CSC-20053 - Statistical Techniques for Data Analytics
Coordinator: Jie Cheng Tel: +44 1782 7 33775
Lecture Time: See Timetable...
Level: Level 5
Credits: 15
Study Hours: 150
School Office: 01782 733075

Programme/Approved Electives for 2022/23

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2022/23


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: 1,2
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.: 1,2

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.