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.
Full module specification
| Module title: | Programming for Business Analytics |
|---|---|
| Module code: | BEM1025 |
| Module level: | 1 |
| Academic year: | 2023/4 |
| Module lecturers: |
|
| Module credit: | 15 |
| ECTS value: | 7.5 |
| Pre-requisites: | None |
| Co-requisites: | None |
| Duration of module: |
Duration (weeks) - term 2: 12 |
Module aims
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
ILO: Module-specific skills
- 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
- 3. critically analyse the use of data within a business context, identifying strengths and limitations.
ILO: Personal and key skills
- 4. demonstrate technological and digital literacy: Our graduates are able to use technologies to source, process and communicate information.
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 36 | 114 | 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 |
| 0 | ||||
| 0 | ||||
| 0 | ||||
| 0 |
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 |
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
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.
Module has an active ELE page?
Yes
Origin date
06/01/2020
Last revision date
19/01/2021