CMT315: Statistical Programming
| School | Cardiff School of Computer Science and Informatics |
| Department Code | COMSC |
| Module Code | CMT315 |
| 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 will be a practical module, which will consider programming with structured and unstructured data and statistical analysis of this data. Students will learn how to analyse both numeric and textual data using a range of computational programming languages.
On completion of the module a student should be able to
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Use code to extract, store and analyse textual and numeric data
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Carry out data analysis and statistical testing using code
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Critically analyse and discuss methods of data collection, management and storage
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Analyse and visualise textual and numeric data from a range of sources, including online
How the module will be delivered
The module will be delivered through a combination of lectures, supervised lab sessions and tutorials as appropriate. You will be expected to attend all timetabled sessions and engage with online material. You will be guided through learning activities appropriate to your module, which may include:
You will be guided through learning activities appropriate to your module, which may include:
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Through face-to-face lectures, you will be taught theoretical aspects of each week’s topic with visually promoted examples. You will be involved in the discussions via questions and interactive polls.
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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 problems and case studies on big data for various problems.
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Weekly lectures and practical sessions will be supported by resources 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
Data analysis using appropriate libraries
How the module will be assessed
An assessment type which includes coursework and portfolio assessments.
Students will be provided with reassessment opportunities in line with University regulations.
Assessment Breakdown
| Type | % | Title | Duration(hrs) |
|---|---|---|---|
| Practical-Based Assessment | 100 | Statistical Programming & Data Analysis | N/A |
Syllabus content
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)
Retrieving data from online sources (web scraping, APIs)
Descriptive statistics & Hypothesis testing
Regression analysis & prediction (e.g. statsmodels, scikit-learn)
(EXTRA) Estimation Theory and Bayesian Sampling (e.g. pymc3)