Programme/Approved Electives for 2021/22
Available as a Free Standing Elective
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: 1evaluate machine learning methods in the context of statistical analysis of data representing social or natural systems: 2develop advanced applications of statistical data analytics techniques using an advanced specialist programming language (e.g. R, Matlab): 1assess the options of storing, managing and manipulating very large volumes of data in the context of research or business organisations: 2assess 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
6 hours scheduled online Q&A sessions6 hours classroom-based tutorials20 hours online lectures expected to be attended as independent study14 hours online tutorial activity attended as independent study108 hours independent study2 hour exam
1: Assignment weighted 50%
Description of Module Assessment
Written reportA 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 examThe 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.