Learn how to think about building systems that scale — client-server architecture, load balancers, caching, databases, reliability, and the practical estimation and async-processing skills used in real system design.
A practical, no-fluff introduction to version control — track your changes, collaborate without overwriting teammates, and stop naming files final_v2_FINAL.docx.
Learn the fundamentals of Microsoft Excel — the interface, entering and formatting data, everyday formulas and functions, sorting/filtering, charts, and good spreadsheet habits.
Learn Python from scratch — variables, control flow, lists and dictionaries, functions, string methods, error handling, and reading/writing files.
Learn the full data analysis cycle — understanding and cleaning data, summarizing with statistics and pivot tables, presenting findings honestly, and spotting trends and relationships.
Learn to navigate the terminal with confidence — the file system, viewing and editing files, searching, permissions, processes, and piping/redirection.
Learn to read and modify data with SQL — SELECT, WHERE, sorting, aggregates, GROUP BY, JOINs, and INSERT/UPDATE/DELETE — starting from installing SQLite on your own computer.
A practical introduction to cybersecurity fundamentals — the CIA triad, common threats like malware and phishing, and the core defenses (passwords, MFA, encryption, backups) every individual and organization relies on.
A practical introduction to cloud computing — service and deployment models, core compute/storage/networking services, working across AWS/Azure/GCP, and cloud security, cost management, and DevOps basics.
Learn what makes an AI system an "agent" — the perceive-reason-act loop, tool use, planning, memory, multi-agent architectures, and the guardrails needed to deploy agentic systems safely.
A foundational look at the practice of software engineering — the SDLC, requirements, clean code and design principles, testing and debugging, and how engineers work together through agile, architecture, and maintenance.
A practical, no-hype introduction to AI — how machines learn, how neural networks and generative AI work, where AI shows up today, and how to use AI tools well (and critically) in everyday work.
A hands-on introduction to the three building blocks of the web — structuring pages with HTML, styling them with CSS (including flexbox and responsive design), and adding interactivity with JavaScript and the DOM.
A focused, practical dive into generative AI — how LLMs and diffusion models actually work, the current tool landscape, prompt engineering techniques, RAG, and how to build with and responsibly govern generative AI.