Generative AI for Data Analytics module (BU32016)

Develop practical skills in using AI for data analytics. Cover data preparation, statistical modelling, visualisation, and linear regression, with a focus on responsible use in business settings

Credits

Module code

BU32016

Level

Semester

Semester 2

School

School of Business

Discipline

Economics

Data analytics is becoming a core skill in business. This module shows you how AI tools can make that work faster, more reliable, and more insightful.

You'll work through every stage of the analytics process: collecting and cleaning data, exploring statistical distributions, creating visualisations, building regression models, and running predictive and sensitivity analyses. Throughout, you'll use generative AI platforms to support your work, not to replace the need to understand what you're doing.

A key theme is responsible use. You'll develop critical awareness of AI-generated outputs, biases, and data privacy considerations, building the professional judgement needed to use these tools ethically.

The module runs as a mix of lectures and workshops. An individual project and a group project give you the chance to apply everything you've learned to real-world business problems.

What you will learn

In this module, you will:

Understand the core principles of generative AI and its role across the data analytics workflow
Explore statistical data distributions and how AI tools help summarise and interpret them
Use AI-assisted linear regression and predictive analytics to support business decision-making
Apply generative AI to collect, clean, and summarise data in a transparent and ethical way
Create advanced data visualisations and communicate analytical findings clearly

By the end of this module, you will be able to:

Explain the core principles of generative AI and its role across the data analytics workflow in business settings
Describe statistical data distributions and how AI tools assist in interpreting patterns in business data
Demonstrate understanding of linear regression, predictive analytics, and sensitivity analysis
Use generative AI to collect, clean, and summarise datasets in a reproducible and ethical manner
Construct advanced data visualisations by directing AI tools to generate effective graphics for business audiences
Apply AI-assisted modelling to estimate regressions, generate forecasts, and run sensitivity analyses
Communicate analytical findings clearly, using AI to support reporting and visual storytelling
Critically evaluate AI-generated outputs, with awareness of bias, data privacy, and professional integrity