CMT314: Data Science Foundations
| School | Cardiff School of Computer Science and Informatics |
| Department Code | COMSC |
| Module Code | CMT314 |
| External Subject Code | 100366 |
| Number of Credits | 10 |
| Level | L7 |
| Language of Delivery | English |
| Module Leader | Dr Oktay Karakus |
| Semester | Autumn Semester |
| Academic Year | 2025/6 |
Outline Description of Module
This module will introduce core data science concepts, including understanding of the different types of data sources available (administrative data, survey data, open data, big data, etc.); how to collect data, including innovative data collection methods, e.g. web scraping; understanding the challenges with unstructured data; how to treat different data types; how to undertake basic data analysis (structured and unstructured data); and how to present data through basic data visualisations.
On completion of the module a student should be able to
-
Use the Python programming language to complete a range of programming tasks
-
Critically analyse and discuss methods of data collection
-
Extract textual and numeric data from a range of sources, including online
-
Reflect upon the legal, ethical and social issues relating to data science and its applications
How the module will be delivered
Modules will be delivered through mainly in-person sessions, supportive online sources and reading materials.
You will be guided through learning activities appropriate to your module, which may include:
-
Through in-person lectures, you will be taught theoretical aspects of each week’s topic with visually promoted examples. You will be involved the discussions via questions and interactive polls (Mentimeter).
-
Practical hours will serve as the gaining experience step of the things covered during lectures. You will be given some example scenarios to develop a Python code to solve a generic problem at the early stages of the module. When it gets closer to the end of semester, case studies will be focused on primary data science aspects.
-
Self-paced online resources, including videos, web materials, and e-books–> Weekly lectures and practical sessions will be supported by sources from web/cloud for you to improve your experience more in cases when you could not get enough during the practical sessions.
Skills that will be practised and developed
Fundamental programming in Python
Reading and Writing common data formats
Data analysis using appropriate libraries
How the module will be assessed
An assessment type which includes coursework and portfolio contents.
Students will be provided with reassessment opportunities in line with University regulations.
Assessment Breakdown
| Type | % | Title | Duration(hrs) |
|---|---|---|---|
| Practical-Based Assessment | 100 | Evaluation Of Data Analysis Process | N/A |
Syllabus content
Computational & algorithmic thinking and developing basic algorithmic steps for coding.
Basic programming in Python: Fundamental data types, program control structures, basic language features.
Data extraction and importing; analysis using common libraries (e.g. pandas, numpy, scipy)
Data Visualisation (e.g. matplotlib, plotly)
Natural language processing using common libraries (e.g regex, nltk)
Retrieving data from online sources (web scraping, APIs)
Data Science applications
Legal issues relating to Data Science (GDPR)
Social and Ethical issues relating to Data Science