Carnegie Mellon University

Essential Mathematics and Statistics For Scientists

Course Number: 02-680

This course rigorously introduces fundamental topics in mathematics and statistics to first-year master’s students.  It directly prepares students for 02-620 (Machine Learning for Scientists) and  gives students the quantitative foundation needed for advanced courses that apply concepts in machine learning to scientific datasets, such as 02-710 (Computational Genomics) and 02-750 (Automation of Biological Research).


Semester(s): Fall
Units: 9
Prerequisite(s): There are no formal prerequisites. However, we expect that students will have a strong foundation in high school mathematics (including calculus) and possess strong quantitative reasoning skills, as the course will be taught at a high level and proceed quickly.

Learning Objectives

Students completing this course will obtain a broad skillset of mathematical techniques and statistical inference as well as a deep understanding of mathematical proof. They will have the quantitative foundation to immediately step into an introductory master’s level machine learning or automation course.

Assessment Structure: 

Homework assignments (40% of grade) Written homework assignments will test your knowledge of the material covered in class.

Attendance and participation (10% of grade) Attendance will be taken, and we will have occasional in-class exercises that serve to reinforce the concepts we have covered. These exercises will not be graded, but participation will be expected in order to receive a complete grade for that day. You are allowed three “dropped” attendance grades without penalty. These can be used for any purpose.

Examinations (50% of grade)  Two midterms test knowledge of the material from the class.