Overview

This module provides a foundation in data science through the data lifecycle and the CRISP-DM process: data collection, cleaning, transformation, exploratory data analysis (EDA), data governance & quality, and reproducibility using notebooks, SQL/Python, and version control. It emphasizes ethics and data privacy, project lifecycle (from raw data to decision-making), data standards, and reproducible outcomes. Learners are introduced to data thinking, EDA processes, and core tools.

Unit Aims

• To understand the data project lifecycle (CRISP-DM) and the role of each stage.
• To develop basic skills in data collection, cleaning, transformation, and management.
• To apply exploratory data analysis (EDA) techniques to uncover patterns, anomalies, and insights.
• To use notebooks, SQL/Python, and version control tools to ensure reproducible results.
• To identify ethical considerations and apply principles for responsible data usage.

• View details on Learning Outcomes, Topics, and Suggested Readings HERE.

Overview

Provides a foundational understanding of probability and statistics for drawing inferences from data, including estimation, hypothesis testing, regression, and experimental/AB test design.

Unit Aims

• To apply random variables and key probability distributions (discrete/continuous).
• To perform sampling, estimation, confidence intervals, and hypothesis testing.
• To analyze correlation, linear regression, and parametric inference.
• To design and evaluate AB tests; interpret p-values, statistical power, and Type I/II errors.
• To present statistical results accurately and avoid common fallacies.

• View details on Learning Outcomes, Topics, and Suggested Readings HERE.

Overview

Focuses on advanced prediction techniques, including feature engineering, regularization, ensemble models, hyperparameter tuning, time series forecasting, and probability calibration.

Unit Aims

• To build an end-to-end prediction pipeline aligned with clear KPIs.
• To apply L1/L2/Elastic Net regularization, SVM, decision trees, and ensemble methods (RF, GBM).
• To perform hyperparameter tuning using cross-validation, grid/Bayesian search, and handle data imbalance.
• To evaluate models using ROC-AUC, PR-AUC, RMSE, MAPE, and probability calibration.
• To conduct time series forecasting with appropriate backtesting methods.

• View details on Learning Outcomes, Topics, and Suggested Readings HERE.

Overview

Covers advanced research methods for the fields of computing and data, including research design, literature review, ethics, experimental/survey methods, and academic writing.

Unit Aims

• To formulate research problems, questions, and testable hypotheses.
• To conduct systematic literature reviews; cite and manage references effectively.
• To select appropriate quantitative, qualitative, or mixed-method research designs aligned with research objectives.
• To design experiments, choose sampling methods, measure variables, and collect and analyze data.
• To write research proposals and academic reports/papers, ensuring ethical standards, reproducibility, and proper data management.

• View details on Learning Outcomes, Topics, and Suggested Readings HERE.

The Capstone Project is a comprehensive, final assignment that students undertake at the end of their program. It is designed to integrate the knowledge and skills they have developed throughout their studies. Unlike traditional exams, the Capstone Project requires students to apply their learning to solve real-world problems or challenges in the field of artificial intelligence.

Key Features of a Capstone Project:

• Practical Application: The project focuses on solving real-world challenges using AI technologies, such as machine learning models, intelligent systems, or AI-driven solutions for industry problems.
• Integration of Knowledge: Students must synthesize concepts from multiple courses, demonstrating their ability to develop and implement AI solutions effectively in a professional context.
• Research Component: The project involves conducting substantial research, gathering and analyzing data, and presenting evidence-based conclusions and innovative recommendations.
• Submit and/or Present: Upon completion, students are typically required to submit and/or present the Capstone Project.

Benefits of a Capstone Project:

• Real-World Experience: Students gain hands-on experience by addressing actual AI challenges, from automation to data-driven problem-solving.
• Skill Development: The project enhances critical thinking, coding, problem-solving, research, and communication skills essential for AI professionals.
• Portfolio Piece: The completed project serves as a showcase of students’ capabilities, strengthening their resumes for job interviews and career opportunities.
• Networking: Projects often involve collaboration with industry experts, providing valuable opportunities to build connections within the AI and tech sectors.

In essence, the Capstone Project is the culmination of the program, allowing students to showcase their expertise, creativity, and readiness to tackle complex AI challenges in professional environments.