| Semester 1 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
N/A
N/A
This module aims to introduce the fundamental computing concepts and techniques underpinning contemporary data science. The module aims to provide students with a grounding in program design and implementation, programming environments. Furthermore, it explores how to apply and devise algorithms for a particular problem.
This module places an emphasis on clear design and development of programs, teaching how to break problems down to provide simpler and easier-to-use solutions. Students will apply these skills at a practical level with a particular programming language, though the skills learned here can be applied to any programming language.
- What is programming?
- The building blocks and structure of computer programs.
- Tackling data analysis problems.
- Algorithms and some examples.
- Introduction to a programming language, and relevant libraries, for data analysis.
- Methods and data structures for data analysis.
- Case studies in software development within a data science context.
At the end of the module students will have:
- A knowledge of what is meant by programming a computer.
- An understanding of how to break down a problem into a form suitable for solving with a program.
- An appreciation of what algorithms are and how to devise them.
- A knowledge of how to design, implement, test and debug software.
- An understanding of the application of computer programming to data analysis problems.
At the end of the module, students will be expected to be able to devise a programmatic approach to problem solving, and to use a programming language and software development environments to design, implement, test, and debug programs.
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 12 | 2:00 | 24:00 | Lecture follow-up includes time for formative exercises |
| Scheduled Learning And Teaching Activities | Lecture | 8 | 1:00 | 8:00 | Synchronous present in person (pip) tutorial sessions. |
| Structured Guided Learning | Lecture materials | 12 | 1:00 | 12:00 | Asynchronous online materials |
| Guided Independent Study | Assessment preparation and completion | 1 | 20:00 | 20:00 | Practical/lab report assessments |
| Scheduled Learning And Teaching Activities | Practical | 8 | 2:00 | 16:00 | Synchronous present in person (pip) practical sessions. |
| Guided Independent Study | Independent study | 1 | 20:00 | 20:00 | Background Reading |
| Total | 100:00 |
Lectures materials are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. Lecture follow-up, e.g., quizzes and exercises, is associated with each lecture in order to provide sufficient hands-on training and rapid feedback on understanding. Scheduled sessions are used both for solution of problems and work requiring extensive computation and to give insight into the ideas/methods studied.
The format of resits will be determined by the Board of Examiners
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Practical/lab report | 1 | M | 100 | Programming assignment |
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Prob solv exercises | 1 | M | Practical/Tutorial exercises |
Programming assignment allows the students to develop their problem-solving techniques, to practise the methods learned in the module, to assess their progress and to receive feedback.
Students will be given a formative exercise to introduce them to relevant tools, develop their understanding of programming concepts and provide them with the opportunity to gain experience through practical application.
N/A
Disclaimer: The information contained within the Module Catalogue relates to the 2023/24 academic year. In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described. Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, and student feedback. Module information for the 2024/25 entry will be published here in early-April 2024. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.