Introductory description

This module will cover a wide range of cutting edge research in Big Data Analytics. The module has a particular focus on the extensive value of data from the Internet, much of which is freely available if students have the skills to mine it.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

The sessions in this module are made up of lectures, seminars and lab sessions.
Lectures will help students understand use cases for big data, and will provide them with some guidance on acquiring practical data science skills.
Seminars will focus on the acquisition of statistical skills in R, as well as techniques for visualisation.
Lab sessions will enable the students' acquisition of practical programming skills in R.
The course will follow the structure below.
Week 1
Lecture: Data science - an introduction
Week 2
Lecture: Getting quicker measurements with big data
Lab session: Mining Google data
Week 3
Lecture: Making predictions with big data - 1
Seminar: Basic statistics in R - 1
Lab session: Mining data from photos
Week 4
Lecture: Making predictions with big data - 2
Seminar: Basic statistics in R - 2
Lab session: Mining Wikipedia data
Week 5
Lecture: Measuring emotions and personality
Seminar: Basic statistics in R - False discoveries
Lab session: Mining Twitter data
Week 6
Lecture: Big data in the city
Seminar: Visualising data
Lab session: Visualisations
Week 7
Lecture: Data science - your own project
Seminar: Final project
Lab session: Project work
Week 8
Lab sessions: Project work
Week 9
Lab sessions: Project work
Weeks are specified in module weeks, not term weeks.

Learning outcomes

By the end of the module, students should be able to:

• Understand research methods in big data analytics and computational social science.
• Demonstrate understanding of results of research in big data analytics and computational social science.
• Understand and identify links between big data resources and real world events.
• Critically evaluate empirical research.
• Acquire and mine large data sets using a range of methods.
• Preprocess data sets to allow their subsequent application to real world problems.
• Visualise extensive data sets, applying methods which both allow the visualisation consumer to ask their own questions, and methods which directly answer specific questions.
• Formulate hypotheses and apply statistical methods for their evaluation.

Indicative reading list

Please see Talis bibliography at the following link:
https://rl.talis.com/3/warwick/lists/E1DCBC50-A278-379F-F255-3D29A326D2AB.html

Subject specific skills

Apply methods in big data analytics and computational social science.
Perform small pilot studies in big data analytics.
Demonstrate business relevant data science skills.
Demonstrate confidence in discussing research results and their practical relevance in the real world.

Transferable skills

Write in an academically appropriate way.