What you'll learn

Build, evaluate and deploy machine learning and deep learning models end-to-end

Apply NLP, LLMs and computer vision to real business problems

Run MLOps pipelines — CI/CD for models, monitoring and retraining

Sit the Google Data Analytics, AWS ML Specialty and Azure AI Engineer certification exams with structured prep

Design 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 5 lessons

The vectors, matrices and derivatives that every ML algorithm quietly depends on, taught for practitioners not mathematicians.

Vectors, Matrices & Matrix Operations for ML 65m Free preview

Eigenvalues, Eigenvectors & Dimensionality Intuition 65m Free preview

Derivatives, Gradients & the Chain Rule 65m

Optimisation Basics — Gradient Descent by Hand 65m

Lab: Implementing Gradient Descent from Scratch in Python 70m

2. Week 2: Statistics & Probability for Data Science 5 lessons

Distributions, hypothesis testing and confidence intervals — the statistical toolkit behind every real analysis.

Probability Foundations & Common Distributions 65m

Descriptive Statistics & the Central Limit Theorem 65m

Hypothesis Testing — p-values, t-tests & Chi-Square 65m

Confidence Intervals & Statistical Power 65m

Lab: Running and Interpreting a Real Hypothesis Test 70m

3. Week 3: Advanced Python for Data Science 5 lessons

NumPy, Pandas and SciPy at a level that lets you clean and manipulate real, messy datasets fast.

NumPy — Vectorised Operations & Broadcasting 60m

Pandas — Cleaning & Reshaping Messy Real-World Data 60m

SciPy for Statistical & Scientific Computing 60m

Writing Performant, Production-Grade Data Code 60m

Lab: Cleaning a Real Messy Dataset End-to-End 70m

4. Week 4: Data Engineering — ETL Pipelines & Data Warehousing 5 lessons

Building the pipelines that move and transform data before any analysis or model ever sees it.

ETL vs ELT — Designing a Data Pipeline 60m

Data Warehousing Concepts — Star Schemas & Fact/Dimension Tables 60m

Orchestrating Pipelines with Airflow 60m

Data Quality, Validation & Pipeline Monitoring 60m

Lab: Building an ETL Pipeline into a Warehouse 65m

5. Week 5: SQL & NoSQL for Analytics at Scale 5 lessons

Advanced querying, window functions and when a document/NoSQL store beats a relational one.

Advanced SQL — Window Functions & CTEs 60m

Query Optimisation & Indexing for Analytics Workloads 60m

NoSQL Data Models — Document, Key-Value & Column-Family Stores 60m

When NoSQL Beats Relational — Real Trade-offs 50m

Lab: Solving the Same Analytics Problem in SQL and MongoDB 65m

6. Week 6: Exploratory Data Analysis & Statistical Inference 5 lessons

Turning a raw dataset into a defensible set of findings — the step most courses skip.

EDA Workflow — From Raw Data to First Findings 60m

Detecting Outliers, Missing Data & Data Quality Issues 60m

Correlation, Causation & Common Statistical Traps 60m

Feature Engineering for Analysis and Modelling 60m

Lab: Full EDA Report on a Real Dataset 70m

7. Week 7: Data Visualization & Storytelling 5 lessons

Power BI, Tableau and Plotly, and the difference between a chart and a chart that changes a decision.

Chart Selection — Matching the Visual to the Question 50m

Building Dashboards in Power BI 60m

Building Dashboards in Tableau 60m

Interactive Visualization with Plotly 60m

Lab: Telling a Decision-Changing Data Story 60m

8. Week 8: Google Data Analytics Professional Certificate — Exam Preparation 5 lessons

Structured review aligned to Google's own Data Analytics certification curriculum.

The Analysis Lifecycle — Ask, Prepare, Process, Analyse, Share, Act 50m

Spreadsheets & SQL for the Google Analytics Track 60m

R Programming Basics for Data Analysis 60m

Tableau for the Case-Study Presentation 50m

Google Data Analytics Capstone Case Study & Review 55m

9. Week 9: Machine Learning Foundations 5 lessons

Supervised and unsupervised learning — regression, classification, clustering — built from first principles.

Supervised Learning — Regression from First Principles 65m

Supervised Learning — Classification & Decision Boundaries 60m

Unsupervised Learning — Clustering & Dimensionality Reduction 60m

Model Evaluation — Bias/Variance, Cross-Validation & Metrics 65m

Lab: Building and Evaluating Your First ML Pipeline 65m

10. Week 10: Deep Learning & Neural Networks 5 lessons

Neural network fundamentals with TensorFlow and PyTorch, from a single perceptron to a trained model.

From a Single Perceptron to a Multi-Layer Network 65m

Backpropagation & Training Neural Networks 65m

Building Models with TensorFlow and Keras 60m

Building Models with PyTorch 60m

Lab: Training an Image Classifier from Scratch 65m

11. Week 11: Natural Language Processing & Large Language Models 5 lessons

Text processing, embeddings and working with modern LLMs for real NLP tasks.

Text Preprocessing, Tokenization & Embeddings 60m

From Word2Vec to Transformers — How Modern NLP Works 60m

Working with Large Language Models & Prompt Engineering 60m

Fine-Tuning & Retrieval-Augmented Generation (RAG) 60m

Lab: Building an NLP Application on a Real Text Dataset 65m

12. Week 12: Computer Vision & Generative AI 5 lessons

Image classification, object detection, and the generative models reshaping creative and business work.

Image Classification with Convolutional Neural Networks 65m

Object Detection & Image Segmentation 60m

Generative Models — GANs & Diffusion Models 60m

Practical Generative AI for Business Use Cases 50m

Lab: Building an Image Classification or Generation Demo 65m

13. Week 13: MLOps — Model Deployment, Monitoring & CI/CD for ML 5 lessons

Getting a model out of a notebook and into production, with monitoring for when it starts to drift.

Getting a Model Out of the Notebook — Packaging & Serving 60m

Model Deployment Patterns — Batch, Real-Time & Edge 60m

Monitoring for Model & Data Drift 60m

CI/CD for Machine Learning Pipelines 60m

Lab: Deploying a Model with a Monitoring Dashboard 65m

14. Week 14: AWS Certified Machine Learning – Specialty — Exam Preparation 5 lessons

Exam-mapped review of ML services and workflows on AWS.

Domain Review: Data Engineering for Machine Learning on AWS 60m

Domain Review: Exploratory Data Analysis on AWS 60m

Domain Review: Modelling — Algorithm Selection, Training & Tuning 60m

Domain Review: ML Implementation & Operations on SageMaker 60m

Full-Length AWS ML Specialty Practice Exam 55m

15. Week 15: Microsoft Certified: Azure AI Engineer Associate — Exam Preparation 5 lessons

Exam-mapped review of Azure's AI and cognitive services stack.

Planning & Managing an Azure AI Solution 60m

Azure AI — Computer Vision Services 60m

Azure AI — Natural Language Processing Services 60m

Knowledge Mining & Document Intelligence on Azure 60m

Generative AI Solutions with Azure OpenAI Service & AI-102 Review 65m

16. Week 16: Business Intelligence & Automation with AI Agents 5 lessons

Wiring AI agents and BI tooling into real business workflows, not just dashboards.

From Dashboards to Decisions — BI That Drives Action 50m

Designing AI Agents for Business Workflows 60m

Wiring an AI Agent into a Real Business Process 60m

Guardrails, Evaluation & Trust for Agentic Automation 60m

Lab: Building a BI + AI Agent Workflow End-to-End 65m

17. Week 17: Robotic Process Automation & Workflow Automation 5 lessons

Automating repetitive business processes end-to-end with RPA tooling.

RPA Fundamentals — What to Automate and What Not To 50m

Building Bots with RPA Tooling (UiPath/Power Automate) 60m

Handling Exceptions & Unattended vs Attended Automation 60m

Workflow Automation Beyond RPA — Low-Code Integration Platforms 60m

Lab: Automating a Multi-Step Business Process 65m

18. Week 18: AI Ethics, Governance & Responsible AI 5 lessons

Bias, fairness, explainability and the governance questions every deployed model raises.

Bias & Fairness in Machine Learning Systems 60m

Explainability & Interpretability (SHAP, LIME) 60m

AI Governance Frameworks & Regulation 50m

Responsible AI in Practice — Building an AI Ethics Checklist 50m

Lab: Auditing a Model for Bias and Explainability 65m

19. Week 19: Big Data Technologies — Spark & Distributed Computing 5 lessons

Processing data at a scale a single machine can no longer handle.

Why a Single Machine Stops Being Enough 60m

Apache Spark Architecture — RDDs, DataFrames & Lazy Evaluation 65m

Distributed Data Processing with PySpark 60m

Spark for Machine Learning at Scale (MLlib) 60m

Lab: Processing a Large Dataset with Spark 65m

20. Week 20: A/B Testing & Experimentation for Business Decisions 5 lessons

Designing and reading experiments well enough to trust the decision they justify.

Designing a Valid A/B Test 65m

Sample Size, Power & Common Pitfalls 60m

Reading Results — Statistical vs Practical Significance 60m

Sequential Testing & Multi-Armed Bandits 60m

Lab: Design and Analyse an A/B Test 65m

21. Week 21: Research Methods & Quantitative Analysis 5 lessons

Framing a research question and analysing it the way a postgraduate thesis committee expects.

Framing a Postgraduate Research Question in Analytics 55m

Quantitative Research Design & Methodology 50m

Data Collection & Sampling for Research 60m

Analysing and Presenting Research Findings 60m

Lab: Draft Your Research Proposal 55m

22. Week 22: Applied Business Analytics Consulting Project 5 lessons

A live, mentored consulting-style project solving a real organisation's data problem.

Scoping a Real Organisation's Data Problem 55m

Data Discovery & Stakeholder Interviews 55m

Building the Analysis & Recommendations 60m

Presenting Findings to a Non-Technical Client 50m

Consulting Project Review & Mentor Feedback 55m

23. Week 23: Thesis-Style Capstone — Data-Driven Business Automation System 5 lessons

Designing and building the full automation system that becomes your capstone submission.

Designing the Automation System Architecture 65m

Building the Data & Model Layer 60m

Building the Automation & Integration Layer 60m

Testing, Evaluation & Documentation 60m

Finalising the Capstone Submission 65m

24. Week 24: Capstone Defence, Portfolio & Career Placement 5 lessons

Defend the capstone live, finalise your portfolio, and move into remote/hybrid job-placement support.

Preparing Your Capstone Defence Presentation 55m

Capstone Defence — Live Panel Review 50m

Building a Data Science Portfolio That Gets Interviews 60m

Interview Preparation — Technical & Case Questions 60m

Job Placement Support — Remote & Hybrid Opportunities 60m

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

Dr. Ifeoma Chukwu

Live Zoom classes · recordings to rewatch · certificate on completion

MSc-Track: Applied Data Science, AI & Business Automation

What you'll learn

Course syllabus

Requirements

About this course

Useful Links

Contact

MSc-Track: Applied Data Science, AI & Business Automation

What you'll learn

Course syllabus

Requirements

About this course

Follow us for Updates