Postgraduate Course: Programming and Data-Driven Storytelling (CMSE11691)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course is an introduction to programming and algorithmic thinking, effective data visualisation, and the spinning of empirical narratives. The covered material enables students to build their own programs, critically reflect on requirements and problem translation, make decisions to tailor presentations to an audience, and be aware of misinformation tactics in data communication. |
| Course description |
The ability to write programs and derive answers to specific problems in doing so is a key skill for many aspects of data and decision analytics. Similarly, communicating these answers to people effectively in an accurate and accessible manner is equally important for these abilities to have impact. The course, using Python as the teaching language, encompasses an overview of the start-to-end pipeline of working with data and conveying the results to an intended target audience.
Starting with the basics of programming logic, subsequent lectures build on these to equip students with a sound set of fundamentals that allow them to explore more advanced topics. The presentation of findings is then addressed from a multidisciplinary point of view, drawing on different fields to enable decision-making about suitable data visualisations, presentation design, and narrative elements. As an introductory course, prior knowledge in these topics is not required.
Outline content
- History and relevance of programming and empirical storytelling
- Control structures, linear data structures, and matrix operations
- Functions, classes, and data analysis using a programming language
- Data pre-processing, basic and advanced data visualisation types
- Objective-driven decision-making and visual data misrepresentation
- Narrative elements, storytelling tools for audiences, and personas
Student learning experience
Teaching takes the form of synchronous online lectures. As with languages in general, practice is important for learning a programming language, and this part of the course includes weekly voluntary homework exercises during the first half of the course to apply learned concepts in preparation for an individual coursework assignment. The second half of the course explores empirical storytelling and visualisations, with a co-creation element allowing for student-led examples to be incorporated into the teaching material. The second coursework assignment takes the form of a group project putting the covered material into practice in a business case.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | For MSc Data and Decision Analytics students only. |
Course Delivery Information
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| Academic year 2025/26, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
176 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
30% Presentation (Group) - 20 slides - Assesses course Learning Outcomes 3,4,5
70% Project report (Individual) - 500 words - Assesses course Learning Outcomes 1,2,3 |
| Feedback |
Formative: Feedback will be provided throughout the course.
Summative: Feedback will be provided on the assessments within agreed deadlines. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Use and critically apply a variety of programming concepts in Python.
- Document and communicate code structures and address problems using data.
- Select and apply suitable visualisations for a range of different data types.
- Use storytelling techniques to create effective presentations tailored to an audience.
- Combine the above elements for a data-driven narrative encompassing an analysis.
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Reading List
Matthes, E. (2023), Python Crash Course: A Hands-On, Project-Based Introduction to Programming. San Francisco, CA: No Starch Press, ISBN: 9781098156664
Knaflic, C. N. (2015), Storytelling with Data: A Data Visualization Guide for Business Professionals. Hoboken, New Jersey: Wiley, ISBN: 9781119002260
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Additional Information
| Graduate Attributes and Skills |
Cognitive Skills
After completing this course, students should be able to:
Be self-motivated; curious; show initiative; set, achieve and surpass goals; as well as demonstrating adaptability, capable of handling complexity and ambiguity, with a willingness to learn; as well as being able to demonstrate the use digital and other tools to carry out tasks effectively, productively, and with attention to quality.
Understand how to manage and sustain successful individual and group relationships in order to achieve positive and responsible outcomes, in a range of virtual and face-to-face environments.
Communication, ICT, and Numeracy Skills
After completing this course, students should be able to:
Critically evaluate and present digital and other sources, research methods, data and information; discern their limitations, accuracy, validity, reliability and suitability; and apply responsibly in a wide variety of organisational contexts.
Knowledge and Understanding
After completing this course, students should be able to:
Identify, define and analyse theoretical and applied business and management problems, and develop approaches, informed by an understanding of appropriate quantitative and/or qualitative techniques, to exploreand solve them responsibly. |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Ben Moews
Tel: (01316) 508074
Email: Ben.Moews@ed.ac.uk |
Course secretary | |
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