- Department: Computer Science
- Module co-ordinator: Dr. Tarique Anwar
- Credit value: 20 credits
- Credit level: I
- Academic year of delivery: 2023-24
Introduction to Data Science
Occurrence | Teaching cycle |
---|---|
A | Semester 1 2023-24 |
Students will be introduced to key concepts required to undertake rigorous and valid data analysis. Students will be introduced to processes for collecting, manipulating and cleaning data, while gaining experience in judging the quality of data sources. Students will be introduced to statistical analysis in data science, including correlation, inferential statistics and regression, and how to use these tests in a programming environment. Relational databases, SQL, and and other database paradigms such as NoSQL, are covered as a way of storing and accessing data. A key aim of the module is to solve complex problems and deliver insights about multi-dimensional data.
Task | Length | % of module mark |
---|---|---|
Open Examination DATA |
N/A | 100 |
None
Task | Length | % of module mark |
---|---|---|
Open Examination DATA Reassessment |
N/A | 100 |
Feedback is provided through work in practical sessions, and after the final assessment as per normal University guidelines.
*** Spiegelhalter, D., The Art of Statistics: Learning from Data, Pelican, 2019.
*** VanderPlas, J. Python Data Science Handbook: Essential Tools for Working with Data, O’Reilly, 2016.
** Igual, L. Segui, S. Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications, Springer, 2017