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Programming & CS Foundations

How to think like a programmer, from zero, before any AI or math content

8 phases. 31 lessons. 31 labs. 1 capstone. The true zero-background starting point for this entire platform — bits and memory, real Python (variables through classes), the data structures and algorithmic reasoning every later course assumes, testing and code quality, and a self-designed capstone tool. No prior programming or CS background required.

8 phases · 31 lessons · 31 labs · 1 capstone

No prerequisites — this is a starting track.

Outcomes you will have by the end

• A self-designed, tested Python tool — A real multi-file program built from your own written design, not a followed tutorial — tested with pytest and linted clean with ruff.
• A working mental model of how a computer runs your code — Bits, memory, the fetch-execute cycle, and why CPUs and GPUs are shaped differently — the foundation every later AI-focused course on this platform builds on without re-explaining.
• Real algorithmic judgment — The ability to recognize when code will silently become too slow for real data, derived from measured timing, not memorized Big-O notation.
• Verified certificate of completion — Issued by DeVenture Academy, tied to your completion record. Lists the programming, data structures, and testing skills you built.

What you will be able to do

Python Programming · Data Structures · Algorithmic Thinking · Object-Oriented Design · Automated Testing · Code Quality

Every phase, every lesson, every project

• How Computers Actually Work (3 lessons) — free — Bits and bytes, RAM vs. disk, the fetch-execute cycle, CPU vs. GPU cores, and classifying syntax vs. runtime vs. logic errors.
  • What a Computer Actually Does
  • CPU vs. GPU, and Why It'll Matter Later
  • Reading a Program: Syntax, Interpreters, and Why Errors Happen
• Programming Fundamentals I (5 lessons) — free — Variables and assignment, if/elif/else, for and while loops, functions with parameters and return values, modules and imports.
  • Variables, Types, and the Assignment Trap
  • Control Flow — Conditionals
  • Control Flow — Loops
  • Functions — Abstraction and Reuse
  • Modules and Imports — Organizing Code
• Programming Fundamentals II (5 lessons) — Lists, tuples, dicts, and sets; string processing; reading and writing files and CSVs; try/except error handling.
  • Lists and Tuples — Ordered Collections
  • Dictionaries and Sets — Lookup-Based Collections
  • Strings and Text Processing
  • Reading and Writing Files
  • Exceptions — Handling Things Going Wrong
• Data & Statistical Literacy (4 lessons) — Categorical vs. numerical data, mean/median/mode/standard deviation, distribution shape, correlation vs. causation.
  • What Data Actually Is
  • Descriptive Statistics — Center and Spread
  • Distributions — Why Shape Matters
  • Correlation, Causation, and Lying with Charts
• Computational Thinking & Algorithms (5 lessons) — Linear vs. binary search, Big-O reasoning from measured timing, sorting, recursion, and choosing the right data structure.
  • Searching — Slow vs. Fast
  • Big-O Intuition — Naming What You Just Saw
  • Sorting
  • Recursion
  • Choosing the Right Data Structure
• Object-Oriented & Modular Thinking (4 lessons) — Classes, __init__ and self, the configure-once/reuse-repeatedly pattern behind ML libraries, composition over inheritance.
  • Classes and Objects — Bundling Data and Behavior
  • Why ML Codebases Are Full of Classes
  • Composition and Modular Design
  • Writing Code Other People (and Future You) Can Read
• Testing & Code Quality (3 lessons) — Why 'it ran' isn't 'it's correct,' writing real pytest tests, reading test failures, and linting with ruff.
  • Why Untested Code Lies to You
  • Writing Your First Real Tests
  • Linters, Formatters, and Style
• Capstone (2 lessons) — Designing a real tool before writing code, building incrementally, and shipping a tested, linted, multi-file program.
  • Design Your Tool
  • Build, Test, and Ship It

The technologies you will use

Python · pytest · ruff

Roles this course prepares you for

• Junior Python Developer ($70k–$95k) — Write, test, and maintain Python code as part of a team — the exact foundation this course builds: correct control flow, functions, data structures, and tests, not yet AI-specific.
• QA / Test Engineer (entry-level) ($65k–$90k) — Write and run automated tests, triage bug reports, and reason about edge cases — directly built by Phase 6's testing discipline and Phase 4's edge-case habits.

Common questions

I've genuinely never written a line of code — is this really for me?

Yes — this is the platform's true zero-background starting point. Phase 0 starts before any Python at all, with what a computer is actually doing when it runs your code.

Do I need this before the AI-focused courses?

If you already write Python comfortably — functions, loops, classes, reading a traceback — you can skip straight to a course like ML & AI Engineering. This course exists specifically for people who don't have that yet.

How is this different from a generic 'learn Python' course?

Every example and lab is built to feed directly into this platform's AI-focused courses — the data-structure choices, the testing discipline, and the algorithmic reasoning are the exact things those courses assume you already have.

Continue your learning path

Engineering Practices & Tooling · Quantitative Foundations · Data Analysis & Visualization · ML & AI Engineering

Start the Programming & CS Foundations course

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