ARTIFICIAL INTELLIGENCE AND DATA (ANALYTICS, MACHINE LEARNING & GENAI)

Jobs today, not someday: AI/analytics skills power roles in fintech (payments/fraud), telco (network & churn), agri‑tech (yield, pricing), logistics (routing, demand), health (triage), and government dashboards.

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About this Course

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AI & Data (Analytics, Machine Learning & GenAI)
— Comprehensive Curriculum & Setup Guide

 

Why this Course Matters (Kenya Context)

• Jobs today, not someday: AI/analytics skills power roles in fintech (payments/fraud), telco (network & churn), agri‑tech (yield, pricing), logistics (routing, demand), health (triage), and government dashboards.
• Compliance‑ready talent: Kenya’s Data Protection Act (2019) and the Office of the Data Protection Commissioner require developers to build systems that respect data rights. Graduates trained in both AI/ML and governance are instantly valuable.
• Local data accessibility: Kenya National Bureau of Statistics (KNBS) provides economic, demographic, and sectoral datasets—perfect for applied projects.Practical GenAI demand: Businesses need people who can deploy assistants, document analysis systems, and RAG apps, and evaluate them for safety, bias, and cost.

 

Program Format & Timeline

• Cadence: 4 days/week × 3h/day (2h teaching + 1h practical) = 12h/week.
• Blocks: Every 2 months → 2‑week break.
• Practical month: Weeks 25–28 are capstone projects.
• Total completion: ~8.5 months
• Teaching weeks: 28 (incl. practical month)
• Breaks: 3 × 2 weeks = 6 weeks
• Total calendar span: ~34 weeks
• Contact hours: 28 × 12h = 336 hours.

Structure: - Block 1: Foundations (Weeks 1–8) → 2‑week break - Block 2: ML Core (Weeks 9–16) → 2‑week break - Block 3: Deep Learning, GenAI & MLOps (Weeks 17–24) → 2‑week break - Block 4: Practical/Capstone (Weeks 25–28)

 

Assessments & Deliverables

• Project 1 (Week 8): Analytics dashboard
• Project 2 (Week 16): End‑to‑end ML system with API/UI
• Project 3 (Week 24): GenAI or DL app
• Capstone (Weeks 25–28): Real project with repo, model card, deployment, and presentation
• Portfolio: README + demo video per project, plus final showcase

 

Software & Resources

Core stack: - Python 3.11, VS Code, Git, JupyterLab, Conda (Miniforge) - Pandas, NumPy, SciPy, Matplotlib, Seaborn, Statsmodels - scikit‑learn, FastAPI, Uvicorn, Streamlit - MLflow, DVC - PostgreSQL, psycopg2 - PyTorch (CPU), Transformers, Datasets, Sentence‑Transformers - FAISS, LangChain, LlamaIndex - Docker (optional)

Cloud/GPU options: - Google Colab, Kaggle, Paperspace Gradient (free tiers)

Compliance references: - Kenya Data Protection Act (2019) + ODPC guidance

Datasets (Kenya): - KNBS economic, CPI, GDP, agriculture, debt, etc.

Hardware (student laptop): - 8 GB RAM (16 GB preferred), 256 GB SSD, stable internet; optional NVIDIA GPU for local DL

 

Lab Machines Checklist

• CPU: i5/Ryzen 5/M1 or better
• RAM: 16 GB recommended (8 GB min)
• SSD: 256 GB min (512 preferred)
• OS: Windows 10/11 or Ubuntu 22.04
• Installed: Git, VS Code, Conda, PostgreSQL
• VS Code extensions: Python, Jupyter, Pylance, GitHub PRs, Docker (optional), SQLTools (optional)
• Shared dataset folder, backups enabled
• Projector/Wi‑Fi/whiteboard ready

 

Setup Scripts (One‑Click)

• Windows PowerShell: Installs Git, VS Code, Miniforge, PostgreSQL; creates aisdk conda env; installs PyTorch, scikit‑learn, MLflow, DVC, Transformers, Streamlit, LangChain, etc.

• Ubuntu Bash: Installs Git, VS Code (snap), PostgreSQL, Miniforge; creates aisdk conda env with same stack.

• Both scripts generate a post_install_test.py to verify environment.

 

Weekly Template

• Day 1: Concepts & examples
• Day 2: Patterns & applied lab
• Day 3: Pitfalls, performance, error analysis
• Day 4: Mini‑project sprint, demo, reflection

 

Graduate Outcomes

• Frame problems, clean/analyze data, build dashboards
• Train, evaluate, deploy ML models reproducibly
• Prototype GenAI apps with safety & evaluation
• Comply with Kenya data protection regulations
• Showcase 3 projects + capstone in a professional portfolio