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Available Courses & Curriculum
Below is an overview of our modular courses, each focused on practical, hands-on skill development. Courses are designed to be taken individually or combined to meet individual or unit-level operational needs.
Learning Objectives
- Set up and navigate a coding environment
Gain an understanding of the tools, terminals, and editors essential for working with any technical system. This allows participants to operate independently in engineering workflows. - Master programming fundamentals
Variables, data types, loops, functions, and control flows are the universal building blocks of all software. Those who understand them can reason about programs, debug issues, and create useful scripts. - Process files and work with structured data
Missions rely on logs, telemetry, reports, CSVs, and text data. The ability to read, parse, and transform files enables the automation of manual tasks and analysis at scale.
Resulting Capability
Participants complete the course with the ability to write simple, effective Python scripts to clean data, parse files, and automate repetitive tasks, skills that are immediately useful for analysts, operators, and technologists across the USSF.
Learning Objectives
- Build reliability into Python code via testing
Testing ensures programs behave as expected, which is critical for mission environments where code accuracy cannot be left to chance. - Write programs that connect to public APIs
Many modern systems expose data through APIs. Learn to pull, send, and integrate live data to automate workflows and build powerful data-driven tools. - Understand and work with larger programs using modules and packages
Real-world software isn’t a single file. Learn to contribute to multi-file codebases, understand architecture, and collaborate with engineering teams.
- Model real-world problems with advanced data structures
Learn how to map real mission scenarios to code, unlocking the ability to prototype solutions for scheduling, resource allocation, data pipeline steps, and more.
Resulting Capability
Participants complete the course ready to build small applications, automate multi-step processes, integrate external data sources, and work within a larger software ecosystem. These are foundations for future work in analytics, automation, and software roles.
Learning Objectives
- Set up and understand a TypeScript development environment
Gain fluency with the tools used by production engineering teams, including terminal commands, editors, bundlers, and TypeScript compilers.
- Learn core programming fundamentals
Learn core programming fundamentals – types, functions, control flow, objects, and arrays – to reinforce correctness and build skills applicable to enterprise software workflows. - Understand basic web concepts (HTML/CSS/DOM)
Learn how the browser works in order to build interactive, dynamic webpages, and understand how front-end applications actually function.
Resulting Capability
Participants complete the course with the ability to build simple interactive web pages, understand strongly typed code, and reason about how modern front-end systems operate. This is ideal groundwork for future front-end or application development roles.
Learning Objectives
- Frame analytical problems and prepare reliable data
Develop an analytics mindset by asking the right questions before using tools. Ingest, clean, and structure real-world datasets, including CSVs, APIs, and tabular data, to ensure accuracy and reliability before analysis begins. - Analyze data using industry-standard tools
Use Python, pandas, and NumPy to filter, group, aggregate, and explore structured data, uncovering meaningful patterns and trends.
- Communicate insights through visualization and narrative
Create clear, effective visualizations via Matplotlib and Seaborn, and translate findings into concise, decision-ready narratives that support operational and strategic decisions.
Resulting Capability
Participants complete the course with the ability to clean and structure datasets, perform exploratory analysis, create meaningful visualizations, and communicate insights that support operational decision-making across intelligence, cyber, operations, acquisition, and program management roles.
Learning Objectives
- Adopt the product manager mindset and define customer problems
Understand the role of a product manager, collaborate across functions, and translate research into validated problem statements grounded in user and business needs. - Build strategy and execute with structured methods
Define product vision, success metrics, and priorities, applying Agile practices to manage backlogs, write clear requirements, and guide iterative delivery.
- Design, validate, and deliver solutions with business alignment
Prototype and test ideas, collaborate with technical teams, assess risk and ROI, and develop business cases that align stakeholders around a shared roadmap.
- Communicate and present product recommendations effectively
Develop executive-ready narratives, apply structured storytelling, and present data-informed proposals with clarity and confidence.
Resulting Capability
Participants complete the course with the ability to identify validated customer problems, define product strategy, prioritize effectively, collaborate with engineering teams, and communicate structured, data-informed product recommendations that support mission-aligned innovation.
Learning Outcomes
Each lab builds on the previous one, moving participants from safe individual use to team-level, operational AI execution.
- Build foundational AI understanding and apply safe usage practices
Develop a practical understanding of how AI systems are used in knowledge work, including their capabilities, limitations, and risks. Apply safe handling practices and responsible usage principles in everyday workflows.
- Prepare inputs and design structured, repeatable AI processes
Recognize how data quality, structure, and documentation impact AI performance, and design structured prompts and reusable processes that move beyond one-off interactions.
- Evaluate outputs and apply quality control and risk-based oversight
Use testing, review practices, and documentation standards to assess AI outputs, identify failure modes, and apply appropriate levels of human oversight based on workflow risk.
- Identify, prioritize, and integrate AI into real workflows
Evaluate where AI can add value, prioritize opportunities, and integrate AI into workflows with clear ownership, defined roles, and consistent operational controls.
Resulting Capability
Participants complete the program with the ability to:
- Make informed decisions about when and where AI should be used, based on data readiness, risk, and practical constraints
- Create and maintain structured AI processes that improve consistency and reduce reliance on ad hoc prompting
- Assess the reliability of AI outputs and intervene appropriately when results do not meet defined standards
- Operationalize AI within team workflows by establishing clear expectations, controls, and points of accountability
Participants leave with practical artifacts—including readiness assessments, process templates, scoring frameworks, quality controls, and workflow maps—that enable them to implement AI in a consistent, safe, and operationally sustainable way within their teams.
Learning Outcomes
Participants develop the ability to move from ambiguous feature requests to production-ready implementations by combining structured specifications, test-driven development with AI, and repeatable engineering workflows.
- Understand model behavior, limitations, and responsible deployment
Develop awareness of how AI systems behave in technical environments, including how they fill gaps in specifications, where risks emerge, and how to apply appropriate constraints, security, and scope boundaries when integrating AI into engineering workflows.
- Design AI-ready specifications and implementation contracts
Translate ambiguous feature requests into structured specifications, requirements, and acceptance criteria that AI systems can implement without guessing. Apply formal requirement structures, examples, and explicit constraints to eliminate ambiguity and define clear implementation contracts.
- Design and apply AI-enabled development workflows
Use structured development patterns—including test-driven development and human–AI pairing—to generate, evaluate, and refine code, tests, and design decisions while maintaining control over implementation quality and scope.
- Execute and evaluate AI-assisted software delivers
Decompose features into small, testable units, generate scaffolds from specifications, validate outputs through acceptance criteria and testing, and deliver review-ready code using disciplined workflows that ensure reliability, traceability, and maintainability.
Resulting Capability
Participants complete the program with the ability to:
- Translate ambiguous feature requests into structured specifications and acceptance criteria that define clear, testable implementation contracts
- Build and apply AI-assisted development workflows using test-driven development, structured prompting, and human–AI pairing practices
- Evaluate AI-generated outputs—including code, tests, and design decisions—using verification methods, quality criteria, and engineering judgment
- Deliver production-ready changes through disciplined workflows that include slicing, testing, debugging, and review to ensure reliability and maintainability
These skills enable engineers to integrate AI into engineering processes while maintaining code quality, system reliability, and responsible deployment standards.
Available Courses & Curriculum
Below is an overview of our modular courses, each focused on practical, hands-on skill development. Courses are designed to be taken individually or combined to meet individual or unit-level operational needs.
Learning Objectives
- Set up and navigate a coding environment
Gain an understanding of the tools, terminals, and editors essential for working with any technical system. This allows participants to operate independently in engineering workflows. - Master programming fundamentals
Variables, data types, loops, functions, and control flows are the universal building blocks of all software. Those who understand them can reason about programs, debug issues, and create useful scripts. - Process files and work with structured data
Missions rely on logs, telemetry, reports, CSVs, and text data. The ability to read, parse, and transform files enables the automation of manual tasks and analysis at scale.
Resulting Capability
Participants complete the course with the ability to write simple, effective Python scripts to clean data, parse files, and automate repetitive tasks, skills that are immediately useful for analysts, operators, and technologists across the USSF.
Learning Objectives
- Build reliability into Python code via testing
Testing ensures programs behave as expected, which is critical for mission environments where code accuracy cannot be left to chance. - Write programs that connect to public APIs
Many modern systems expose data through APIs. Learn to pull, send, and integrate live data to automate workflows and build powerful data-driven tools. - Understand and work with larger programs using modules and packages
Real-world software isn’t a single file. Learn to contribute to multi-file codebases, understand architecture, and collaborate with engineering teams.
- Model real-world problems with advanced data structures
Learn how to map real mission scenarios to code, unlocking the ability to prototype solutions for scheduling, resource allocation, data pipeline steps, and more.
Resulting Capability
Participants complete the course ready to build small applications, automate multi-step processes, integrate external data sources, and work within a larger software ecosystem. These are foundations for future work in analytics, automation, and software roles.
Learning Objectives
- Set up and understand a TypeScript development environment
Gain fluency with the tools used by production engineering teams, including terminal commands, editors, bundlers, and TypeScript compilers.
- Learn core programming fundamentals
Learn core programming fundamentals – types, functions, control flow, objects, and arrays – to reinforce correctness and build skills applicable to enterprise software workflows. - Understand basic web concepts (HTML/CSS/DOM)
Learn how the browser works in order to build interactive, dynamic webpages, and understand how front-end applications actually function.
Resulting Capability
Participants complete the course with the ability to build simple interactive web pages, understand strongly typed code, and reason about how modern front-end systems operate. This is ideal groundwork for future front-end or application development roles.
Learning Objectives
- Frame analytical problems and prepare reliable data
Develop an analytics mindset by asking the right questions before using tools. Ingest, clean, and structure real-world datasets, including CSVs, APIs, and tabular data, to ensure accuracy and reliability before analysis begins. - Analyze data using industry-standard tools
Use Python, pandas, and NumPy to filter, group, aggregate, and explore structured data, uncovering meaningful patterns and trends.
- Communicate insights through visualization and narrative
Create clear, effective visualizations via Matplotlib and Seaborn, and translate findings into concise, decision-ready narratives that support operational and strategic decisions.
Resulting Capability
Participants complete the course with the ability to clean and structure datasets, perform exploratory analysis, create meaningful visualizations, and communicate insights that support operational decision-making across intelligence, cyber, operations, acquisition, and program management roles.
Learning Objectives
- Adopt the product manager mindset and define customer problems
Understand the role of a product manager, collaborate across functions, and translate research into validated problem statements grounded in user and business needs. - Build strategy and execute with structured methods
Define product vision, success metrics, and priorities, applying Agile practices to manage backlogs, write clear requirements, and guide iterative delivery.
- Design, validate, and deliver solutions with business alignment
Prototype and test ideas, collaborate with technical teams, assess risk and ROI, and develop business cases that align stakeholders around a shared roadmap.
- Communicate and present product recommendations effectively
Develop executive-ready narratives, apply structured storytelling, and present data-informed proposals with clarity and confidence.
Resulting Capability
Participants complete the course with the ability to identify validated customer problems, define product strategy, prioritize effectively, collaborate with engineering teams, and communicate structured, data-informed product recommendations that support mission-aligned innovation.
Learning Outcomes
Each lab builds on the previous one, moving participants from safe individual use to team-level, operational AI execution.
- Build foundational AI understanding and apply safe usage practices
Develop a practical understanding of how AI systems are used in knowledge work, including their capabilities, limitations, and risks. Apply safe handling practices and responsible usage principles in everyday workflows.
- Prepare inputs and design structured, repeatable AI processes
Recognize how data quality, structure, and documentation impact AI performance, and design structured prompts and reusable processes that move beyond one-off interactions.
- Evaluate outputs and apply quality control and risk-based oversight
Use testing, review practices, and documentation standards to assess AI outputs, identify failure modes, and apply appropriate levels of human oversight based on workflow risk.
- Identify, prioritize, and integrate AI into real workflows
Evaluate where AI can add value, prioritize opportunities, and integrate AI into workflows with clear ownership, defined roles, and consistent operational controls.
Resulting Capability
Participants complete the program with the ability to:
- Make informed decisions about when and where AI should be used, based on data readiness, risk, and practical constraints
- Create and maintain structured AI processes that improve consistency and reduce reliance on ad hoc prompting
- Assess the reliability of AI outputs and intervene appropriately when results do not meet defined standards
- Operationalize AI within team workflows by establishing clear expectations, controls, and points of accountability
Participants leave with practical artifacts—including readiness assessments, process templates, scoring frameworks, quality controls, and workflow maps—that enable them to implement AI in a consistent, safe, and operationally sustainable way within their teams.
Learning Outcomes
Participants develop the ability to move from ambiguous feature requests to production-ready implementations by combining structured specifications, test-driven development with AI, and repeatable engineering workflows.
- Understand model behavior, limitations, and responsible deployment
Develop awareness of how AI systems behave in technical environments, including how they fill gaps in specifications, where risks emerge, and how to apply appropriate constraints, security, and scope boundaries when integrating AI into engineering workflows.
- Design AI-ready specifications and implementation contracts
Translate ambiguous feature requests into structured specifications, requirements, and acceptance criteria that AI systems can implement without guessing. Apply formal requirement structures, examples, and explicit constraints to eliminate ambiguity and define clear implementation contracts.
- Design and apply AI-enabled development workflows
Use structured development patterns—including test-driven development and human–AI pairing—to generate, evaluate, and refine code, tests, and design decisions while maintaining control over implementation quality and scope.
- Execute and evaluate AI-assisted software delivers
Decompose features into small, testable units, generate scaffolds from specifications, validate outputs through acceptance criteria and testing, and deliver review-ready code using disciplined workflows that ensure reliability, traceability, and maintainability.
Resulting Capability
Participants complete the program with the ability to:
- Translate ambiguous feature requests into structured specifications and acceptance criteria that define clear, testable implementation contracts
- Build and apply AI-assisted development workflows using test-driven development, structured prompting, and human–AI pairing practices
- Evaluate AI-generated outputs—including code, tests, and design decisions—using verification methods, quality criteria, and engineering judgment
- Deliver production-ready changes through disciplined workflows that include slicing, testing, debugging, and review to ensure reliability and maintainability
These skills enable engineers to integrate AI into engineering processes while maintaining code quality, system reliability, and responsible deployment standards.
Available Courses & Curriculum
Below is an overview of our modular courses, each focused on practical, hands-on skill development. Courses are designed to be taken individually or combined to meet individual or unit-level operational needs.
Learning Objectives
- Set up and navigate a coding environment
Gain an understanding of the tools, terminals, and editors essential for working with any technical system. This allows participants to operate independently in engineering workflows. - Master programming fundamentals
Variables, data types, loops, functions, and control flows are the universal building blocks of all software. Those who understand them can reason about programs, debug issues, and create useful scripts. - Process files and work with structured data
Missions rely on logs, telemetry, reports, CSVs, and text data. The ability to read, parse, and transform files enables the automation of manual tasks and analysis at scale.
Resulting Capability
Participants complete the course with the ability to write simple, effective Python scripts to clean data, parse files, and automate repetitive tasks, skills that are immediately useful for analysts, operators, and technologists across the USSF.
Learning Objectives
- Build reliability into Python code via testing
Testing ensures programs behave as expected, which is critical for mission environments where code accuracy cannot be left to chance. - Write programs that connect to public APIs
Many modern systems expose data through APIs. Learn to pull, send, and integrate live data to automate workflows and build powerful data-driven tools. - Understand and work with larger programs using modules and packages
Real-world software isn’t a single file. Learn to contribute to multi-file codebases, understand architecture, and collaborate with engineering teams.
- Model real-world problems with advanced data structures
Learn how to map real mission scenarios to code, unlocking the ability to prototype solutions for scheduling, resource allocation, data pipeline steps, and more.
Resulting Capability
Participants complete the course ready to build small applications, automate multi-step processes, integrate external data sources, and work within a larger software ecosystem. These are foundations for future work in analytics, automation, and software roles.
Learning Objectives
- Set up and understand a TypeScript development environment
Gain fluency with the tools used by production engineering teams, including terminal commands, editors, bundlers, and TypeScript compilers.
- Learn core programming fundamentals
Learn core programming fundamentals – types, functions, control flow, objects, and arrays – to reinforce correctness and build skills applicable to enterprise software workflows. - Understand basic web concepts (HTML/CSS/DOM)
Learn how the browser works in order to build interactive, dynamic webpages, and understand how front-end applications actually function.
Resulting Capability
Participants complete the course with the ability to build simple interactive web pages, understand strongly typed code, and reason about how modern front-end systems operate. This is ideal groundwork for future front-end or application development roles.
Learning Objectives
- Frame analytical problems and prepare reliable data
Develop an analytics mindset by asking the right questions before using tools. Ingest, clean, and structure real-world datasets, including CSVs, APIs, and tabular data, to ensure accuracy and reliability before analysis begins. - Analyze data using industry-standard tools
Use Python, pandas, and NumPy to filter, group, aggregate, and explore structured data, uncovering meaningful patterns and trends.
- Communicate insights through visualization and narrative
Create clear, effective visualizations via Matplotlib and Seaborn, and translate findings into concise, decision-ready narratives that support operational and strategic decisions.
Resulting Capability
Participants complete the course with the ability to clean and structure datasets, perform exploratory analysis, create meaningful visualizations, and communicate insights that support operational decision-making across intelligence, cyber, operations, acquisition, and program management roles.
Learning Objectives
- Adopt the product manager mindset and define customer problems
Understand the role of a product manager, collaborate across functions, and translate research into validated problem statements grounded in user and business needs. - Build strategy and execute with structured methods
Define product vision, success metrics, and priorities, applying Agile practices to manage backlogs, write clear requirements, and guide iterative delivery.
- Design, validate, and deliver solutions with business alignment
Prototype and test ideas, collaborate with technical teams, assess risk and ROI, and develop business cases that align stakeholders around a shared roadmap.
- Communicate and present product recommendations effectively
Develop executive-ready narratives, apply structured storytelling, and present data-informed proposals with clarity and confidence.
Resulting Capability
Participants complete the course with the ability to identify validated customer problems, define product strategy, prioritize effectively, collaborate with engineering teams, and communicate structured, data-informed product recommendations that support mission-aligned innovation.
Learning Outcomes
Each lab builds on the previous one, moving participants from safe individual use to team-level, operational AI execution.
- Build foundational AI understanding and apply safe usage practices
Develop a practical understanding of how AI systems are used in knowledge work, including their capabilities, limitations, and risks. Apply safe handling practices and responsible usage principles in everyday workflows.
- Prepare inputs and design structured, repeatable AI processes
Recognize how data quality, structure, and documentation impact AI performance, and design structured prompts and reusable processes that move beyond one-off interactions.
- Evaluate outputs and apply quality control and risk-based oversight
Use testing, review practices, and documentation standards to assess AI outputs, identify failure modes, and apply appropriate levels of human oversight based on workflow risk.
- Identify, prioritize, and integrate AI into real workflows
Evaluate where AI can add value, prioritize opportunities, and integrate AI into workflows with clear ownership, defined roles, and consistent operational controls.
Resulting Capability
Participants complete the program with the ability to:
- Make informed decisions about when and where AI should be used, based on data readiness, risk, and practical constraints
- Create and maintain structured AI processes that improve consistency and reduce reliance on ad hoc prompting
- Assess the reliability of AI outputs and intervene appropriately when results do not meet defined standards
- Operationalize AI within team workflows by establishing clear expectations, controls, and points of accountability
Participants leave with practical artifacts—including readiness assessments, process templates, scoring frameworks, quality controls, and workflow maps—that enable them to implement AI in a consistent, safe, and operationally sustainable way within their teams.
Learning Outcomes
Participants develop the ability to move from ambiguous feature requests to production-ready implementations by combining structured specifications, test-driven development with AI, and repeatable engineering workflows.
- Understand model behavior, limitations, and responsible deployment
Develop awareness of how AI systems behave in technical environments, including how they fill gaps in specifications, where risks emerge, and how to apply appropriate constraints, security, and scope boundaries when integrating AI into engineering workflows.
- Design AI-ready specifications and implementation contracts
Translate ambiguous feature requests into structured specifications, requirements, and acceptance criteria that AI systems can implement without guessing. Apply formal requirement structures, examples, and explicit constraints to eliminate ambiguity and define clear implementation contracts.
- Design and apply AI-enabled development workflows
Use structured development patterns—including test-driven development and human–AI pairing—to generate, evaluate, and refine code, tests, and design decisions while maintaining control over implementation quality and scope.
- Execute and evaluate AI-assisted software delivers
Decompose features into small, testable units, generate scaffolds from specifications, validate outputs through acceptance criteria and testing, and deliver review-ready code using disciplined workflows that ensure reliability, traceability, and maintainability.
Resulting Capability
Participants complete the program with the ability to:
- Translate ambiguous feature requests into structured specifications and acceptance criteria that define clear, testable implementation contracts
- Build and apply AI-assisted development workflows using test-driven development, structured prompting, and human–AI pairing practices
- Evaluate AI-generated outputs—including code, tests, and design decisions—using verification methods, quality criteria, and engineering judgment
- Deliver production-ready changes through disciplined workflows that include slicing, testing, debugging, and review to ensure reliability and maintainability
These skills enable engineers to integrate AI into engineering processes while maintaining code quality, system reliability, and responsible deployment standards.