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Data, Models and Decisions in Business Analytics

Provided by Columbia University (ColumbiaX)
8–10 hours
per week, for 12 weeks
Free

$320 USD for graded exams and assignments, plus a certificate

Learn fundamental tools and techniques for using data towards making business decisions in the face of uncertainty.

Start Date:

Before you start

Undergraduate probability, statistics and linear algebra. Students should have working knowledge of Python and familiarity with basic programming concepts in some procedural programming language.
Course opens: Sep 16, 2019
Course ends: Dec 16, 2019

What you will learn

  • Fundamental concepts from probability, statistics, stochastic modeling, and optimization to develop systematic frameworks for decision-making in a dynamic setting
  • How to use historical data to learn the underlying model and pattern
  • Optimization methods and software to solve decision problems under uncertainty in business applications
  • Introduction to Probability: Random variables; Normal, Binomial, Exponential distributions; applications
  • Estimation: sampling; confidence intervals; hypothesis testing
  • Regression: linear regression; dummy variables; applications
  • Linear Optimization; Non-linear optimization; Discrete Optimization; applications
  • Dynamic Optimization; decision trees

Overview

In today’s world, managerial decisions are increasingly based on data-driven models and analysis using statistical and optimization methods that have dramatically changed the way businesses operate in most domains including service operations, marketing, transportation, and finance.

The main objectives of this course are the following:

  • Introduce fundamental techniques towards a principled approach for data-driven decision-making.
  • Quantitative modeling of dynamic nature of decision problems using historical data, and
  • Learn various approaches for decision-making in the face of uncertainty

Topics covered include probability, statistics, regression, stochastic modeling, and linear, nonlinear and discrete optimization.

Most of the topics will be presented in the context of practical business applications to illustrate its usefulness in practice.

Meet your instructors

Vineet Goyal
Associate Professor, Industrial Engineering and Operations Research
Columbia University
View Courses
This course is part of:

Earn a MicroMasters® Program Certificate in 1 year if courses are taken one at a time.

View the program
  1. 96–120 hours of effort

    Learn the fundamental of programming in Python and develop the ability to analyze data and make data-driven decisions.

  2. Data, Models and Decisions in Business Analytics
  3. 96–120 hours of effort

    Learn how to use data to develop insights and predictive capabilities to make better business decisions.

  4. 96–120 hours of effort

    Develop quantitative models that leverage business data to forecast sales and support important marketing decisions.

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