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LPS-MY201: Big Data: Data Analytics for Business and Beyond

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In this modern information age, the broad availability of Big Data (i.e. data of unprecedented sizes and complexities) brings opportunities with challenges to business and beyond. For example, companies are focused on exploiting data for competitive advantages; cyberspace communications reveal complex social interactions; and Big Data surveillance is an effective way to detect actionable security threats. Data analytics is a subject of learning from data,of measuring, controlling, and communicating uncertainty, and of data-driven decision-makings (DDD). It will become ever more critical as businesses, governments and also academia rely increasingly on DDD, expanding the demand for data analytics expertise.

The primary goal of this course is to help you view various problems from business, science and social domains from a data perspective and understand the principles of extracting useful information and knowledge from data. To achieve this primary goal, inevitably we will introduce some basic data analytic methods and illustrate them with real-life examples (some from China).

The secondary goal of the course is a focus on a fundamental structure to data-analytic thinking, and basic principles and concepts of data analytic methodology. We will also point out the limitation of data analysis: one should not be carried away by the findings from data and models. Common sense, intuition, domain knowledge and creativity often play roles in good data analytics.

Data analytics has multiple facets and approaches, encompassing diverse statistical techniques under a variety of names such as data mining, machine learning. The methods to be covered include:

  • Classification. Among all customers of EDF, who are likely to switch to another energy supplier?
  • Regression (i.e. value estimation). How much will a given customer use the service?
  • Similarity matching. Identify individuals who are similar to your most loyal customer group.
  • Clustering. How should our customer care teams be structured?
  • Market-basket analysis. Should beers be placed next to baby napkins in a supermarket?
  • Link prediction. As you and John share 10 friends, maybe you would like to be John’s friend?
  • Causal modelling. Is the increase of sales caused by a particular advertisement?

This is not a course on algorithms and IT technologies required for handling massive data, which deserve separate courses. The focus is on the fundamental principles and concepts of data analytics or data science. It becomes ever-increasingly important in this information age to gain adequate understanding of data science even if you never intend to apply it yourself.

Full course outline

About the Instructor

Qiwei Yao

Professor Qiwei Yao  is a  Professor of Statistics at LSE and a Distinguished Visiting Professor at Guanghua School of Management at PKU. Professor Yao is a leading expert in high-dimensional time series analysis and nonlinear time series analysis, and is also a Fellow of the Institute of Mathematics Statistics, Fellow of the American Statistical Association, and Elected Member of the International Statistical Institute. His current research focuses on modelling and forecasting with vast time series data. Professor Yao has undertaken extensive data analytics consultancy projects from major industry companies including Barclays Bank, Electricité de France (EDF), and Winton Capital Management Ltd.

 
Did you know...?

Data-driven decision making has never been as important and irreplaceable as now, in this information age, with big and complex data.
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