UCL School of Management

Module Fact Sheet

MSIN0096: Mathematical Foundations of Business Analytics

Taught by
Level
Masters, level 7
Prerequisites
None
Eligibility
MSc Business Analytics (MS)
Terms
1
Delivery method
10 x 3-hour teaching blocks
Assessment
(2 X 20%) Coursework; 60% unseen 3-hour examination
Previous Module Code
MSING0054

Course overview

The context for the Mathematical Foundations of Business Analytics module is management in complex, interconnected, data-driven environments.

The benefits that can be achieved through the appropriate analysis of data are significant. But companies face significant challenges dealing with large, complex data sets that are difficult to process and learn from. And it is all too easy to misunderstand the data and its structure or to apply inappropriate analytical techniques, which consequently draw flawed conclusions.

Managers need to understand the variety of mathematical and statistical techniques and complex analytics that can extract value from complicated, multifaceted data. And know when and how these techniques can be applied.

Businesses are increasingly using application specific platforms and tools where the analytics are embedded into the software. However, understanding the underlying mathematical and statistical techniques is important to appreciate the limitations of such tools.

This module introduces students to the range of mathematical and statistical techniques that underpin business analytics and develops their understanding of the challenges of handling complex data through the study of selected techniques.

The aims of the Mathematical Foundations of Business Analytics module are:

  • To provide students with an understanding of some of the key mathematical concepts that underpin business analytics.
  • To expose students to the range of mathematical and statistical techniques used to analyse large, complex data sets, such as probabilistic methods,  Bayesian analysis, econometric models, supervised learning and unsupervised learning.
  • To introduce students to the principles of scientific experimentation, including hypothesis construction, experimental testing and design, and population selection and sampling, needed to evaluate the validity of data analyses.
  • To provide students with an understanding of selected mathematical and statistical techniques and how they are used in practice.
  • To ensure that students have the necessary mathematical and statistical skills to be able to make effective use of the latest business analytics platforms and tools.

Learning outcomes

Upon successful completion of the module, students will be able to:

  • Understand the characteristics of data that influence the selection of appropriate mathematical and statistical techniques.
  • Understand key mathematical concepts that underpin business analytics.
  • Develop core mathematical and statistical “literacy” skills needed to support data-driven decision-making.
  • Appreciate the variety of mathematical and statistical techniques that can extract value from complicated, multifaceted data and when and how these techniques can be applied.
  • Apply selected mathematical and statistical techniques in practice.
  • Communicate the results of selected mathematical and statistical techniques to non-specialist audiences.

Assessment summary

(2 X 20%) Coursework; 60% unseen 3-hour examination

Past versions of this module

MSING054 17/18

MSING054 16/17

Last updated Friday, 17 August 2018