Accessibility statement

Data 2 - COM00022I

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  • Department: Computer Science
  • Module co-ordinator: Dr. Rahul Ruttun
  • Credit value: 10 credits
  • Credit level: I
  • Academic year of delivery: 2020-21

Module summary

Data Analysis and Management

Related modules

Pre-requisite modules

Co-requisite modules

  • None

Prohibited combinations

  • None

Module will run

Occurrence Teaching cycle
A Autumn Term 2020-21

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, regression, and Principal Components Analysis, and understand when to apply them.

D205

Demonstrate and apply in practice the principles of map-reduce methods for analysing big data.

Assessment

Task Length % of module mark
Essay/coursework
Data 2 Lab Exam
N/A 100

Special assessment rules

None

Reassessment

Task Length % of module mark
Essay/coursework
Data 2 Lab Exam
N/A 100

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,



The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.