Programming for Business Analytics
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|
This module is closed to MSc Business Analytics only
|Duration of module:||
Duration (weeks) - term 1: |
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. P1: Demonstrate knowledge and understanding of fundamental, and domain-specific, analytics methods and tools.
- 2. P5: 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. P6: Critically analyse the use of data within a business context, identifying strengths and limitations.
- 4. P7: Critically analyse and interpret relevant academic, technical and industry literature.
ILO: Personal and key skills
- 5. P14: 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|
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|
|Guided Independent Study||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|
|Form of assessment||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|n class quizzes and multiple choice exercises||In class||1, 2, 3, 4, 5||Oral - in class|
Summative assessment (% of credit)
|Coursework||Written exams||Practical exams|
Details of summative assessment
|Form of assessment||% of credit||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|Coursework 1||20||1hr coursework||1, 2, 3, 4, 5||Written|
|Coursework 2||20||1hr coursework||1, 2, 3, 4, 5||Written|
|Practical Exam||60||2 hr lab based, open-book practical exam||1, 2, 5||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|
|Coursework 1 (20%)||Coursework (20%)||1, 2, 3, 4 , 5||Summer reassessment period|
|Coursework 2 (20%)||Coursework (20%)||1, 2, 3, 4 , 5||Summer reassessment period|
|Practical exam (60%)||Practical exam (60%)||1, 2, 5||Summer reassessment period|
Re-assessment will be in nature to the original assessment, but the topic, data, and materials must be new.
Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a reassessment 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 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%.
The following content will be covered during the course.
- Getting started with Python and R
- Introduction to solving problems using programs
- 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:
Downey, A. (2012). Think Python: How to think like a computer scientist. Needham, Mass.: Green Tea Press.
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:
Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. Sebastopol, Calif.: O'Reilly.
Chen, D.Y. (2017). Pandas for everyone: Python data analysis. Boston: Addison-Wesley.
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?
Last revision date