Big Data Analytics (MSIN0208)

Key information

Faculty: Faculty of Engineering Sciences
Teaching department: 果冻影院 School of Management
Credit value: 15

Restrictions: This module is restricted to students on the MSc Finance (TMSFINSING01) programme and 果冻影院 School of Management affiliates (TAFMNGSING01).
Timetable

Alternative credit options

There are no alternative credit options available for this module.

Description

This module discusses modern methods from Machine Learning, Computer Science, Statistics, and Artificial Intelligence for the analysis of large datasets and their use in finance. These Machine Learning methods have become essential tools for the exploration and evaluation of data for scientists and practitioners in many fields, including finance. The course will cover these from a theoretical and applied perspective, with many finance applications. Topics covered include dimension reduction methods for large data sets, methods for forecasting with many predictors, fundamentals of neural networks, and more advanced topics such as nowcasting and natural language processing.

Module deliveries for 2024/25 academic year

Intended teaching term: Term 3 听听听 Postgraduate (FHEQ Level 7)

Teaching and assessment

Mode of study: In person
Methods of assessment: 40% Other form of assessment

60% Exam
Mark scheme: Numeric Marks

Other information

Number of students on module in previous year: 27
Module leader: Professor Timothy Christensen
Who to contact for more information: mgmt-postgraduate@ucl.ac.uk

Last updated

This module description was last updated on 8th April 2024.

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Important information

The catalogue has been updated with key information about the modules that will run during the 2024/25 academic session. Current and prospective students can browse through the catalogue to consider possible module choices for the coming year.

Please note,听information in the catalogue is听subject to change as teaching and assessment arrangements for 2024/25 may need to be adjusted in line with the University's Feedback and Assessment principles and operating model.

Centrally managed exam durations will be provided on students' individual exam timetables. Arrangements for locally managed exams and in-class activity will be confirmed by the teaching department for the module.

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