ISYE 6501: Intro to Analytics Modeling

Instructional Team

Joel Sokol
Joel Sokol
Creator, Instructor
Buzz
Ramon Rodriguez
Instructional Designer
Pin Hsu
Pin Hsu
Head TA

Overview

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, 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

This course is not foundational and does not count toward any specializations at present, but it can be counted as a free elective.

Course Goals

The most important thing you can learn from this course is not the memorization of any specific bit of material. Instead, I would like you to learn these skills:

  • Given a business (or other) question, select an appropriate analytics model to answer it, specify the data you will need to solve it, and understand what the model’s solution will and will not provide as an answer.
  • Given someone else’s use of analytics to address a specific business (or other) question, evaluate whether they have used an appropriate model (and appropriate data) and whether their conclusion is reasonable.

Another goal of this course is for you to learn how to think though descriptions and usage of new models, so you can continue to learn throughout your career. New techniques will certainly be developed after you graduate, and we want you to be able to pick them up quickly.

We will not cover the mathematics and algorithms under the hood, or deeper mastery of the modeling needed to set up the use of the technique. You can acquire those deeper levels of knowledge in elective courses. (In fact, we could spend an entire semester on many of the topics you’ll see in the course.)

Sample Syllabi

Summer 2023 syllabus and schedule (PDF)
Spring 2023 syllabus and schedule (PDF)
Fall 2022 syllabus and schedule (PDF)

Note: Sample syllabi are provided for informational purposes only. For the most up-to-date information, consult the official course documentation.

Before Taking This Class...

Suggested Background Knowledge
  • Probability and statistics
  • Basic programming proficiency
  • Linear algebra
  • Basic calculus

A little background in R can also be useful, but it isn’t necessary if you’re willing to learn on the fly.

Academic Integrity

All Georgia Tech students are expected to uphold the Georgia Tech Academic Honor Code. This course may impose additional academic integrity stipulations; consult the official course documentation for more information.