# Columbia University: Data, Models and Decisions in Business Analytics

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

12 weeks
8–10 hours per week
Instructor-paced
Instructor-led on a course schedule

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.

### At a glance

• Institution: ColumbiaX
• Prerequisites:

Undergraduate probability, statistics and linear algebra. Students should have working knowledge of Python and familiarity with basic programming concepts in some procedural programming language.

• Language: English
• Video Transcript: English
• Associated skills: Operations, Data Modeling, Marketing, Mathematical Optimization, Stochastic Modeling, Data-Driven Decision-Making, Probability, Business Analytics, Statistics, Decision Making

# What you'll learn

Skip What you'll 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

# Syllabus

Skip Syllabus
• 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

# Who can take this course?

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.