Postgraduate & Executive Programmes
MSc-Track: Applied Data Science, AI & Business Automation
Machine learning, deep learning, MLOps and AI business automation — 24 weeks, Google/Microsoft/AWS AI certification prep, thesis-style capstone
What you'll learn
check_circleBuild, evaluate and deploy machine learning and deep learning models end-to-end
check_circleApply NLP, LLMs and computer vision to real business problems
check_circleRun MLOps pipelines — CI/CD for models, monitoring and retraining
check_circleSit the Google Data Analytics, AWS ML Specialty and Azure AI Engineer certification exams with structured prep
check_circleDesign and defend an AI-driven business-automation capstone, written to postgraduate thesis standard
Course syllabus
1. Week 1: Mathematics for Data Science — Linear Algebra & Calculus Refresher
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The vectors, matrices and derivatives that every ML algorithm quietly depends on, taught for practitioners not mathematicians.
2. Week 2: Statistics & Probability for Data Science
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Distributions, hypothesis testing and confidence intervals — the statistical toolkit behind every real analysis.
3. Week 3: Advanced Python for Data Science
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NumPy, Pandas and SciPy at a level that lets you clean and manipulate real, messy datasets fast.
4. Week 4: Data Engineering — ETL Pipelines & Data Warehousing
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Building the pipelines that move and transform data before any analysis or model ever sees it.
5. Week 5: SQL & NoSQL for Analytics at Scale
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Advanced querying, window functions and when a document/NoSQL store beats a relational one.
6. Week 6: Exploratory Data Analysis & Statistical Inference
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Turning a raw dataset into a defensible set of findings — the step most courses skip.
7. Week 7: Data Visualization & Storytelling
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Power BI, Tableau and Plotly, and the difference between a chart and a chart that changes a decision.
8. Week 8: Google Data Analytics Professional Certificate — Exam Preparation
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Structured review aligned to Google's own Data Analytics certification curriculum.
9. Week 9: Machine Learning Foundations
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Supervised and unsupervised learning — regression, classification, clustering — built from first principles.
10. Week 10: Deep Learning & Neural Networks
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Neural network fundamentals with TensorFlow and PyTorch, from a single perceptron to a trained model.
11. Week 11: Natural Language Processing & Large Language Models
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Text processing, embeddings and working with modern LLMs for real NLP tasks.
12. Week 12: Computer Vision & Generative AI
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Image classification, object detection, and the generative models reshaping creative and business work.
13. Week 13: MLOps — Model Deployment, Monitoring & CI/CD for ML
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Getting a model out of a notebook and into production, with monitoring for when it starts to drift.
14. Week 14: AWS Certified Machine Learning – Specialty — Exam Preparation
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Exam-mapped review of ML services and workflows on AWS.
15. Week 15: Microsoft Certified: Azure AI Engineer Associate — Exam Preparation
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Exam-mapped review of Azure's AI and cognitive services stack.
16. Week 16: Business Intelligence & Automation with AI Agents
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Wiring AI agents and BI tooling into real business workflows, not just dashboards.
17. Week 17: Robotic Process Automation & Workflow Automation
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Automating repetitive business processes end-to-end with RPA tooling.
18. Week 18: AI Ethics, Governance & Responsible AI
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Bias, fairness, explainability and the governance questions every deployed model raises.
19. Week 19: Big Data Technologies — Spark & Distributed Computing
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Processing data at a scale a single machine can no longer handle.
20. Week 20: A/B Testing & Experimentation for Business Decisions
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Designing and reading experiments well enough to trust the decision they justify.
21. Week 21: Research Methods & Quantitative Analysis
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Framing a research question and analysing it the way a postgraduate thesis committee expects.
22. Week 22: Applied Business Analytics Consulting Project
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A live, mentored consulting-style project solving a real organisation's data problem.
23. Week 23: Thesis-Style Capstone — Data-Driven Business Automation System
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Designing and building the full automation system that becomes your capstone submission.
24. Week 24: Capstone Defence, Portfolio & Career Placement
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Defend the capstone live, finalise your portfolio, and move into remote/hybrid job-placement support.
Requirements
- Completion of a Diploma-level Data Analysis track (ours or equivalent) or working comfort with Excel/SQL and basic statistics
- Basic Python — variables, loops, functions
- A laptop able to run Python/Jupyter and, ideally, a free-tier cloud account
About this course
The deepest data programme we run — built like a taught MSc, not a bootcamp.
You will go from statistics and applied Python through machine learning, deep learning, NLP and computer vision, then into MLOps — actually shipping and monitoring models in production, not just training them in a notebook.
Certification prep is built into the syllabus for the Google Data Analytics Professional Certificate, the AWS Certified Machine Learning – Specialty, and Microsoft Certified: Azure AI Engineer Associate. The last four weeks are a thesis-style capstone: an applied business-automation system, written up and defended in front of an academic-style panel.
₦450,000
person Dr. Ifeoma Chukwu
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Live Zoom classes · recordings to rewatch · certificate on completion
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