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CSC-20053 - Module Specification
School of Computer Science and Mathematics
Faculty of Natural Sciences

For academic year: 2024/25 Last Updated: 14 May 2024

CSC-20053 - Statistical Techniques for Data Analytics
Coordinator: Kp Lam Room: CR022 Tel: +44 1782 7 34110
Lecture Time: See Timetable...
Level: Level 5
Credits: 15
Study Hours: 150
School Office: 01782 733075

Programme/Approved Electives for 2024/25

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2024/25


Aims
The module aims to equip learners with the knowledge of a variety of tools and statistical techniques that enable them to deal with the analysis of large datasets.
The learners will be able to choose and apply data analytics and statistical techniques appropriate to different types of problems.

Intended Learning Outcomes

evaluate available data and determine how best to analyse the information available to provide required outcomes
: 1,2
apply statistical data analytics techniques using an advanced specialist programming language (e.g. R, Python, Matlab): 1,2
assess the options of storing, managing and manipulating large volumes of data in the context of business organisations: 1
select and apply an appropriate statistical approach, including contemporary machine learning methods, to extract information from a dataset, in the general context of data analysis: 1

Study hours

18 hours workshops/tutorials (supported online and in block release)
20 hours online lectures
106 hour independent learning
6 hour class-test (during block release)

School Rules

None

Description of Module Assessment

1: Assignment weighted 50%
Written report
A report (maximum 2000 words) on the accessing, storage, manipulation and analysis of data available from an internet based data repository. Code and analysed data will be submitted as instructed.

2: Exercise weighted 50%
A 6-hour lab-based class test on statistical data analysis techniques.
The class test contains a set of exercises for which the learners will have to complete in designated practical lab sessions timetabled in the last residential week. The exercises cover book work material covered during the online lectures (e.g. definitions, comparisons of concepts) and statistical data analysis algorithms, including application and modification of such algorithms.

Please note that for the 2022/23 Academic Year programmes may be adapted to operate in accordance with a flexible, digital approach. This means that there may be some changes made to how modules are delivered and assessed. The latest information will be provided to you by your School in module handbooks. We endeavour to give you as much notice as possible of any changes and details about the circumstance in which changes are made to programmes can be found in the Student Agreement, available at: https://www.keele.ac.uk/student-terms-and-conditions/

Last Updated: 14 May 2024