Statistics for Data Science (데이터사이언스를 위한 확률과 통계)
Spring 2021 (Tue/Thur 12:30~13:45)
Instructor: Seunggeun Lee (lee7801@snu.ac.kr)
Overview
All areas of data science are concerned with collecting and analyzing data. This course is designed to provide foundations of probability and statistics. From this course, student will understand how probability and statistics explain the data generating process and can be used to analyze data.
Definition of Probability
Random Variables
Expectation , Convergence of Random Variables
Statistical Inference
Parametric and non-parametric method (such as Bootstrap)
Hypothesis test
Bayesian Inference
Course Materials
All of Statistics by Larry Wasserman, Springer 2004
Syllabus
01. Course introduction, Intro Data
02. Probability
03. Random Variable (1)
04. Random Variable (2), Expectation (1)
05. Expectation (2)
06. Convergence
07. Introduction of the Inference, CDF
08. Bootstrap
09. Parametric Inference (1)
10. Parametric Inference (2)
11. Hypothesis Test (1)
12. Hypothesis Test (2), Bayesian Inference (1)
13. Bayesian inference (2)
14. Regression (1)
15. Regression (2)
Grading Policy
Attendance: 5%
Task: 40%
Midterm: 25%
Final: 30%