MS1S15 - Principles of Data Science 01 Sep 2021 - 31 Aug 2027 | Version 1
Associated Module Information
| Module Code: | MS1S15 | ||
|---|---|---|---|
| Module Title: | Principles of Data Science | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Computing and Mathematical Sciences | ||
| Faculty Sub Group: | Mathematical Sciences | ||
| Module Leader: | Samuel Jobbins, Ian Fitzell | ||
| Module Team: | Ieuan Griffiths, Angelica Pachon, Stephanie Perkins | ||
| First Intended Intake: | SEP 2021 | Final Year of Intake: | 2024 |
| Date Closed: | |||
| Credit Value: | 20 | Credit Level: | 4 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | 100403 - mathematics | ||
| HECOS Code Weighting: | 100 | ||
Document Version Information
| Version | 1 |
|---|---|
| Valid From | 01 Sep 2021 |
| Valid To | 31 Aug 2027 |
Module Aims
To provide students with an understanding of basic data science techniques.
To provide students with an understanding of using software for data analysis and visualization.
Content Summary
Introduction to statistical analysis software:
Fundamentals of statistical programming, working with integrated development environments.
Data wrangling:
Loading data from different sources, data cleaning, feature selection and engineering.
Data visualisation:
Boxplots, histograms, bar charts, scatter plots; producing plots using statistical software.
Exploratory data analysis:
Exploring the structure of data, exploring numerical variables, exploring categorical variables, exploring relationships between variables. Producing numerical summaries using statistical software. Finding patterns using association rules. Finding groups of data using k-means.
Regression:
Linear and Logistic regression. Multinomial regression. Estimating regression coefficients using statistical software.
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Lecture | 10 |
| Practical classes and workshops | 10 |
| Supervised time in studio/workshop | 6 |
| Work based learning | 74 |
| Directed Study | 28 |
| Formative Assessment - Independent | 72 |
| Total Hours Selected | 200 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | Understanding the principles of data science techniques. |
| LO2 | Using software for data analysis and visualisation in practical applications. |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Asynchronous Assessment | Portfolio 1 | Portfolio of exercises | 0 | 4000 | 100 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | ||
| Portfolio 1 | ✔ | ✔ | |