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.
At a Glance
- Instructor(s): (Louis) Daniel Egger
- Semester(s) typically taught: Fall
- 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: n/a
Course Description/Synopsis (from DukeHub)
In this course, students will learn the fundamentals of data science, including core technical vocabulary and mathematical concepts. This will include topics such as (i) probability through Bayesian techniques; (ii) binary classification; (iii) linear regression for forecasting; (iv) Information measures used in data science, including mutual information, relative entropy (KL divergence), and log loss (cross entropy), (v) Experimental design; and (vi) the roles of training and test data, using Hoeffding’s inequality to forecast error rates. Students will apply the above concepts to real-world data, while developing their own models for probabilistic forecasting.
Course Syllabus (Previous)
EGRMGMT 585.01 Syllabus, Fall 2021
A Class Sample
A Word From the Faculty/TA
Please check back for this at a later time.
Student Testimonials (from Course Evaluations)
- “Super useful content! I learned a lot of machine learning vocabulary and basic understanding in this domain.”
- “Prof Egger is a good teacher who knows a lot and has a good command of knowledge in the machine learning domain. Though sometimes he will make small mistakes in class, he is amicable, humorous, and open to questions.”
- “Learned new programming skills, how to use Python Jupiter Notebook, and found Pandas very useful and efficient in data analytics.”
- “The ideas, conceptions on the class are illustrated very clearly and are very useful for us to know about data science.”
Previous Course Evaluations
- Fall 2021
- Fall 2022
