Program Overview

Business Analytics (MSIN0017)

Key Information

• Faculty: Faculty of Engineering Sciences
• Teaching department: UCL School of Management
• Credit value: 15
• Restrictions: MSIN0017 is only available to 2nd year BSc Information Management for Business students.

Alternative Credit Options

There are no alternative credit options available for this module.

Description

Most challenges that companies/organisations face today are too complex to rely on just intuition to resolve. The recent explosion of digital data provides increasingly more opportunities for organisations to make data-driven decisions. Business analytics is the intersection of business, statistics, and technology, offering new opportunities for a competitive advantage. It unlocks the predictive potential of data analysis to improve operational efficiency, strategic management, and financial performance. It is vital in preparing organisations to solve 21st-century business challenges, and participants of this module will have exposure to powerful and innovative concepts, methodologies, and tools that support decision making.

Specifically, this 10-week module aims to foster analytical and statistical thinking in business and management so that students are able to make informed decisions under uncertainty in real business settings. Students are trained to understand the need for data, the importance of data production, the omnipresence of variability, and the quantification and explanation of variability.

This course is intended for students with minimal quantitative preparations. It is designed mainly to be applied and practical. Hands-on experience using Excel is emphasized throughout the module.

Module Deliveries for 2026/27 Academic Year

• Intended teaching term: Term 1
• Undergraduate (FHEQ Level 5)

Teaching and Assessment

• Mode of study: In person
• Methods of assessment:
  • 80% Exam
  • 20% Coursework
• Mark scheme: Numeric Marks

Other Information

• Number of students on module in previous year: 120
• Module leader: Dr Andy Huang

Last Updated

This module description was last updated on 10th March 2026.