Course Detail (Course Description By Faculty)

This course is an introduction to the fundamentals of probability and statistics with an aim towards building foundational skills in modern data science. Topics to be covered include 1) Exploratory data analysis and descriptive statistics, 2) Basic probability, common pitfalls and fallacies, 3) Statistical modeling, inference, p-values, and A/B testing, and 4) Prediction, regression, and classification. Emphasis will be placed on ethics and privacy in data analysis as well as real-world applications and case studies.

None.

Although there is no official textbook for the class, we will use the following free online reference:
 *** OpenIntro Statistics (free online resource https://www.openintro.org/book/os/).

All lecture notes and course materials will be available in the class website. In addition, these references may
help you as a complement to the class:

1. Statistics for Business: Decision Making and Analysis, by Robert Stine and Dean Foster
2. Introductory Statistics, by Robert Gould and Colleen N. Ryan
3. Naked Statistics, by Charles Wheelan

No pass/fail grades. No auditors.

Course grades will be based on the following:

1. Homework - 20%

2. Midterms - 30%

3. Finals - 50%

The Midterm and Final exams will be in-person. It is expected and recommended that students complete all homeworks. There will be 4 homeworks (roughly 1 HW every 2 weeks).
Please do not make any travel plans on the specified dates of the final. No make up exams will be offered for any reason.

Restrictions