• University of Exeter Business School
• Module

Programming for Business Analytics

Module description

In this module you will learn fundamental programming skills that enable you to search and sort data. You will be introduced to programming in Python and R, and will learn how to develop and run programmes in Jupyter Notebooks. You will learn key programming principles and will practice applying them to real business problems. These skills will form the basis of your ability to address business problems using data.

Module aims - intentions of the module

This module aims to give a comprehensive introduction to the programming skills that underpin Business Analytics and Data Science.  We will focus primarily on Python, but you will also learn how to use the R software environment. You will learn to:

• Understand the role that programming plays in a Business Analytics context
• Be confident writing, testing, and debugging procedural and functional programmes in Python and R
• Import and process data using Python
• Understand the principles of object-oriented programming for Python

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

• 1. demonstrate knowledge and understanding of fundamental, and domain-specific, analytics methods and tools;
• 2. create, manage, interrogate, interpret and visualise data from a wide range of different sources, types and including structured and unstructured forms.

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

• 3. critically analyse the use of data within a business context, identifying strengths and limitations.

ILO: Personal and key skills

On successfully completing the module you will be able to...

• 4. demonstrate technological and digital literacy: Our graduates are able to use technologies to source, process and communicate information.

Syllabus plan

The following content will be covered during the course.

• Getting started with Python and R
• Introduction to solving problems using programs
• Functions
• Control Structures
• Sequences and iteration
• Data types and structures for Python and R
• Data manipulation using Python
• Developing more complex programmes using Python
• Introduction to Python libraries

Learning activities and teaching methods (given in hours of study time)

Details of learning activities and teaching methods

Formative assessment

Summative assessment (% of credit)

Details of summative assessment

Details of re-assessment (where required by referral or deferral)

Indicative learning resources - Basic reading

The following book is a useful resource for this course.  It is freely available online, and also available in printed format in the university library:

Think Python, Allen B, Downey, O’Reilly, second edition

You may also find the following book useful for learning more about R.  It is freely available online, and also available in printed format in the university library:

R for Data Science, Haley Wickham and Garrett Grolemund, O’Reilly, 2016

There are further useful resources on the Python and R websites.  Further information and resources for the Jupyter Notebook interactive development environment are available on the Jupyter website.

You will find information about how to install Python, R, and Jupyter Notebook on the module ELE pages.  It also contains further information about other IDE’s, code editors and other useful tools for programming.

Key words search

Python, R, Programming, Analytics