Introduction to Data Science and Big Data Analysis
calendar_month 29 Iul 2015, 00:00
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. 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. 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 form more advanced or more specialised study.The course will cover the following topics:- an overview of data science and the challenge of working with big data using statistical methods- how to integrate the insights from data analytics into knowledge generation and decision-making- how to acquire data, both structured and unstructured, and to process it, store it, and convert it into a format suitable for analysis;- the basics of statistical inference including probability and probability distributions, modelling, experimental design;- an overview of classification methods and related methods for assessing model fit and cross-validating predictive models;- supervised learning approaches, including linear and logistic regression, decision trees, and na%C3%AFve Bayes;- unsupervised learning approaches, including clustering, association rules, and principal components analysis- quantitative methods of text analysis, including mining social media and other online resources;- social network analysis, covering the basics of social graph data and analysing social networks;- data visualisation through a variety of graphs.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.
Course leader
Professor Kenneth Benoit (LSE)Dr Slava Mikhaylov (UCL)
Target group
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 aim
This course aims to provide participants with:- an understanding of the structure of datasets and databases, including "big data";- the ability to work with datasets and databases;- an introduction to programming languages and basic skills in the R statistical program;- the ability to analyse data using statistical and machine learning methods.
Credits info
5 ECTS The decision to award credits is at the discretion of the student's home institution. Students should always check with their home institution to confirm the number of credits that can be awarded.
Fee info
GBP 1435: Student rate - available to current university (including PhD) students.Academic staff and staff of UK charities are eligible for a reduced rate of 1,930. GBP 2425: Standard rate
London School of Economics
Address: Houghton Street
Postal code: WC2A 2AE
City: London
Country: United Kingdom
Website: http://www.lse.ac.uk/study/summerSchools/Methods/home.aspx
E-mail: summer.methods@lse.ac.uk
Phone: +44 (0)20 7955 6422
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