Data 2: Data Analysis & Management - COM00022I
- Department: Computer Science
- Credit value: 10 credits
- Credit level: I
- Academic year of delivery: 2022-23
Module summary
Data Analysis and Management
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Autumn Term 2022-23 |
Module aims
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.
Module learning outcomes
|
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. |
Indicative assessment
| Task | % of module mark |
|---|---|
| Online Exam -less than 24hrs (Centrally scheduled) | 50 |
| Online Exam -less than 24hrs (Centrally scheduled) | 50 |
Special assessment rules
None
Indicative reassessment
| Task | % of module mark |
|---|---|
| Online Exam -less than 24hrs (Centrally scheduled) | 50 |
| Online Exam -less than 24hrs (Centrally scheduled) | 50 |
Module feedback
Feedback is provided through work in practical sessions, and after the final assessment as per normal University guidelines
Indicative reading
*** 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,