The 2016 Methods Summer Programme is now closed. Details for the 2017 Programme will be available soon.
Data Science and Big Data Analytics are exciting new areas that combine scientific inquiry, statistical knowledge, substantive expertise, and computer programming. One of the main challenges for businesses and policy makers when using big data is to find people with the appropriate skills. 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. This 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.
Typical students will be Masters and PhD students from any field requiring the fundamentals of data science or working with typically large datasets and databases. Practitioners from industry, government, or research organisations with some basic training in quantitative analysis or computer programming are also welcome. Because this course surveys diverse techniques and methods, it makes an ideal foundation for more advanced or more specific training. Our applications are drawn from social, political, economic, legal, and business and marketing fields, rather than engineering or other sciences.
Course benefits
This course provides participants with:
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an understanding of the structure of datasets and databases, including "big data"
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the ability to work with datasets and databases
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an introduction to programming languages and basic skills in the R statistical program
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the ability to analyse data using statistical and machine learning methods.
Prerequisites
Students should already be familiar with quantitative methods at an introductory level, up to linear regression analysis. Familiarity with computer programming or database structures is a benefit, but not formally required.
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. We 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. We also cover data preparation and processing, including working with structured databases, key-value formatted data (JSON), and unstructured textual data. 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 specialised study.
The course will be delivered as a series of morning lectures, followed by lab sessions in the afternoon where students will apply the lessons in a series of instructor-guided exercises using data provided as part of the exercises.
The course will cover the following topics:
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an overview of data science and the challenge of working with big data using statistical methods
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how to integrate the insights from data analytics into knowledge generation and decision-making
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how to acquire data, both structured and unstructured, and to process it, store it, and convert it into a format suitable for analysis
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the basics of statistical inference including probability and probability distributions, modelling, experimental design
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an overview of classification methods and related methods for assessing model fit and cross-validating predictive models
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supervised learning approaches, including linear and logistic regression, decision trees, and naïve Bayes
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unsupervised learning approaches, including clustering, association rules, and principal components analysis
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quantitative methods of text analysis, including mining social media and other online resources
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social network analysis, covering the basics of social graph data and analysing social networks
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data visualisation through a variety of graphs.
Main texts
James et al. (2013) An Introduction to Statistical Leaning: With applications in R . Springer.
Zumel, N. and Mount, J. (2014). Practical Data Science with R. Manning Publications.
The following are supplemental texts which you may also find useful:
Lantz, B. (2013). Machine Learning with R. Packt Publishing.
Conway, D. and White, J. (2012) Machine Learning for Hackers . O'Reilly Media.
Leskovec, J., Rajaraman, A. and Ullman, J. (2011). Mining of Massive Datasets . Cambridge University Press.
Zafarani, R., Abbasi, M. A. and Liu, H. (2014) Social Media Mining: An introduction . Cambridge University Press.
Software used
R and SQLite.
Kenneth Benoit is Professor of Quantitative Social Research Methods at the Department of Methodology, LSE. With a background in political science, his substantive work focuses on political party competition, political measurement issues, and electoral systems. His research and teaching is primarily in the field of social science statistical applications. His recent work concerns the quantitative analysis of text as data, for which he has developed a package for the R statistical software.
Dr Slava Mikhaylov is a Senior Lecturer in Quantitative Methods at UCL and has been teaching quantitative methods at UCL Political Science department for the last five years. He’s currently involved in an ESRC Big Data infrastructure investment initiative – Consumer Data Research Centre at UCL. One of Slava’s responsibilities in the Centre is development and provision of big data analytics training for academic and professional community (data users). In addition Slava Mikhaylov is deputy director of UCL Q-Step Centre, an ESRC-funded initiative to promote quantitative methods.
Please note: A full timetable will be provided at registration on Monday 15 August. The below timetables contain approximate hours only.
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3
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3
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3
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3
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3
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1.5
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1.5
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3
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3
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3
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3
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3
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1.5
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1.5
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1.5
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Exam
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