Syllabi Header

Math 678: Introduction to Statistical Methods in Data Science
Spring 2019 Course Syllabus

NJIT Academic Integrity Code: All Students should be aware that the Department of Mathematical Sciences takes the University Code on Academic Integrity at NJIT very seriously and enforces it strictly. This means that there must not be any forms of plagiarism, i.e., copying of homework, class projects, or lab assignments, or any form of cheating in quizzes and exams. Under the University Code on Academic Integrity, students are obligated to report any such activities to the Instructor.

Course Information

Course Description: This course introduces to students concepts in statistical methods used in data science, including data collection, data visualization and data analysis. Emphasis is on model building and statistical concepts related to data analysis methods. The course provides the basic foundational tools on which to pursue statistics, data analysis and data science in greater depth. Topics include sampling and experimental design, understanding the aims of a study, principles of data analysis, linear and logistic regression, resampling methods, and statistical learning methods. Students will use the R statistical software.

Number of Credits: 3

Prerequisites: MATH 333 with a grade of C or better or MATH 341 with a grade of C or better

Course-Section and Instructors

Course-Section Instructor
Math 6678-002 Professor W. Guo

Office Hours for All Math Instructors: Spring 2019 Office Hours and Emails

Required Textbook:

Title An Introduction to Statistical Learning: with Applications in R
Author Gareth James, et al.
Edition 1st (2013 ed.)
Publisher Springer
ISBN # 978-1461471370
Reference The Elements of Statistical Learning: Data Mining, Inference, and Prediction, by Hastie, Tibshirani, and Friedman; Publisher: Springer, 2nd edition (2009); ISBN: 978-0387848570.

University-wide Withdrawal Date: The last day to withdraw with a W is Monday, April 8, 2019. It will be strictly enforced.


DMS Course Policies: All DMS students must familiarize themselves with, and adhere to, the Department of Mathematical Sciences Course Policies, in addition to official university-wide policies. DMS takes these policies very seriously and enforces them strictly.

Grading Policy: The final grade in this course will be determined as follows:

Homework 30%
Midterm Exam 30%
Final Exam 40%

Your final letter grade will be based on the following tentative curve.

A 90 - 100 C+ 70 - 79
B+ 85 - 89 C 60 - 69
B 80 – 84 F 0 - 59

Attendance Policy: Attendance at all classes will be recorded and is mandatory. Please make sure you read and fully understand the Math Department’s Attendance Policy. This policy will be strictly enforced.

Exams: There will be one midterm exam held in class during the semester and one comprehensive final exam. Exams are held on the following days:

Midterm Exam March 7, 2019
Final Exam Period May 10 - 16, 2019

The final exam will test your knowledge of all the course material taught in the entire course. Make sure you read and fully understand the Math Department's Examination Policy. This policy will be strictly enforced.

Makeup Exam Policy: There will be No make-up QUIZZES OR EXAMS during the semester. In the event an exam is not taken under rare circumstances where the student has a legitimate reason for missing the exam, the student should contact the Dean of Students office and present written verifiable proof of the reason for missing the exam, e.g., a doctor’s note, police report, court notice, etc. clearly stating the date AND time of the mitigating problem. The student must also notify the Math Department Office/Instructor that the exam will be missed.

Cellular Phones: All cellular phones and other electronic devices must be switched off during all class times.

Additional Resources

Math Tutoring Center: Located in the Central King Building, Lower Level, Rm. G11 (See: Spring 2019 Hours)

Further Assistance: For further questions, students should contact their instructor. All instructors have regular office hours during the week. These office hours are listed on the Math Department's webpage for Instructor Office Hours and Emails.

All students must familiarize themselves with and adhere to the Department of Mathematical Sciences Course Policies, in addition to official university-wide policies. The Department of Mathematical Sciences takes these policies very seriously and enforces them strictly.

Accommodation of Disabilities: Disability Support Services (DSS) offers long term and temporary accommodations for undergraduate, graduate and visiting students at NJIT.

If you are in need of accommodations due to a disability please contact Chantonette Lyles, Associate Director of Disability Support Services at 973-596-5417 or via email at The office is located in Fenster Hall Room 260. A Letter of Accommodation Eligibility from the Disability Support Services office authorizing your accommodations will be required.

For further information regarding self identification, the submission of medical documentation and additional support services provided please visit the Disability Support Services (DSS) website at:

Important Dates (See: Spring 2019 Academic Calendar, Registrar)

Date Day Event
January 22, 2019 T First Day of Classes
February 1, 2019 F Last Day to Add/Drop Classes
March 17 - 24, 2019 Su - Su Spring Recess - No Classes, NJIT Open
April 8, 2019 M Last Day to Withdraw
April 19, 2019 F Good Friday - No Classes, NJIT Closed
May 7, 2019 T Friday Classes Meet/ Last Day of Classes
May 8 & 9, 2019 W & R Reading Days
May 10 - 16, 2019 F - R Final Exam Period

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: Monday ~ March 7, 2019
Week 8 - 3/11 7 Chapter 5 The Bootstrap; R Lab
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

Updated by Professor W. Guo - 1/21/2019
Department of Mathematical Sciences Course Syllabus, Spring 2019