Programming for Business Analytics

Module title	Programming for Business Analytics
Module code	BEM1025
Academic year	2023/4
Credits	15
Module staff	Dr Mohsen Mosleh (Convenor)

Duration: Term	1	2	3
Duration: Weeks		12

Number students taking module (anticipated)	60

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)

Scheduled Learning and Teaching Activities	Guided independent study	Placement / study abroad
60	90	0

Details of learning activities and teaching methods

Category	Hours of study time	Description
Scheduled Learning and Teaching Activity	12	Scheduled lectures
Scheduled Learning and Teaching Activity	24	Scheduled labs and practical workshops
Scheduled Learning and Teaching Activity	24	Structured sessions and practical exercises via online resources, for example, Datacamp
Guided Independent Study	60	Guided reading and practice of technical skills
Guided Independent Study	30	Completion of coursework assessments

Formative assessment

Form of assessment	Size of the assessment (eg length / duration)	ILOs assessed	Feedback method
In class quizzes and multiple choice exercises	During each class	1- 4	Oral in class

Summative assessment (% of credit)

Coursework	Written exams	Practical exams
0	0	100

Details of summative assessment

Form of assessment	% of credit	Size of the assessment (eg length / duration)	ILOs assessed	Feedback method
Practical exam 1	40	2hr lab based, open-book practical exam	1-4	Written
Practical exam 2	60	2hr lab based, open-book practical exam	1-2	Written

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

Original form of assessment	Form of re-assessment	ILOs re-assessed	Timescale for re-assessment
Practical exam 1 (40%)	Practical exam 1 (2 hr, lab based, 40%)	1-4	August/September Reassessment Period
Practical exam 2 (60%)	Practical exam 2 (2 hr, lab-based, 60%)	1-2	August/September Reassessment Period

Re-assessment notes

Deferral – if you have been deferred for any assessment you will be expected to submit the relevant assessment. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 40%) you will be expected to submit the relevant assessment. The mark given for a re-assessment taken as a result of referral will be capped at 40%.

Indicative learning resources - Basic reading

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

Credit value	15
Module ECTS	7.5
Module pre-requisites	None
Module co-requisites	None
NQF level (module)	4
Available as distance learning?	No
Origin date	06/01/2020
Last revision date	19/01/2021