The topics include analysis of matching methods, identification of average, local average and marginal treatment effects using instrumental variables, regression discontinuity, randomised control experiments, post-estimation diagnostics, cross section and panel data with static and dynamic models, binary choice models and binary classification methods in machine learning, maximum likelihood estimation, ridge regression, lasso regression, and principal component regression. This course is ideal for advanced undergraduate students, graduate students, early-career academic researchers, and researchers in the public, private or non-profit sector. You will learn how to apply these techniques to data in business and scientific applications. A master's degree in economics and data science can be completed within 20-24 months. The data on the form will also be used for monitoring purposes and to track future applications. The course will provide participants with the knowledge they require to understand the intuition behind relevant machine learning algorithms. Students in this specialization examine theories and models used to analyze data, identify empirical patterns, forecast economic variables, and make decisions. This course provides an introduction to modern applied economics in a manner that does not require any prior background in economics or statistics. Topics include equality of opportunity, education, health, the environment, and criminal justice. This page provides lecture materials and videos for a course entitled “Using Big Data Solve Economic and Social Problems,” taught by Raj Chetty and Greg Bruich at Harvard University. Challenges of building Big Data infrastructure for sustainable scalability and flexibility; Strategies and frameworks for the effective integration of new datasets into policy analysis and decision-making procedures; Case study: how did the Bank of England embrace Big Data technologies to support better data … Dates: 13 July – 31 July 2020. Browse the latest online big data courses from Harvard University, including "Harvard Business Analytics Program " and "Introduction to Functional and Stream Programming for Big Data Systems." The course was most recently taught at Harvard in Spring 2019, and, with an enrollment of 375 students, was one of the largest classes in the university. Overview of Statistical Reasoning, and Introduction to Causal Inference (potential outcomes model, SUTVA, ATE), Standard errors: serial correlation, clustering and the bootstrap, Binary Models, Likelihood-based inference, Numerical optimisation in practice, Introduction to GMM & Practical Problems In Applied Analysis, Post-estimations diagnostics (Goodness of fit, Tests for functional form, tests for normality of errors, Leverage, influential observations and test for outliers), quantile regression and quantile treatment effects, Regression discontinuity design. This course provides an introduction to modern applied economics in a manner that does not require any prior background in economics or statistics. We want to hear from you! Our Big Data Hadoop certification training course lets you master the concepts of the Hadoop framework, Big Data tools, and methodologies to prepare you for success in your role as a Big Data Developer. Alumni are employed in a wide range of national and international organisations, in government, international institutions, business and finance. 1280 Massachusetts Avenue Harvard University A central part of Opportunity Insights’ mission is to train the next generation of researchers and policy leaders on methods to study and improve economic opportunity and related social problems. Based at Harvard University, our team of researchers and policy analysts work together to analyze new data and create a platform for local stakeholders to make more informed decisions. Stories from the Atlas: Describing Data using Maps, Regressions, and Correlations, Empirical Project 2 We except participants to have completed an introductory economics course. 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