CMT309: Computational Data Science
| School | Cardiff School of Computer Science and Informatics |
| Department Code | COMSC |
| Module Code | CMT309 |
| External Subject Code | 100366 |
| Number of Credits | 20 |
| Level | L7 |
| Language of Delivery | English |
| Module Leader | Dr Oktay Karakus |
| Semester | Double Semester |
| Academic Year | 2024/5 |
Outline Description of Module
This module introduces the foundations of computational data science, covering both theoretical underpinnings and the practical computational applications of core data science knowledge and skills. Students will learn how to extract, store and analyse both numeric and textual data using a range of computational programming languages.
On completion of the module a student should be able to
- Use the Python programming language to complete a range of programming tasks
- Demonstrate familiarity with programming concepts and data structures
- Use code to extract, store and analyse textual and numeric data
- Carry out data analysis and statistical testing using code
- Critically analyse and discuss methods of data collection, management and storage
- 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 blended learning. You will be guided through learning activities appropriate to your module, which may include:
on-line resources that you work through at your own pace (e.g. videos, web resources, e-books, quizzes)
on-line interactive sessions to work with other students and staff (e.g. discussions, live streaming of presentations, live-coding, team meetings)
face to face small group sessions (e.g. help classes, feedback sessions)
Skills that will be practised and developed
Fundamental programming in Python
Reading and Writing common data formats
Data analysis using appropriate libraries
Understanding HTML document structure and the fundamentals of the web (HTTP, APIs, Security and Authentication)
How the module will be assessed
A blend of assessment types which may include coursework and portfolio assessments, class tests, and/or formal examinations
Students will be provided with reassessment opportunities in line with University regulations.
Assessment Breakdown
| Type | % | Title | Duration(hrs) |
|---|---|---|---|
| Written Assessment | 30 | Programming Exercises | N/A |
| Practical-Based Assessment | 70 | Data Science Portfolio | N/A |
Syllabus content
Computational & algorithmic thinking and developing basic algorithmic steps for coding.
Basic programming in Python: Fundamental data types, program control structures, Object Oriented Programming and other basic language features.
Data extraction and importing; analysis using common libraries (e.g. pandas, numpy, scipy)
Data Visualisation (e.g. matplotlib, seaborn, plotly)
Natural language processing using common libraries (e.g regex, nltk)
Testing and documentation
Data Science applications
Legal issues relating to Data Science (GDPR)
Social and Ethical issues relating to Data Science
Descriptive statistics
Hypothesis testing
Regression analysis & prediction
Estimation Theory & Bayesian Sampling
Retrieving data from online sources (web scraping, APIs)