- Department: Computer Science
- Module co-ordinator: Dr. Simos Gerasimou
- Credit value: 10 credits
- Credit level: C
- Academic year of delivery: 2021-22
Introduction to Data Science
Occurrence | Teaching period |
---|---|
A | Spring Term 2021-22 to Summer Term 2021-22 |
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 the key statistical tests for frequentist data science, where specific theories are explored and tested via descriptive and inferential statistics. Students will apply their existing programming knowledge to execute these tests in an appropriate programming language using existing statistical libraries.
D101 |
Distinguish between different types of data that are generated in science, engineering and design. |
D102 |
Identify the types of questions that can be asked of data in satisfaction of a particular information goal. |
D103 |
Employ strategies for ensuring data quality |
D104 |
Identify aspects of data governance to judge whether and how data can be used in analyses |
D105 |
Collect, transform, prepare and clean data for purposes of analysis |
D106 |
Use appropriate visualisations to present and explore data sets |
D107 |
Apply descriptive statistics to understand the basic features of the data |
D108 |
Apply inferential statistics to test hypotheses about features and relationships within data sets |
D109 |
Retrieve data from a variety of different data sources in a variety of different formats |
D110 |
Identify the ethical concerns regarding the provenance of data, the privacy of individuals, and the impact data analytics can have on society. |
D111 |
Describe and apply topics from a code of ethics from a professional, national or international body in relation to data protection. |
Task | Length | % of module mark |
---|---|---|
Online Exam - 24 hrs (Centrally scheduled) Data 1: Introduction to Data Science (DAT1) |
8 hours | 100 |
None
Task | Length | % of module mark |
---|---|---|
Online Exam - 24 hrs (Centrally scheduled) Data 1: Introduction to Data Science (DAT1) |
8 hours | 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