Master’s Program
Data Scientist 4.0 & Industrial Data Analytics
End-to-end ML + GenAI, from data to production systems
Overview
Data Scientist 4.0 is a modern, applied track built for people who want to ship real AI products. You’ll learn the full lifecycle: data preparation, modeling (ML & deep learning), GenAI patterns, evaluation, deployment, monitoring, and iteration with production constraints.
Key outcomes
- Build reliable ML pipelines with strong data quality foundations
- Train, evaluate, and debug models using robust methodology
- Apply GenAI patterns (RAG, agents, guardrails) with measurable KPIs
- Deploy production services (FastAPI), containers (Docker) and CI/CD basics
- Deliver a portfolio of end-to-end projects with documentation
Format
- Flexible: 4-week intensive or 10-week part-time format
- Weekly deliverables + checkpoints
- Portfolio-based evaluation
- Remote-friendly (worldwide cohorts)
Tools
- Python, Pandas, NumPy
- scikit-learn, PyTorch or TensorFlow
- FastAPI, Docker
- Git/GitHub, CI/CD fundamentals
- MLflow basics (tracking & versioning)
- Vector DB concepts (RAG)
Detailed program
A production-first curriculum aligned with today’s hiring standards: ML systems, deployment, monitoring, and GenAI (RAG) patterns used in real products.
Module 1 — Data Foundations & Quality
Build clean, reliable datasets and prevent downstream quality issues.
2–3 weeks
Module 1 — Data Foundations & Quality
Build clean, reliable datasets and prevent downstream quality issues.
What you’ll learn
- Advanced pandas patterns (joins, reshaping, performance basics)
- Data quality checks (missingness, outliers, duplicates, schema validation)
- SQL for analytics (CTE, window functions, query design basics)
- Leakage detection and reproducible dataset building
- Project structure: src/, configs, notebooks policy, reproducibility habits
Skills you’ll gain
Module 2 — Statistics, Experimentation & Decision Metrics
Make decisions with correct metrics and avoid common statistical traps.
2 weeks
Module 2 — Statistics, Experimentation & Decision Metrics
Make decisions with correct metrics and avoid common statistical traps.
What you’ll learn
- EDA framework: distributions, correlations, target leakage checklist
- Practical statistics: confidence intervals, hypothesis testing, effect size
- A/B testing basics and pitfalls (power, bias, peeking, seasonality)
- Business metrics vs model metrics: aligning success criteria
Skills you’ll gain
Module 3 — Supervised Machine Learning (Production Mindset)
Train strong baselines, optimize, and explain models reliably.
4 weeks
Module 3 — Supervised Machine Learning (Production Mindset)
Train strong baselines, optimize, and explain models reliably.
What you’ll learn
- Regression & classification pipelines (preprocessing, CV, baselines)
- Evaluation beyond accuracy: ROC-AUC, PR-AUC, calibration, thresholding
- Error analysis: slices, segments, stability checks
- Feature engineering: categorical, time-based, aggregation features
- Explainability basics: SHAP intuition + communicating model behavior
Skills you’ll gain
Module 4 — Deep Learning Essentials
Understand neural networks enough to use them correctly in real projects.
2–3 weeks
Module 4 — Deep Learning Essentials
Understand neural networks enough to use them correctly in real projects.
What you’ll learn
- Neural network fundamentals: forward/backprop intuition
- Training strategy: learning rate, batch size, regularization, early stopping
- Overfitting control and practical troubleshooting
- When to use deep learning vs classical ML (cost/benefit)
Skills you’ll gain
Module 5 — GenAI in Production (RAG + Evaluation + Guardrails)
Build ‘chat-with-your-data’ systems with evaluation and reliability.
2–3 weeks
Module 5 — GenAI in Production (RAG + Evaluation + Guardrails)
Build ‘chat-with-your-data’ systems with evaluation and reliability.
What you’ll learn
- Prompting patterns and structured outputs (schemas, constraints)
- Embeddings + vector search fundamentals (RAG architecture)
- Chunking, retrieval strategy, citations, freshness considerations
- Evaluation basics: groundedness, hallucinations, test sets, regression tests
- Safety basics: guardrails mindset, policy constraints, failure modes
Skills you’ll gain
Module 6 — Deployment & MLOps Basics
Ship models as real services: versioning, CI/CD basics, monitoring signals.
4–5 weeks
Module 6 — Deployment & MLOps Basics
Ship models as real services: versioning, CI/CD basics, monitoring signals.
What you’ll learn
- Serving: FastAPI inference endpoints, input validation, payload schemas
- Docker basics for ML services (images, env configs, reproducibility)
- MLflow patterns: tracking runs, artifacts, model versioning basics
- CI/CD fundamentals: lint/tests, basic pipeline concepts, release discipline
- Monitoring signals: performance degradation, drift awareness, feedback loop
Skills you’ll gain
Capstone — End-to-End AI Product
Deliver a portfolio-grade product with demo, docs, and deployment.
3–4 weeks
Capstone — End-to-End AI Product
Deliver a portfolio-grade product with demo, docs, and deployment.
What you’ll learn
- Problem framing: objectives, constraints, success metrics
- Data pipeline + modeling + evaluation + error analysis
- Deploy an API + containerize + basic release workflow
- Documentation, demo storytelling, and portfolio packaging
Skills you’ll gain