ISYE 6414: Regression Analysis

Instructional Team

Nicoleta Serban
Nicoleta Serban
Creator, Instructor
Olaoluwa (Dami) Alebiosu
Olaoluwa (Dami) Alebiosu
Head TA

Overview

This course provides an introduction to the principles and practice of regression analysis, covering linear regression, generalized linear models, and model selection techniques. Students gain foundational experience with widely used analytical tools while engaging in hands on data science investigation. Emphasis is placed on active participation, independent exploration, and developing the ability to apply appropriate methods to real data. By the end of the course, students will be able to formulate and address data driven questions, select and implement suitable regression techniques, and interpret results within the context of a defined analysis. Because regression modeling underpins most statistical and machine learning approaches, mastering these fundamentals strengthens analytic skills across a wide range of application areas. Regression remains one of the most widely used tools in data analysis, making a solid understanding of its foundations valuable for any student pursuing a career in analytics.

Course Goals

The primary learning goals of the course are to understand:

  1. The fundamental principles and foundations of regression modeling, including linear regression, generalized linear models, model assumptions, estimation, diagnostics, and model selection strategies.
  2. The end to end workflow for conducting data driven regression analysis, from exploratory data analysis to modeling, evaluation, interpretation, and communication of results.
  3. The role of regression modeling within modern analytics and machine learning, and how regression concepts generalize to or underpin more advanced statistical learning methods.

These learning goals map to the following learning outcomes. By the end of the course, you will be able to:

  1. Build and diagnose regression models grounded in statistical principles, choosing appropriate techniques based on data characteristics and evaluating model adequacy through residual analysis and validation.
  2. Conduct complete regression analysis workflow, including exploratory data analysis, variable selection, model implementation in R and Python, interpretation of coefficients and predictions, and communication of findings clearly and rigorously.
  3. Apply regression modeling skills to open ended, real world problems, extending core techniques to new datasets, integrating broader analytical reasoning, and using regression as a foundation for more advanced methods in analytics and machine learning.

Sample Syllabus

Summer 2026 syllabus

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

Although this course does not have formal prerequisites within OMSCS, students will be most successful if they enter the course with the following background knowledge and skills:

Mathematical Foundational Background:

  • Linear algebra, including vectors, matrices, matrix operations, and basic properties of linear systems.
  • Probability and statistics, including random variables, distributions, expectation and variance, correlation, and hypothesis testing.

General Analytical Skills:

  • Interpreting mathematical notation within statistical models.
  • Reasoning about assumptions, limitations, and trade offs when selecting analytical methods.
  • Engaging in independent exploration and iterative model development, as the course emphasizes individual discovery and hands on application.
Technical Requirements and Software

Software:

  • Python and/or R installed locally.
  • Ability to run Jupyter Notebooks, which will be used extensively for homework, exams and project work.
  • Adobe PDF Reader or equivalent for viewing and verifying submissions.

Technical Requirements:

  • Familiarity with Python and/or R statistical software for implementing regression models and completing homework assignments.
  • Use of Jupyter Notebooks with R and/or Python for completing most coding tasks in Jupyter notebooks. You should be able to:
    • Run notebooks using Python and/or R kernels.
    • Install and manage required libraries.
    • Execute, modify, and annotate analysis workflows within notebook environments.
  • Familiarity with Git and GitHub, as all course examples, datasets, and template notebooks are provided through a GitHub repository. Students should be able to:
    • Clone or download the course repository.
    • Pull updates as new materials are released.
    • Navigate the repository structure to access slides, examples, datasets, and code.
  • Optional but helpful: An IDE such as VS Code, PyCharm, or RStudio for managing analysis scripts outside the notebook environment.

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.