Year One. (Will be attaching finished wok screenshots, links in areas of what’s studied in the roadmap. E.G attaching CLI calculator code and image at the project name under projects heading.)
Year 1 is the most important year. It determines whether the next 4 years accelerate or struggle.
Year 1 Goal:
- Think like a programmer
- Be mathematically comfortable with linear algebra & probability
- Implement basic ML models from scratch
- Build 8–12 solid projects
We will structure this month-by-month.
YEAR 1 — Month-by-Month Plan
Structure per month:
- CS Focus
- Math Focus
- ML Focus
- Project Milestone
MONTH 1 — Python Foundations
CS
- Variables
- Data types
- Conditionals
- Loops
- Functions
- Lists & dictionaries
- Basic file handling
- Error handling
Math
- Algebra refresh
- Functions & graphs
- Basic equations
ML
- What is ML?
- Types of ML
- Simple intuition of regression
Project
Build:
- CLI Student Manager
- CLI Calculator
- File-based task tracker
Goal:
Comfortable writing 300–500 line Python programs.
MONTH 2 — Data Structures & Complexity
CS
- Big-O notation
- Arrays vs Lists
- Stacks
- Queues
- Recursion
- Sorting algorithms (bubble, merge, quick)
Math
- Intro to vectors
- Basic matrix operations
ML
- Linear Regression (from scratch)
- Loss functions
- Gradient descent intuition
Project
Implement:
- Sorting visualizer
- Linear regression from scratch (no libraries)
Goal:
Understand efficiency and basic optimization.
MONTH 3 — Trees & Hashing
CS
- Hash tables
- Binary Trees
- Binary Search Trees
- Tree traversal (DFS, BFS)
Math
- Matrix multiplication
- Linear combinations
- Dot products
ML
- Logistic Regression
- Classification basics
- Sigmoid function deeply understood
Project
Build:
- Contact manager using hash maps
- Logistic regression classifier from scratch
Goal:
Understand data organization deeply.
MONTH 4 — Graphs & Intermediate Python
CS
- Graph representations
- BFS
- DFS
- Shortest path basics
- OOP fundamentals
Math
- Matrix transformations
- Systems of linear equations
ML
- Model evaluation:
- Accuracy
- Precision
- Recall
- Confusion matrix
Project
Build:
- Graph traversal simulator
- ML classifier with evaluation metrics
Goal:
Algorithmic thinking becomes natural.
MONTH 5 — Databases & Applied ML
CS
- SQL basics
- CRUD operations
- Data modeling basics
Math
- Probability fundamentals
- Random variables
- Mean & variance
- Basic distributions
ML
- Decision Trees
- Overfitting & underfitting
- Cross-validation
Project
Build:
- Python app connected to SQL database
- Decision tree classifier using scikit-learn
Goal:
Move from theory to applied data systems.
MONTH 6 — Mid-Year Integration
CS
- Refactor older projects
- Clean architecture basics
- Code organization
- Testing basics
Math
- Conditional probability
- Bayes theorem
ML
- Random Forest
- Feature engineering basics
Project
Major project:
- Student performance prediction system
- Data cleaning
- Feature engineering
- Model training
- Evaluation
Goal:
Full ML workflow implemented.
Checkpoint:
You should be able to explain:
- Gradient descent
- Overfitting
- Time complexity
- Basic probability
MONTH 7 — Advanced Algorithms
CS
- Heap
- Priority Queue
- Advanced recursion
- Divide & conquer
Math
- Eigenvalues (intro)
- Eigenvectors (intuition level)
ML
- Regularization (L1, L2)
- Bias-variance tradeoff
Project
Build:
- Heap-based task scheduler
- ML model with regularization comparison
Goal:
Start thinking in optimization terms.
MONTH 8 — Numerical Thinking
CS
- Advanced problem solving
- Coding interview-style problems
- Complexity optimization
Math
- Matrix decomposition intuition
- Linear transformations visualized
ML
- k-Nearest Neighbors
- Distance metrics
Project
Build:
- KNN classifier from scratch
- Compare models performance
Goal:
Comfortable implementing algorithms independently.
MONTH 9 — Real Data Work
CS
- File parsing
- Data pipelines basics
- Modular code structure
Math
- Standard deviation
- Variance deeply
- Statistical intuition
ML
- Data preprocessing
- Normalization
- Handling missing data
Project
End-to-end ML pipeline:
- Load dataset
- Clean data
- Train models
- Compare results
Goal:
Operate like a junior ML engineer.
MONTH 10 — Mini System Design
CS
- Basic system design principles
- API basics
- Modular architecture
Math
- Review & strengthen weak areas
ML
- Hyperparameter tuning
- Grid search
- Cross-validation deeply
Project
Deploy simple ML model as API (Flask/FastAPI)
Goal:
Understand production thinking.
MONTH 11 — Integration & Speed
CS
- Solve 3–4 algorithm problems weekly
- Optimize older code
Math
- Consolidation month
ML
- Ensemble methods
- Compare all models studied
Project
Capstone:
- Full ML system with UI or API
- Clean code
- Documentation
- GitHub portfolio polished
MONTH 12 — Mastery Consolidation
Review:
- All data structures
- All algorithms learned
- All ML models
Major Final Project:
Design something meaningful in your environment in Uganda:
- Agriculture prediction
- Education performance model
- Traffic analysis
- Local business analytics
Make it real.
Document everything.
End of Year 1 Outcome
If executed seriously, you will have:
- Strong Python ability
- Real understanding of algorithms
- Linear algebra & probability foundation
- 8–12 solid projects
- Real ML intuition
- Public GitHub profile on GitHub
- Growing authority on LinkedIn (weekly posts)
This is stronger than most university Year 1 CS students globally.