Executive Coaches Workshop badge image
Badge earners who've successfully completed the General Assembly & EZRA AI for Executive Coaches Workshop have learned the skills to understand, evaluate, and implement AI solutions. They can navigate the AI ecosystem, assess generative AI use cases, and confidently identify viable AI opportunities through structured problem framing and solution design.
Attend and complete the 3-hour AI for Executive Coaches Workshop.
Attend and complete the AI for Sales Workshop
AI for Product Managers Workshop badge image
Badge earners who've successfully completed the General Assembly AI for Product Managers Workshop understand how to leverage AI to enhance productivity, accelerate product delivery, and improve the quality of product outcomes. They can differentiate clearly between software development and data science driven product processes, work effectively with data scientists, and strategically integrate AI into their product workflows.
Attend and complete the AI for Product Managers Workshop
Leading In An AI World Workshop badge image
Badge earners who've successfully completed the General Assembly Leading In An AI World Workshop understand AI and generative AI use cases and have received hands-on practice with prompt-writing techniques that bolster confidence in making decisions related to AI deployment. They can strategically address where GenAI can serve as a pivotal tool and guide their team's efforts toward the most valuable applications of AI.
Summarize the AI landscape by exploring its history, current state, and potential future trends, including advancements in Generative AI (GenAI). Identify and discuss key ethical and legal considerations related to generative AI, ensuring an understanding of the responsibilities and impacts associated with its use. Demonstrate how to write effective prompts for generative AI models, equipping leaders with the skills to leverage AI technology. Strategize the deployment of GenAI within teams by aligning AI capabilities with business goals, and planning for integration, scalability, and long-term impact. Understand practical applications and use cases for GenAI, recognizing opportunities for real-world implementation.
The recipient of this badge has successfully completed the 200-hour Digital Marketing Bootcamp. This intensive, project-based program equips professionals with foundational knowledge in digital marketing principles and data-driven strategy. Graduates have demonstrated proficiency in the responsible use of AI tools within Adobe’s creative and analytical ecosystem and have built a professional portfolio showcasing job-ready skills for entry-level marketing roles.
- Digital Marketing Foundations & Strategy: Applying core digital marketing principles, strategic planning, and data interpretation to real-world scenarios. - Web Presence & SEO: Implementing website fundamentals, UX principles, and comprehensive search engine optimization (on-page, technical, off-page, and local SEO). - Content & Email Marketing: Developing content marketing strategies and executing personalized email campaigns enhanced by AI tools. - Paid Advertising: Managing targeted campaigns across PPC (Google Ads), display, video, and paid social media platforms. - Adobe Creative Tools: Utilizing Adobe Express and core Adobe Creative Cloud tools for content creation and campaign management. - Optimization & Automation: Applying conversion rate optimization (CRO), marketing automation, and CRM principles to maximize performance. - Professional Portfolio: Assembling and presenting a project-based portfolio that demonstrates practical marketing expertise.
The recipient of this badge has successfully completed the 250-hour Business Development & Sales Bootcamp. This intensive, expert-led program prepares graduates for high-performing sales roles in the tech industry by building deep business understanding, technical fluency, and mastery of modern sales tools. Graduates have demonstrated the ability to manage complex B2B sales cycles—from prospecting and lead generation to negotiating and closing—while leveraging AI and automation to sell smarter.
- Sales Workflow & Execution: Mastering the end-to-end sales process, including prospecting, discovery, and qualification using frameworks like MEDDIC. - Tech Fluency for Sales: Navigating technical conversations with engineering, product, and IT stakeholders regarding SaaS architecture, APIs, cloud computing, and security. - Modern Sales Stack: Utilizing industry-standard tools including Salesforce, HubSpot, and Gong to manage pipeline and customer relationships. - Strategic Communication: Delivering compelling, customer-focused presentations, building ROI-focused proposals, and managing complex stakeholder objections. - AI & Automation: Applying prompt engineering and AI-powered tools to enhance sales workflows and build personalized outbound campaigns. - Business Fundamentals: Understanding how businesses function, identifying buyer personas, and mapping the buying process. - Professional Branding: Developing a professional digital presence and establishing thought leadership through personal branding and networking.
Performance Marketing with AI badge image
Badge earners who've successfully completed the General Assembly Performance Marketing with AI Course have gained skills to execute, optimize, and communicate AI-powered performance strategies with clarity and confidence. They can design AI-informed campaign architectures, interpret modeled conversion data, build automated reporting systems, and apply responsible AI practices.
• Explain how AI reshapes performance marketing channels, workflows, and optimization models across search, social, and commerce ecosystems. • Evaluate automation and predictive-signal opportunities to improve audience targeting, bidding, creative, and campaign structure decisions. • Design AI-informed campaign architectures—including asset groups, audience strategies, and signal-sharing frameworks—that enable scalable automated performance. • Apply generative AI and prompt engineering techniques to develop, test, and assess performance-ready creative variations. • Interpret AI-driven analytics, modeled conversions, attribution methods, and experiment results to determine reporting accuracy and optimization impact. • Construct automated dashboards and insight workflows that translate AI generated outputs into clear, actionable performance recommendations. • Assess ethical, legal, and governance considerations—including data use, algorithmic bias, and automation risk—to ensure responsible AI deployment in paid media. • Develop and present an integrated, AI-powered performance marketing strategy that demonstrates predictive learning, measurement rigor, and stakeholder-ready storytelling.
Content Marketing Strategy with AI badge image
Badge earners who've successfully completed the General Assembly Content Marketing Strategy with AI Course have gained skills to apply generative tools, structured prompting, workflow design, and audience intelligence to real content challenges. They can confidently deliver content that performs—reliably, ethically, and at scale in an AI-enabled environment.
• Analyze modern AI capabilities and identify where automation and generative tools strengthen content strategy and production. • Conduct AI-supported audience, market, and search research to uncover insights that guide messaging and editorial decisions. • Synthesize internal documents, performance data, and research inputs with AI tools to generate strategic recommendations. • Design content strategies, themes, and calendars aligned to business goals, audience needs, and search intent. • Apply structured prompting and generative frameworks to produce, refine, and optimize high-quality content assets. • Evaluate and improve content using SEO, accessibility, UX, and performance analytics signals. • Build scalable content systems, workflows, and governance practices that ensure consistency, compliance, and responsible AI usage. • Present and justify a full AI-powered content strategy and execution plan through a polished, portfolio-ready capstone project.
Badge earners who've successfully completed the General Assembly Creative Production with AI Course have gained skills to design AI-assisted creative strategies, evaluate and refine AI-generated content, build scalable production workflows, and apply responsible AI practices. They can ideate, produce, optimize, and communicate AI-powered creative work with clarity and confidence.
• Describe the evolving role of AI in modern creative workflows and evaluate where automation adds value. • Analyze and optimize creative processes by identifying steps where AI can enhance speed, quality, or ideation. • Apply brand integrity standards to AI-generated content to ensure voice, visuals, and messaging remain consistent. • Generate creative concepts, drafts, and visual directions using structured prompting and iterative refinement. • Adapt content across channels using AI support while maintaining clarity, strategic alignment, and audience fit. • Evaluate AI-generated content using editorial quality criteria, feedback loops, and basic experiment-driven insights. • Collaborate with cross-functional partners to articulate creative rationale and integrate AI responsibly. • Produce a full AI-assisted creative strategy and case-study portfolio demonstrating ethical and effective AI usage.
Badge earners who've successfully completed the General Assembly Data Analytics Course have gained skills to extract meaningful insights from complex datasets. They have learned essential data analysis and visualization skills and can confidently query databases, create compelling dashboards, and communicate findings that influence strategic outcomes.
Badge earners who've successfully completed the General Assembly UX Design Course have gained skills in cross-functional collaboration and ethical AI research leadership. They can confidently execute AI-enhanced approaches to qualitative analysis, quantitative synthesis, and strategic planning that strengthen rather than compromise research quality and user advocacy.
- Integrate AI tools into UX research and strategy to enhance how you gather, interpret, and apply user insights across the product lifecycle. - Apply design thinking and agile methods with AI-driven workflows to speed iteration and strengthen collaboration across product teams. - Develop and execute AI-informed research plans that align user needs, business goals, and technical opportunities. - Use AI to analyze complex data and turn research findings into clear, actionable design recommendations. - Communicate insights effectively through AI-assisted visualizations, storytelling, and tailored stakeholder presentations. - Uphold research quality, inclusivity, and ethics by mitigating bias and ensuring accessible, responsible AI use. - Collaborate effectively in interdisciplinary AI product teams, balancing user advocacy with technical and strategic priorities.
Badge earners who've successfully completed the General Assembly Product Management Course have learned to integrate AI throughout the product lifecycle—from customer discovery to delivery—so they can build better products faster and lead with confidence in an AI-powered world. They gained skills in conducting AI-enhanced customer research, building data-driven product strategies, and delivering compelling product pitches.
- Define the role, responsibilities, and career pathways of a product manager in the AI era. - Apply a product management mindset grounded in empathy, teamwork, and ethical decision-making. - Conduct customer research using interviews, data synthesis, and AI tools to uncover meaningful insights. - Develop personas, empathy maps, and customer journeys that illustrate customer needs and experiences. - Translate customer insights into product stories, goals, and measurable success metrics. - Design product visions and roadmaps that align with business strategy, supported by AI-driven analysis. - Plan and execute experiments to validate assumptions, using AI to prototype, analyze, and refine solutions. - Manage product development cycles with Agile frameworks, integrating AI into backlog, sprint, and QA processes.
Badge earners who've successfully completed the General Assembly Vibe Coding Course have gained skills to build functional websites, interactive mock-ups, and automated workflows by communicating with AI systems rather than writing complex code. They can confidently bridge the gap between strategic business vision and technical execution, rapidly iterate on digital concepts, demonstrate ideas to stakeholders, and lead innovation initiatives in the AI era.
• Master strategic prompt engineering to communicate complex requirements to AI systems. • Develop functional web prototypes by generating HTML, CSS, and JavaScript through natural language commands. • Architect multi-page websites with cohesive navigation and logical information structures. • Evaluate when to use AI-generated code versus no-code platforms for different business scenarios. • Automate communication workflows and data processing tasks using AI-assisted configuration. • Integrate dynamic data sources and APIs to create responsive, data-driven prototypes. • Debug and refine digital solutions using systematic problem-solving approaches with AI assistance. • Apply digital fluency principles to lead prototyping initiatives and drive innovation in professional context.
Badge earners who've successfully completed the General Assembly Build AI Agents Course have gained skills to design, build, and deploy autonomous AI agents using no-code tools and natural language instructions. They can confidently create agentic systems that can reason, act, integrate with tools, and operate independently to achieve real business goals.
• Master the core principles of agentic AI to distinguish autonomous systems from traditional generative AI tools. • Analyze how agents perceive, reason, and act by mapping the data, memory, and tool flows that drive autonomy. • Design effective prompts and contexts that guide agent behavior and ensure reliable, goal-oriented task execution. • Build fully functional no-code agents using visual platforms such as FlowiseAI or GPT Builder. • Integrate agents with external tools and data sources like Google Sheets, Slack, and Zapier to automate real workflows. • Apply UX and conversational design techniques to create intuitive, human- centered agent interactions. • Evaluate and refine agent performance using testing, logs, and iterative improvement strategies. • Implement ethical, privacy, and security guardrails to ensure responsible and trustworthy agentic AI systems.
Badge earners who've successfully completed the General Assembly Agentic AI Strategy Course have gained skills to understand, plan, and lead organizational transformation in an era of autonomous, goal-driven AI systems. They can confidently navigate the strategic, cultural, and leadership frameworks needed to evaluate where agentic AI creates value and how to implement it responsibly across an organization.
•Understand the foundations of agentic transformation and how autonomous, goal-directed systems reshape decision-making, innovation, and organizational performance. •Analyze the global landscape of AI adoption and emerging trends to identify opportunities and challenges relevant to your organization or sector. • Develop a digital and adaptive mindset that supports experimentation, collaboration, and cultural readiness for AI-driven change. • Identify and prioritize high-impact use cases where agentic systems can enhance efficiency, creativity, and strategic outcomes. • Design intelligent workflows and hybrid human–AI teams that promote autonomy, accountability, and continuous improvement. • Apply principles of data governance, ethics, and responsible AI to ensure trust, compliance, and transparency in agentic operations. • Strengthen leadership and communication capabilities to drive engagement, build trust, and guide teams through technological transformation. • Translate insights into action through hands-on prototyping and strategic planning, creating a sustainable roadmap for agentic transformation and personal growth in the AI era.
Badge earners who've successfully completed the General Assembly AI Marketing pathway have gained practical skills to confidently apply AI across content strategy, creative production, performance marketing, and data analytics. They can build and run AI-powered campaigns across search, social, and commerce, generate ideas, produce content assets, and adapt them across channels using AI while maintaining brand integrity, use AI for audience research, SEO strategy, editorial planning, and governance, and collect, analyze, and visualize marketing data using AI.