Please be advised: the information contained on this page is a general overview of the course. As course information is subject to change from one semester to another, please check DukeHub for the most accurate and up-to-date information about EGRMGMT courses.
Note: This course is currently in search of an instructor after Prof. Yang moved out of state. If you are interested in this course, please look into EGRMGMT 590.xx: Digital Marketing with Machine Learning, taught by Prof. Daniel Egger, for a similar offering.
At a Glance
- Instructor(s): Dan Yang (previously)
- Semester(s) typically taught: Spring
- Last taught: Spring 2021
- Units: 3.0
- Grading scale: Graded (A-F)
- Required or elective for MEM degree? Elective
- If elective, applicable elective track(s): Data Analytics and Machine Learning
- Pre-requisites: n/a
- Recommended previous courses: At least one undergraduate-level Statistics course and previous coding experience (not necessarily in Python).
Course Description/Synopsis (from DukeHub)
This course prepares students for transitioning to industry data science practitioners by focusing on learning-by-doing. Students gain hands-on experience applying statistical and machine learning techniques using real world data and creating data science solutions through Python and other popular open source tools in the era of big data. In addition, the course covers lectures on a number of intermediate data science topics such as supervised learning, unsupervised learning, ensemble learning, model optimization, text analytics, and data visualization.
Course Syllabus (Previous)
EGRMGMT 586.01 Syllabus, Spring 2021
A Word From the Faculty
Previous Course Evaluations
- Spring 2021
