Big Data Analytics ACCFIN5231
- Academic Session: 2022-23
- School: Adam Smith Business School
- Credits: 10
- Level: Level 5 (SCQF level 11)
- Typically Offered: Semester 2
- Available to Visiting Students: No
- Available to Erasmus Students: No
The course will provide students in depth knowledge and techniques of big data analytics and machine learning, as well as some relevant programming skills.
Course is delivered over 2 or 3 weeks, comprising of 15 hours of lectures and 4 hours of practical classes.
Requirements of Entry
Registration on the MSc Financial Technology programme
ILO being assessed
The aim of this course is to provide students with in-depth knowledge of advanced aspects of big data analytics, learning the techniques and analytical approach to the examination of large data sets opportunities to apply appropriate machine learning techniques in order to analyse big data sets, to assess the statistical significance of data mining results, and to use statistical packages to perform basic data mining tasks on big data.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
1. Demonstrate knowledge of advanced aspects of big data analytics
2. Apply appropriate machine learning techniques to analyse big data sets
3. Assess the statistical significance of data mining results
4. Utilize statistical packages (R and Python) to perform basic data mining tasks on big data.
Minimum Requirement for Award of Credits
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.