Participants will begin by learning Python syntax, variables, data types, and control flow statements, as well as lists and dictionaries for data organisation. They will then progress to creating functions, and learning practical tools useful in data analytics like list comprehension, error handling, and file handling. They will also be introduced to object-oriented programming to enable them to use libraries effectively. Building on these fundamentals, participants will then delve into using the data analytics libraries Numpy and Pandas for powerful data processing and analysis.

The lesson will also highlight the role of cloud computing services in supporting each stage of the pipeline, for participants to gain an understanding of how data pipelines facilitate scalable and efficient data workflows. Following which, the module provides participants with a comprehensive understanding of relational databases using SQL and cloud-based databases such as Google Cloud's BigQuery. The course begins with foundational concepts of relational databases, including data modelling, normalisation, and schema design.

Participants will begin by learning linear models for regression using the scikit-learn package to analyse data and evaluate model performance. They will then explore logistic regression for classification tasks, understanding its motivation, relationship to linear regression concepts, and performance evaluation. Building on this foundation, participants will then delve into practical data preprocessing strategies, exploratory data analysis, and feature engineering, culminating in the integration of these steps into cohesive analytical pipelines.

Participants will learn this versatile technique that e-commerce firms famously rely on to trawl data from their competitors in search of competitive advantages. As much of this data is presented as unstructured text, gaining a rudimentary grasp of modern text processing techniques is an essential skill in subsequently making sense of the scraped data.

Participants will start by mastering the fundamentals of data visualisation with Matplotlib and Seaborn, learning to create and customize a variety of plots to effectively present data. They will then learn about selecting appropriate visualisations and applying best practices to ensure clear and impactful communication of insights. Building on this foundation, participants will explore the capabilities of the Plotly package to create interactive visualisations for complex datasets, such as time series and geospatial data.

Participants will explore core AI techniques like Machine Learning, Deep Learning, Generative AI (Gen AI) and Reinforcement Learning; experiencing first-hand the power of AI models through engaging case studies and real-world anecdotes. They will also be exposed to the most recent developments in AI through hands-on activities with Large Language Model (LLMs) and learn about their use cases in industry.

It provides a comprehensive introduction to the tools and techniques used in modern data analysis, covering essential topics such as Python programming, SQL for managing relational databases, and cloud-based enterprise databases such as Google BigQuery and AWS Redshift for large-scale data handling. Participants will explore advanced techniques in predictive analytics, textual analysis using Natural Language Processing (NLP), and web scraping.

Participants will work in groups to create a comprehensive project integrating data collection, data preprocessing, predictive modelling, visualisation, and interactive dashboard development. The module culminates in group capstone project presentations, as well as the submission of final reports documenting the project.

This module is part of a sequential programme and is not available on a standalone basis.

The ability to efficiently manage and analyse data is a crucial skill in today’s digital world. This course introduces participants to foundational database concepts, tools, and practices. 

Designed for beginners, it emphasises practical skills such as working with relational databases, spreadsheets, SQL, and NoSQL databases. Participants will learn how to structure, query, and visualise data effectively for analytics and business insights. 

This course equips participants with the foundational knowledge and practical skills necessary to understand and utilise cloud computing. It emphasises cloud infrastructure, deployment models, service types, and best practices, providing a vendor-neutral perspective that applies across cloud platforms.