Math and Statistics Foundations for Data Science
(데이터사이언스를 위한 수학과 통계의 기초)

Instructor: Seunggeun Lee (lee7801@snu.ac.kr)

Goals

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

Content

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)

Textbook

All of Statistics by Larry Wasserman, Springer 2004

Grading Policy

  • · Attendance: 5%

  • · Task: 40%

  • · Midterm: 25%

  • · Final: 30%