Programme Content
Fundamentals of Python*
Fundamentals of Mathematics – Linear Algebra/Probability
Measures and Descriptors of Data, Distributions and Estimation
Exploratory Data Analysis
Hypothesis Testing and Evaluation
Self-Learning Project: Evaluating Channel Effectiveness by Using Hypotheses
Data and Information Systems
Story Telling with Data
Designing Business Dashboards
Self-Learning Project: Visualising Mutual Funds and Stocks
Optimisation Formulations
Gradient and Search-based Optimisation for Machine Learning
Linear, Quadratic, and Nonlinear Programming
Multi-objective Optimisation
Self-Learning Project: Multi-Objective Optimisation in Stock Investments
Regression and Derivatives
Trees and Random Forests
Support Vector Machines
Clustering – Hierarchical K-means Clustering
Dimensionality Reduction: PCA
Self-Learning Project: Predicting Customer Churn
Deep Feedforward Neural Nets
Convolutional Neural Nets
Long Short-Term Memory (LSTM) Networks
Introduction to Transformers and Attention Mechanisms
Explainable AI
Self Learning Project: CNN Model for Land Use
VAEs, GANs and Diffusion Models
Introduction to LLMs
Applications of Generative AI
Self Learning Project: Generative AI
A capstone project in the Data Science and Machine Learning Programme serves as the culmination of theoretical knowledge and practical skills acquired throughout the programme. It typically involves solving a problem using data-driven techniques and advanced algorithms. Students engage in various stages of the project life cycle, including exploratory data analysis, model selection and training, and evaluation. The project offers an opportunity to demonstrate proficiency in programming languages such as Python, statistical analysis, machine learning algorithms and data visualisation techniques. This activity is carried out in groups, and students pick one of the three or four contexts given to them.
