MS4S08 - Applied Statistics for Data Science 01 Apr 2025 - 31 Aug 2027 | Version 5
Associated Module Information
| Module Code: | MS4S08 | ||
|---|---|---|---|
| Module Title: | Applied Statistics for Data Science | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Computing and Mathematical Sciences | ||
| Faculty Sub Group: | Mathematical Sciences | ||
| Module Leader: | Rebecca Peters, Ieuan Griffiths | ||
| Module Team: | Abigail Peters, Sharan Johnstone | ||
| First Intended Intake: | SEP 2018 | Final Year of Intake: | 2024 |
| Date Closed: | |||
| Credit Value: | 20 | Credit Level: | 7 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | 100403 - mathematics | ||
| HECOS Code Weighting: | 100 | ||
Document Version Information
| Version | 5 |
|---|---|
| Valid From | 01 Apr 2025 |
| Valid To | 31 Aug 2027 |
Module Aims
To provide students with an understanding of the core statistical analysis required for Data Science.
To provide students with the practical knowledge of the statistical techniques used in manipulating and managing complex datasets such that they are able to assess practical situations and interpret real-world applications.
Content Summary
Overview of Applied Statistics: Introduction to the statistics. terminologies and typical application areas of applied statistics.
Principles of data visualisation; data preparation and evaluation; data representation; chart types; data-driven storytelling; visual analytics; dashboard design.
Basics of statistical inference: Confidence limits; null and alternative hypotheses; significance level and power
Correlation and Regression: Bivariate correlation. Multivariate linear regression. Residuals, leverage and Cooks Distance.
Exploratory Data Analysis: initial investigations on data; pattern discovery; anomaly detection; hypothesis testing; summary statistics and graphical representations.
Multivariate techniques: Principal Component Analysis; Factor Analysis; Cluster Analysis.
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Lecture | 24 |
| Practical classes and workshops | 8 |
| Independent Study | 80 |
| Directed Study | 88 |
| Total Hours Selected | 200 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | Learning Outcome 1:To understand the concepts and theory of statistical analysis, and explain the wider context of their value in Data Science. |
| LO2 | Learning Outcome 2:Determine and use statistical techniques to assess practical situations and interpret real-world complex data. |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Synchronous Onsite Oral Assessment | Presentation (Synchronous Onsite) 1 | collect, analyse and interpret a complex data set and present results. Collect, analyse and interpret a complex data set in a group setting, of 3 – 5 students. Providing a presentation of results. | 15 | N/A | 50 | No | 40 |
| Asynchronous Assessment | Practical Coursework 1 (Asynch) | Perform a range of statistical analysis using software. Providing a report of the analysis. | 0 | 2000 | 50 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | ||
| Presentation (Synchronous Onsite) 1 | ✔ | ✔ | |
| Practical Coursework 1 (Asynch) | ✔ | ✔ | |