MA50291: Programming for data science
[Page last updated: 09 August 2024]
| Academic Year: | 2024/25 |
| Owning Department/School: | Department of Mathematical Sciences |
| Credits: | 12 [equivalent to 24 CATS credits] |
| Notional Study Hours: | 240 |
| Level: | Masters UG & PG (FHEQ level 7) |
| Period: |
- Semester 1
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| Assessment Summary: | EXIC 40%, PRPR 60% |
| Assessment Detail: |
- In-class test (EXIC 40%)
- Practical (PRPR 60%)
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| Supplementary Assessment: |
- Like-for-like reassessment (where allowed by programme regulations)
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| Requisites: |
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| Learning Outcomes: |
After completion of the unit, students will be able to:
- Implement and critically evaluate low-level data science functionality using an appropriate programming language.
- Apply a range of complex computational methodologies, data science tools and machine learning techniques using relevant libraries.
- Critically evaluate programming methods and libraries for data science and machine learning applications.
- Assess and explain factors affecting the complexity, performance and scalability of data science and machine learning applications.
- Apply and evaluate software technologies to manipulate large datasets.
- Understand and employ the principles of sustainable software engineering.
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| Aims: | Introduce numerical programming in an appropriate language, including writing algorithms from scratch and utilising algorithms from standard libraries. Introduce methods and structures to handle, process and analyse large datasets. Introduce frameworks for producing sustainable software.
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| Skills: | - Numerical programming in an appropriate language including the use of associated numeric/scientific libraries (T, F, A)
- Algorithmic data analysis (T, F, A)
- Evaluation of scalability of data analysis software (T, F, A)
- Sustainable software engineering (T, F, A)
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| Content: | Programming for data science: general computing, implementation of fundamental algorithms, use of essential libraries for data science and numerical computing, sustainable software design. Analysis of numerical and performance factors. Data structures and software technologies for scalability
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| Course availability: |
MA50291 is Compulsory on the following courses:
Department of Mathematical Sciences
- TSMA-AFM22 : MSc Data Science and Statistics (Health)
- TSMA-AWM22 : MSc Data Science and Statistics (Health)
- TSMA-AFM25 : MSc Financial Mathematics with Data Science
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Notes: - This unit catalogue is applicable for the 2024/25 academic year only. Students continuing their studies into 2025/26 and beyond should not assume that this unit will be available in future years in the format displayed here for 2024/25.
- Courses and units are subject to change in accordance with normal University procedures.
- Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
- Find out more about these and other important University terms and conditions here.
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