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POLS0003 Data Science and Big Data Analytics - (module not running in 2019/20)

Course Code: POLS0003

Length: One term 

Teaching: 20 hours lectures/10 hours seminars

Assessment: One 3,000 word research project

Credits: 15 credits, 4 (US) 7.5 (ECTS) 

Module Level: Advanced

About this course

Data Science and Big Data Analytics are exciting new areas that combine scientific inquiry, substantive expertise, programming, and statistical knowledge. One of the main challenges for businesses and policy makers when using big data is to find people with the appropriate skills. Data Science is no longer only the domain of computer scientists and engineers. Good Data Science requires experts that combine substantive knowledge with data analytical skills, which makes it a prime area for social scientists with an interest in quantitative methods. 

Drew Conway's Data Science Venn Diagram

Drew Conway’s Data Science Venn diagram (http://bit.ly/DrewConwayVenn) highlights the focus of this course. The course integrates prior training in quantitative methods (statistics) and coding with substantive expertise and introduces the fundamental concepts and techniques of Data Science and Big Data Analytics.

This course aims to provide an introduction to the data science approach to the quantitative analysis of data using the methods of statistical learning, an approach blending classical statistical methods with recent advances in computational and machine learning. The course will cover the main analytical methods from this field with hands-on applications using example datasets, so that students gain experience with and confidence in using the methods we cover. It also covers data preparation and processing, including working with structured databases, key-value formatted data (JSON), and unstructured textual data.

Learning outcomes

At the end of this course students will have a sound understanding of the field of data science, the ability to analyse data using some of its main methods, and a solid foundation for more advanced or more specialized study.

Prerequisites

This is an advanced course intended for students who’ve already had some training in quantitative methods for data analysis. An introduction to quantitative methods (statistics/econometrics) at any level would serve as a very useful foundation for this course, although no formal prerequisites are required. Familiarity with computer programming or database structures is a benefit, but not formally required.

If you are unsure whether your prior statistical training is sufficient to take this course please contact the course tutor for confirmation.

This module will not be running in 2018/19