- Department: Computer Science
- Module co-ordinator: Dr. Rahul Ruttun
- Credit value: 10 credits
- Credit level: I
- Academic year of delivery: 2022-23
- See module specification for other years: 2021-22
Data Analysis and Management
Pre-requisite modules
Co-requisite modules
- None
Prohibited combinations
- None
Occurrence | Teaching period |
---|---|
A | Autumn Term 2022-23 |
Students build on the material in Data 1 and Software 2. Students will learn more sophisticated methods of statistical analysis to answer interesting questions about data. The core concepts behind relational and other databases are introduced in this module as a way of storing and accessing data. More specifically, students will learn; more advanced statistics carrying on from Data 1, the essentials of relational databases and SQL, and about other database paradigms (object, document). A key aim of the module is to deliver this in the context of solving complex problems and delivering insights about multi-dimensional data.
D201 |
Cite the basic goals, functions, and models of database systems. |
D202 |
Apply the modelling concepts and notation of the relational data model. |
D203 |
Give a semi-structured equivalent (eg, in JSON) for a given relational schema, and describe the differences between relational and semi-structured data models. |
D204 |
Apply standard statistical procedures such as clustering and regression, and understand when to apply them. |
D205 |
Demonstrate and apply in practice the principles of map-reduce methods for analysing big data. |
Task | Length | % of module mark |
---|---|---|
Online Exam -less than 24hrs (Centrally scheduled) Data 2 Lab Exam |
3 hours | 50 |
Online Exam -less than 24hrs (Centrally scheduled) Data 2 Theory Exam |
3 hours | 50 |
None
Task | Length | % of module mark |
---|---|---|
Online Exam -less than 24hrs (Centrally scheduled) Data 2 Lab Exam |
3 hours | 50 |
Online Exam -less than 24hrs (Centrally scheduled) Data 2 Theory Exam |
3 hours | 50 |
Feedback is provided through work in practical sessions, and after the final assessment as per normal University guidelines
*** Ricardo, Catherine M. and Urban, Susan. Databases Illuminated. 3rd edition. Jones & Bartlett Learning, 2017
*** Hastie et al.The Elements of Statistical Learning. 2nd edition, Springer, 2009
** Hoel, P. Introduction to Mathematical Statistics, Wiley
** McKinney, W. Python for Data Analysis, O’Reilly,