Introduction to Analytics Modeling

Course 1 of 3: MicroMasters® Program in Analytics: Essential Tools and Methods 16 weeks total 8 – 10 hours each week
915

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About this course

Analytical models are key to understanding data, generating predictions, and making business decisions. Without models it’s nearly impossible to gain insights from data. In modeling, it’s essential to understand how to choose the right data sets, algorithms, techniques and formats to solve a particular business problem.

In this course, part of the Analytics: Essential Tools and Methods MicroMasters® program, you’ll gain an intuitive understanding of fundamental models and methods of analytics and practice how to implement them using common industry tools like R.

You’ll learn about analytics modeling and how to choose the right approach from among the wide range of options in your toolbox.

You will learn how to use statistical models and machine learning as well as models for:

  • classification;
  • clustering;
  • change detection;
  • data smoothing;
  • validation;
  • prediction;
  • optimization;
  • experimentation;
  • decision making.

What you’ll learn

  • Fundamental analytics models and methods
  • How to use analytics software, including R, to implement various types of models
  • Understanding of when to apply specific analytics models

Prerequisites

  • Probability and statistics
  • Basic programming proficiency
  • Linear algebra
  • Basic calculus

Who can take this course?

Unfortunately, learners from 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.

Meet Your Instructors

Joel Sokol

Director of the Master of Science in Analytics program
He received his PhD in operations research from MIT and his bachelor’s degrees in mathematics, computer science, and applied sciences in engineering from Rutgers University. His primary research interests are in sports analytics and applied operations research. He has worked with teams or leagues in all three of the major American sports. Dr. Sokol's LRMC method for predictive modeling of the NCAA basketball tournament is an industry leader, and his non-sports research has won the EURO Management Science Strategic Innovation Prize. Dr. Sokol has also won recognition for his teaching and curriculum development from IIE and the NAE, and is the recipient of Georgia Tech's highest awards for teaching.
915

Experience Level

Advanced

Learning Partner

The Georgia Institute of Technology

Program Type

MicroMasters®

Subject

Data Analysis & Statistics Data Science IT
Advanced Business Harvard X-Series