SML 201 is an introduction to the burgeoning field of data science, which is primarily concerned with data-driven discovery and utilizing data as a research and technology development tool. We cover approaches and techniques for obtaining, organizing, exploring, and analyzing data, as well as creating tools based on data. Elements of statistics, machine learning, and statistical computing form the basis of the course content. We consider applications in the natural sciences, social sciences, and engineering.

A detailed syllabus, which includes grading and administrative detials, can be found on the course Blackboard site.

This web site contains links and information on the text books and software that we will use, course materials, the schedule, and additional resources you may find useful.

We will also be posting the lecture notes and precept materials here. The primary content of this course is under version control and openly available at our GitHub site.