Course description: We will cover basics of probability and statistics with applications. A public domain statistical package R will be routinely used as a computational tool — this is our laboratory instrument (I will teach you how to program in R). Use of the personal laptop in the classroom is desirable (almost a must), but use it for statistics purpose only. Statistics applications by your own will be presented in the team projects (themes will be provided by the instructor, your owns are welcome). All exams involve programming and are take home exams (24 hours for completion, submitted electronically).

This course, unlike other courses in statistics on campus, is intended to look at what is “under the hood.” Statistics as a rigorous science.

Three components:
1. Theory.
2. Statistical computations in R.
3. Analysis of real life data/statistical applications.

Instructor: Prof. Eugene Demidenko

This course is required for “Mathematical Data Science” major. Other required courses: MATH 20, MATH 50, and MATH 70.

We will be using canvas on the regular basis (visit often).

Class meetings: MWF 12:50–1:55; X-hour: Tuesday 1:20–2:10 PM.

Textbook: Demidenko E. “Advanced Statistics with Applications in R”. Hoboken, NJ: Wiley.

Additional reading: Michael Lavine “Introduction to Statistical Thought”, provided electronically on canvas.