Lesson 2 – The Gap Year: From Foundations to Product, Tech, Data & AI Diligence
October 2025 – September 2026 • A structured self-study plan to prepare for a Computer Science degree and understand how technology is evaluated in the real world.
1. Year Overview
The year runs from October 2025 to September 2026. You’ll build a foundation in computer science, then extend it into real-world product, technology, data, and AI diligence – the disciplines that connect engineering with business outcomes.
- Semester 1 (Oct – Mar): Core computer-science concepts and programming skills.
- Semester 2 (Apr – Sep): Product thinking, system architecture, data pipelines, and AI fundamentals through the lens of diligence and value creation.
2. Semester 1 – Computer Science Foundations
Key Pillars
| Area | Goal | Suggested Courses |
|---|---|---|
| Programming | Fluent Python, problem solving | Harvard CS50x; Python for Everybody |
| Data Structures & Algorithms | Understand lists, trees, graphs, complexity | Coursera – U of London DSA; freeCodeCamp |
| Computer Systems | Learn how hardware runs code | Nand2Tetris; Crash Course CS |
| Web Development | Front-end basics and simple apps | The Odin Project; freeCodeCamp Web Track |
| Maths & Logic | Binary, logic, probability, sets | Brilliant.org; MIT OCW 6.042J (for reference) |
Goal by March 2026: Confident Python developer with two or three small projects uploaded to GitHub.
3. Semester 2 – Product, Tech, Data & AI Diligence Foundations
From April onwards you’ll shift from “how to code” to “how technology creates value”. You’ll study how investors and operators evaluate software companies – the same diligence process used in private equity.
3.1 Product Thinking
- What makes a good product: usability, adoption, retention, scalability.
- Agile vs Waterfall, MVP design, prioritisation frameworks (RICE, MoSCoW).
- Intro courses: Coursera Product Management Specialization – University of Virginia.
3.2 Technology Architecture
- Modern stack fundamentals: front-end, APIs, databases, cloud.
- Microservices, CI/CD, DevOps basics (GitHub Actions, Docker).
- Recommended: AWS Cloud Practitioner Essentials (Free).
3.3 Data Engineering & Analytics
- Data pipelines – ETL, warehousing, dashboards.
- SQL fundamentals, data cleaning in Python (Pandas).
- Recommended: IBM Data Science Foundations or freeCodeCamp Data Analysis with Python.
3.4 AI & Machine Learning Basics
- What AI is and is not – supervised vs unsupervised learning.
- Intro to neural networks, LLMs, and AI ethics.
- Courses: Elements of AI (University of Helsinki); AI for Everyone (Andrew Ng).
3.5 Technology Due Diligence & Value Creation
- How investors assess a technology company: product fit, architecture quality, data maturity, AI readiness.
- Read Imperem-style case summaries and mock reports (each month review a different sector e.g. FinTech, EdTech, HealthTech).
- Self-project: pick a tech company, analyse its stack and data model from public sources, then write a 2-page “tech health summary”.
Goal by September 2026: Understand how to discuss technology not just as code, but as a business asset that creates value and risk.
4. Month-by-Month Outline
| Month | Theme | Output / Project |
|---|---|---|
| Oct – Nov 2025 | Python Foundations | Text-based games or utility scripts |
| Dec 2025 – Jan 2026 | Algorithms & Data Structures | Sorting visualiser or basic search demo |
| Feb – Mar 2026 | Web Basics + GitHub Portfolio | Personal website and two repos |
| Apr 2026 | Product Management Foundations | Mock product brief + feature prioritisation exercise |
| May – Jun 2026 | Cloud & Architecture Fundamentals | Diagram a sample web app stack |
| Jul – Aug 2026 | Data & AI Essentials | Notebook showing simple data analysis or AI demo |
| Sep 2026 | Diligence Simulation | 2-page Tech Health Assessment Report |
5. Reading & Reference List
- Code: The Hidden Language of Computer Hardware and Software – Charles Petzold
- The Pragmatic Programmer – Hunt & Thomas
- Computer Science Distilled – Wladston Ferreira Filho
- Lean Product Playbook – Dan Olsen
- Designing Data-Intensive Applications – Martin Kleppmann (advanced)
- AI Superpowers – Kai-Fu Lee
6. End-of-Year Checklist
- ✅ Comfortable writing and debugging Python programs.
- ✅ Understand how computers, networks and data pipelines work.
- ✅ Built 3–4 small projects and a personal portfolio site.
- ✅ Can explain basic product concepts and technical architecture in plain English.
- ✅ Produced one mock diligence summary demonstrating your understanding of how product, tech, data and AI fit together.
Outcome: Enter your degree next October not as a beginner but as a junior technologist who already thinks like an engineer and an analyst.