Module manager: Dr Jenny Sexton
Email: j.l.sexton@leeds.ac.uk
Taught: 1 Mar to 30 Apr, 1 Mar to 30 Apr (2mth)(adv yr), 1 Nov to 31 Dec, 1 Sep to 31 Oct (adv yr) View Timetable
Year running 2026/27
Students are required to meet the programme entry requirements prior to studying the module.
N/A
This module is not approved as an Elective
This module empowers professionals from non-STEM backgrounds to confidently engage with data science in their work. Designed specifically for those who are new to quantitative methods, the module takes an intuitive, accessible approach to the technical language commonly used for data science and analytics. Rather than diving into creating complex mathematics, the module focuses on interpretation—teaching participants how to confidently read, run, and extract insights from existing dashboards and reports, written in R and Python.
The objective of this module is to equip participants with the essential mathematical, statistical and programming concepts needed to enable them to participate meaningfully in discussions about data analytics projects and understand technical data science reports.
The module takes an intuitive, non-intimidating approach and introduces statistical concepts, machine learning basics and data visualization using real-world examples and practical applications relevant to a range of fields.
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
1. Explain fundamental statistical concepts used to analyse data.
2. Demonstrate how uncertainty and variability can affect observed data through simulation techniques.
3. Apply fundamental statistical concepts to interpret real-world data scenarios.
On successful completion of the module students will have demonstrated the following skills learning outcomes:
1. Load, execute, edit and interpret existing dashboards, and reports to extract relevant insights and generate reports for decision-making purposes. (Work Ready, Digital)
2. Take a structured and logical approach to solving statistical problems with data. (Digital, Work Ready)
3. Summarise the content of data science reports and dashboards clearly and concisely using both non-technical and academic language. (Work Ready, Academic)
Indicative content for this module includes:
Students will run, and interpret reports, dashboards and applications written in R and Python. Through these practical examples, students will develop familiarity with the following concepts as they appear in data science outputs:
- Basic probability models and their applications
- Simulation approaches for understanding variability and uncertainty
- Common statistical methods (such as method of moments, maximum likelihood)
- Foundational concepts in linear algebra and Markov processes
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Discussion forum | 6 | 1 | 6 |
| WEBINAR | 6 | 1 | 6 |
| Independent online learning hours | 42 | ||
| Private study hours | 96 | ||
| Total Contact hours | 12 | ||
| Total hours (100hr per 10 credits) | 150 | ||
For the 20% coursework assessment: There will be practice activities in a similar format in week 2 and 3. Formative feedback for these activities will be provided in the webinars and on the discussion forum prior to the assessment.
For the 80% coursework assessment: Progress towards assessment 2 will be scaffolded throughout the taught units of this module including self-assessment checkpoints. Students will have weekly formative activities for each taught unit of this module that directly relate to components of the coursework task. Feedback on these will be provided in the webinars and on the discussion forum.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | Students will produce an audio-narration (5 minutes) analysing a video recording of a data analytics task. | 20 |
| Coursework | Students will produce a report (1,500 words) on one of the case studies examined in this module. The report will take the form of a user-guide or tutorial containing at least one worked example. | 80 |
| Total percentage (Assessment Coursework) | 100 | |
This module will be reassessed by a 100% individual assessment covering all learning outcomes.
Check the module area in Minerva for your reading list
Last updated: 22/07/2026
Errors, omissions, failed links etc should be notified to the Catalogue Team