UCL School of Management

Module Fact Sheet

MSIN0096: Mathematical Foundations of Business Analytics

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

Course overview

This module serves as a foundation course for other more advanced business analytics modules. 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 and statistical concepts that underpin business analytics.
  • To expose students to the range of statistical, econometric and machine learning techniques used to analyse large, complex data sets, such as probabilistic methods, Bayesian analysis, discrete choice model, panel data analysis, supervised learning and unsupervised learning.
  • To provide students with the programming ability of implementing these techniques in R. 
  • 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.
  • 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

(4 X 10%) Coursework Assignments; 60% unseen 3-hour examination

Past versions of this module

MSIN0096 18/19

MSING054 17/18

MSING054 16/17

Last updated Friday, 12 July 2019