UCL School of Management

Module Fact Sheet

MSIN0143: Programming for Business Analytics

Taught by
Level
Masters, level 7
Prerequisites
None
Eligibility
MSc Business Analytics (MS)
Terms
1
Delivery method
10 x 3-hour teaching block
Assessment
60% individual coursework; 40% group coursework
Previous Module Code
MSING055

Course overview

Computational models and concepts for business analytics need to be implemented with tools and technologies.

Students will learn about computational thinking, experimental methodology and empirical methods for training, validation and testing models. This course will cover the preparation of datasets, and introduce students to simple examples of both supervised and unsupervised methods. The importance of plotting and visualisation for decision support will be emphasised.

Lab sessions will begin with a general introduction to programming in Python using Jupyter Notebooks. We will introduce programming for students and the content will be covered sufficiently fast that we can reach a point where you will be able to apply this knowledge to your research at the end of the course, and we will not try to hide away the niggling details that you will encounter in real programming.

There will also be an opportunity for students to familiaries themselves with other data analytics software packages including Matlab, RStudio and Stata and Tableau.

Learning outcomes

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

  • Understand computational thinking
  • Be able to explore data and programme simple algorithms
  • Evaluate computational approaches to data analytics problems
  • Visualise data for business analytics
  • Assess performance of algorithms

Topics covered

  • Data types and structures
  • Data preparation and exploratory data analysis
  • Experimental methods
  • Introduction to methods for large data sets
  • Implementation of supervised learning algorithms
  • Plotting, visualisation and decision support
  • Implementation of unsupervised learning algorithms
  • Performance, evaluation and overfitting
  • Simple complexity and runtime analysis

Assessment summary

The course has the following assessment components:

  • 60% individual coursework;
  • 40% group coursework

Current students should refer to Moodle for specific details of the current year’s assessment.

Essential reading

Data Science for Business: What you Need to Know about Data Mining and Data Analytic Thinking, T. Fawcett and F. Provost, O’Reilly, 201

Past versions of this module

MSIN0143 18/19

MSING055 17/18

MSING055 16/17

Last updated Friday, 17 August 2018