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

Programme/Approved Electives for 2024/25


Available as a Free Standing Elective






Barred Combinations


Description for 2024/25

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: 2
evaluate machine learning methods in the context of statistical analysis of data representing social or natural systems: 1
develop advanced applications of statistical data analytics techniques using an advanced specialist programming language (e.g. R, Python, and Matlab): 1
assess the options of storing, managing and manipulating very large volumes of data in the context of research or business organisations: 1
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

24 hours of classroom-based lectures as the active learning
24 hours of classroom-based tutorials as the active learning
24 hours of preparation for tutorials as the independent study
24 hours of preparation for the open-book exam as the independent study
2 hours of the open-book exam as the independent study
52 hours for research and preparing the coursework assignment as the independent study

School Rules

Knowledge of Programming is essential. Students not having a background in programming are required to attend the course CSC-40044 (System Design and Programming) offered by the department

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. The code needs to be submitted as an appendix. The appendix does not count for the word count.

2: Open Book Examination weighted 50%
Online open book exam with 28-hour window
The exam contains three questions. The learners will have to answer two out of these three questions. Each question will have a part covering bookwork 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 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. Students will be given 28 hours to complete the task.