Spring 2019 Course Syllabus:  Math 478/678

 

Course Title:

Introduction to Statistical Methods in Data Science

Textbook:

An Introduction to Statistical Learning: with Applications in R,  by Gareth James, et al.; Publisher: Springer, 1st edition (2013); ISBN: 978-1461471370.

Reference book:

The Elements of Statistical Learning: Data Mining, Inference, and Prediction,  by Hastie, Tibshirani, and Friedman; Publisher: Springer, 2nd edition (2009); ISBN: 978-0387848570.

Prerequisites:

Math 333 with a grade of C or better or Math 341 with a grade of C or better

 

 

Course Outline

Date

Lecture

Sections

Topic

Assignment

Week 1

1/24

1

 

Chapter 1

 

Introduction to Data Science
 

 

Week 2

1/28

2

Chapter 2

Statistical Learning

Homework 1

Week 3

2/4

3

Chapter 3

 

KNN; R Lab
 

 

Week 4

2/11

4

Chapter 3

Linear Regression; R Lab

Homework 2

Week 5

2/18

5

Chapter 4

Logistic Regression; R Lab

 

Week 6

2/25

6

Chapter 4

Linear Discriminant Analysis; R Lab
 

Homework 3

Week 7

3/4

 

 Chapter 5

Cross-Validation

MIDTERM EXAM:

Thursday ~ March 7, 2019

 

Week 8

3/11

7

Chapter 5

The Bootstrap; R Lab
 

 

Week 9

3/18

 

 

SPRING RECESS (NO CLASSES)

 

 

Week 10

3/25

8

Chapter 6

Variable Selection; R Lab

Homework 4

 

Week 11

4/1

9

Chapter 6

Regularization; R Lab

 

Week 12

4/8

10

Chapter 7

Non-linear Modeling; R Lab

 Homework 5

Week 13

4/15

11

Chapter 8

CART with R; Random Forest and Boosting

 

Week 14

4/22

12

Chapter 9

Support Vector Machines; R Lab

 Homework 6

Week 15

4/29

13

Chapter 10

Clustering Methods; R Lab

 

Week 16

5/6

 

 

Review for Final Exam

Reading Day 2 ~ May 9, 2019

 

Week 17

5/13

 

 

FINAL EXAM:

Monday ~ May 13, 2019

 

 

 

IMPORTANT DATES

FIRST DAY OF SEMESTER

January 22, 2019

LAST DAY TO WITHDRAW

April 8, 2019

LAST DAY OF CLASSES

May 7, 2019

FINAL EXAM PERIOD

May 10 – 16, 2019

 

Grading Policy

 

Assignment Weighting

 

Tentative Grading Scale

Homework

30 %

 

A

90 -- 100

Midterm Exam

30 %

 

B+

85 -- 89

Final Exam

40 %

 

B

80 -- 84

 

 

 

C+

70 -- 79

     

C

60 -- 69

 

 

 

F

0 -- 59

 

 

 

Important Departmental and University Policies

 

Prepared by Prof. Wenge Guo, January 10, 2019