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Data Analytics Stream

Learning Outcomes and Skills

  • Ability to formulate and test statistical hypotheses
  • Ability to work with very large datasets of all types
  • Ability to run simulation exercises using computational techniques
  • Knowledge of specialized econometric techniques such as Time Series Econometrics and Bayesian Econometrics
  • Ability to program in R and Hadoop
  • Preparedness for jobs in the data analytics field (FMCG, retail, consulting, e-commerce, financial institutions etc.)
  • How to estimate Bayesian models using R

The course descriptions of each elective are given below.

 

Statistical Computing & Machine Learning

Instructor: Sunil Saraswat, Assistant Professor, University of Mumbai

Statistical computing and machine learning are techniques of recognizing patterns from data in order to develop statistical models. This course will teach students:

  • How to carry out Monte-Carlo simulations using R
  • How to aggregate data in cluster analysis to uncover similarities in large data sets
  • Neural network methods
  • Stochastic vector machine regressions
  • How to work with Decision trees and Boosted Regression trees to model statistical relationships

 

Time Series Econometrics

Instructors: Neeraj Hatekar (Director, Department of Economics, University of Mumbai) and Anuradha Patnaik (Assistant Professor, University of Mumbai)

Many of the insights from the Core Econometrics course, which studies mostly cross-sectional data, carry over to situations where data are collected over time (e.g. financial data). However, time-series data present important challenges of their own. In this course, students will learn:

  • How to represent time series data as observations drawn from a stochastic processes with lag operators
  • The difference between stationary and non-stationary time series
  • How to test whether a time series is stationary or non-stationary
  • How to estimate models with stationary time series data, and how to detect and address the problem of serial correlation in these models
  • How to estimate the temporary and permanent effects of shocks in a macroeconomic or financial model using distributed-lag models
  • How to apply the Box and Jenkins methodology for modelling the conditional mean of a macroeconomic time series
  • How to estimate the volatility in financial markets using the autoregressive conditionally heteroscedastic (ARCH) and generalized conditionally heteroscedastic (GARCH) models
  • How to estimate models with non-stationary time series data, and how to work with integrated processes
  • How to estimate vector autoregression (VAR) and vector error-correction (VEC) models
  • How to infer from the impulse response function the effects of a particular policy intervention
  • How to detect causality in VAR systems
  • How to perform univariate and multivariate forecasting exercises
  • How to implement econometric estimation of time series models using the statistical software R

 

Advanced Econometrics

Instructors: Instructors: Neeraj Hatekar (Director, Department of Economics, University of Mumbai) and Anuradha Patnaik (Assistant Professor, University of Mumbai)

This course builds on the Core Econometrics course and will introduce students to techniques for handling panel data (which combines features of both cross sectional and time series data) and qualitative dependent or binary variables and for working with simultaneous equation models. In particular they will learn:

  • The difference between Fixed effects and Random effects models
  • How to obtain the reduced form from the structural form and estimate the parameters of the same using different approaches like 2SLS, IV,  LIML etc.
  • How to model situations where the dependent variable is binary or qualitative
  • How to formulate and test hypotheses using the Lagrange Multiplier (LM) test, Wald test and Likelihood ratio test (LRT)
  • How to do all this in R

The Workshops/Econ Labs in this stream will cover topics such as programming in R and Hadoop, two computing languages that are very commonly used in data analytics jobs.

Workshops/Econ Labs

The Workshops/Econ Labs in this stream will cover topics such as programming in R and Hadoop, two computing languages that are very commonly used in data analytics jobs.

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Contact a course advisor at
022-60126001