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CSC-40054 - Module Specification
School of Computing and Mathematics
Faculty of Natural Sciences

For academic year: 2021/22 Last Updated: 16 February 2022

CSC-40054 - Data Analytics and Databases
Coordinator: Majeed Soufian
Lecture Time: See Timetable...
Level: Level 7
Credits: 15
Study Hours: 150
School Office: 01782 733075

Programme/Approved Electives for 2021/22

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2021/22


Aims
The module aims to equip learners with the knowledge of operations on databases and of a variety of tools and statistical techniques that enable them to make sense of the emergence and exponential growth of big data.
The learners will be able to critically evaluate and apply big data applications and advanced analytics and statistical modelling 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
evaluate machine learning methods in the context of statistical analysis of data representing social or natural systems: 2
develop advanced applications of statistical data analytics techniques using an advanced specialist programming language (e.g. R, Matlab): 1
assess the options of storing, managing and manipulating very large volumes of data in the context of research or business organisations: 2
assess a range of statistical approaches and apply the correct statistical approaches to extract information from a set of data typically available in a modern business or research organisation: 2

Study hours

6 hours scheduled online Q&A sessions
6 hours classroom-based tutorials
20 hours online lectures expected to be attended as independent study
14 hours online tutorial activity attended as independent study
108 hours independent study
2 hour exam

School Rules

None

Description of Module Assessment

1: Assignment weighted 50%
Written report
A report (maximum 3000 words) on the accessing, storage, manipulation and analysis of data available from an internet based data repository. Code needs to be submitted as appendix and the appendix does not count for the word count.

2: Open Book Examination weighted 50%
Online open book exam
The exam contains three questions. The learners will have to answer two out of these three questions. Each question will have a part covering book work material discussed during the lectures (e.g. definitions, comparisons of concepts) and a part about data analysis algorithms, including application and modification of such algorithms and advanced aspects of these algorithms (an algorithm may be provided in the exam paper and an R or Python or equivalent program code representation of the algorithm may be requested for the exam answer). Students should clearly label their answers with the number of the relevant question from the exam paper. Although students have been given significant time to complete this exam script, we expect most students to spend no more than 2 hours with writing the answers. The additional time is provided so that the student can schedule the writing of their exam answers to fit their other activities and also to accommodate time zone differences. Answers should be as accurate and concise as possible.

Please note that for the 2021/22 Academic Year all programmes will be adapted to operate in accordance with a flexible, digital approach. This means that there will 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: 16 February 2022