ORIE 3120
Practical Tools for Operations Research, Machine Learning and Data Science
Spring 2022

Course Description: The practical use of software tools and mathematical methods from operations research, machine learning, statistics and data science. We begin by learning to manipulate data using structured query language (SQL) and visualize it using Tableau and Python. Then, continuing in Python, we learn the use of machine learning and statistical methods include multiple linear regression, classification, logistic regression, clustering, and time-series forecasting. We also learn about causality, how making decisions based on observational data can be dangerous and how to design experiments to avoid these dangers. Finally we learn about stochastic modeling and simulation using all of this data and how it can be used to make decisions. These topics will be presented in the context of business and public sector applications in healthcare, transportation, manufacturing, retail, and e-commerce.

Lecture: WF 9:40am-11:55am starting Jan 24

Instructor: Peter Frazier

Recitation: Recitations meet starting the week of Jan 31.

Piazza: is where announcements are posted. It also serves as a forum for students to post and answer questions. Piazza should be used to ask logistical questions and conceptual questions. You can enroll for piazza here.

ENGRC 3120: ENGRC 3120 is a companion course that focuses on the communication aspects of the material we teach in ORIE 3120. It will be particularly helpful when we tackle the project, in the second half of the course. We recommend it for students in ORIE 3120 who are interested in communication for its own sake, want to have extra help with clear communication in the project, want to interview well and be effective in an internship or a job, and/or need to satisfy their technical writing requirement.

Office Hours: Office hours are the right venue for detailed help with installation issues or debugging. Office hours times and locations will be posted on the course calendar.

Gradescope: is where you will submit assignments and receive grades.

In-class questions: We will use polleverywhere, PollEv.com/orie3120, for in-class questions. To get credit, answer during lecture. During the virtual instruction period, if you are in a time zone where lecture overlaps with hours between 10:30pm and 8am, make a private post on piazza within 24 of the lecture with your location and answers to the poll to get credit.

Acknowledgment

This course has been built over several years by many dedicated teachers. The present instructor gratefully acknowledges Madeleine Udell, Frans Schalekamp, Peter Jackson, David Ruppert, John Callister and Michael Clarkson for their contributions to this course.