Math 408: Advance Data Analysis

Course Information

Term: Spring 2023

Instructor: Isaac Quintanilla Salinas


Office Location: BTE 2840

Office Hours:

OH Course Other R Programming
Day MW Thur Fri
Location BTE 2840 Look for Group BTE 2810
Time 2-3 PM 2-3 PM 9 AM - 11 AM

Or by Zoom appointment:

Lecture: Monday and Wednesday 3:00-4:15 PM in BT 1462

Course Description

Introduction to data management, regression, and machine learning. Bayesian methods, multivariate data, multivariate normal distribution, multivariate regression, principal components, factor, canonical correlation, discriminant analyses, and clustering. Extensive use of appropriate statistical and programming software.

Learning Outcomes

  • Prepare students for advanced courses in data-management, machine learning, and statistics, by providing the necessary foundation and context
  • Enable students to start careers as data scientists by providing experience working with real-world data, tools, and techniques
  • Empower students to apply computational and inferential thinking to address real-world problems

Required Texts

  • Generalized Linear Models With Examples in R (GLM)
    • Peter Dunn & and Gordon Smyth
    • Available to Download from the Broome Library
  • An Introduction to Statistical Learning (SL)
    • Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
    • Available to Download from the Broome Library
  • Statistical Computing (SC)

Required Software

For this course, we will use R, Quarto, and RStudio. Please download and install on your computer.

  • R is a free statistical software program that is available for download at:

  • R Notebooks is a interactive RMD file that can be used to provide reproducible code and documents. You can learn more about it here

  • RStudio provides free and open source tools for your data analysis in R:

  • INQS Tools is my personal R package that will contain templates to submit assignments for class: INQS Tools

Course Grading

Category Percentage
Homework 25%
Exam 1 25%
Exam 2 25%
Exam 3 25%

At the end of the quarter, course grades will be assigned according to the following scale:

A+ 98 – 100 B+ 87 – <90 C+ 77 – <80 D+ 67 – <70
A 93 – <98 B 83 – <87 C 73 – <77 D 63 – <67 F < 60
A– 90 - <93 B- 80 – <83 C– 70 – <73 D– 60 – <63


Homework will be assigned on a regular basis and posted on and CANVAS. The homework is to help you practice the concepts learned in lecture and to help you study. You must turn in your own individual homework and show your understanding of the material. At the end of the semester, the two lowest homework grades will be dropped. Late work will be accepted, but with a 10 point penalty. The last day late work will be accepted is on 5/14/2023 at 11:59 PM.


There will be three exams. Exam #1 will most likely be during the 6th week of the semester. Exam #2 will most likely be during the 11th week of the semester. Exam #3 will be on finals week. While the exams are not considered cumulative, the material builds on each other. Developing a strong understanding of the material through out the course is important for your success. At the end of the semester, your lowest exam grade will be replaced by your median average exam grade. This course will operate under a zero-tolerance policy. Talking during the time of the exam, sharing materials, looking at another students’ exam, or not following directions given will be subject to the University’s academic integrity policy.

Extra Credit

There will be 6 extra credit opportunities worth a total of 10% of your overall grade. (There are no make-ups for missed extra credit assignments!) More information will be provided on the extra credit assignments on a later date. Information on the extra credit can be found here.

Class Schedule

The following outline may be subject to change. Any changes will be announced in class.

Week Topic Reading Assignment/Exam
1/23-1/27 Intro to Course/Intro to R/Notebooks SC: Ch 1
1/30-2/3 Control Flow SC: Ch 2 HW #1
2/6-2/10 Control Flow/Functional Programming SC: Ch 13 HW #2
2/13-2/17 Functional Programming SC: Ch 15 HW #3
2/20-9/24 Data Manipulation SC: Ch 3 Exam #1
2/27-3/3 Linear Regression GLM: Ch 2 HW #4
3/3-3/10 Multivariable Linear Regression GLM: Ch 2 HW #5
3/13-3/17 Model Development GLM: Ch 3
3/20-3/24 Spring Break
3/27-3/31 Generalized Linear Models GLM: Ch 5 HW #6
4/3-4/7 Model Inference GLM: Ch 6-7 Exam #2
4/10-4/14 Intro to Statistical Learning SL: Ch 2 HW # 7
4/17-4/21 Classifications SL: Ch 4 HW # 8
4/24-4/28 Tree-Based Methods SL: Ch 8 HW # 9
5/1-5/5 Support Vector Machines SL: Ch 9 HW # 10
5/8-5/12 Deep Learning SL: Ch 10
5/15-5/19 Exam 3

University Policies

  1. Academic Honesty:

    Please conduct yourself with honesty and integrity. Do not submit others’ work as your own. For assignments and quizzes that allow you to work with a group, only put your name on what the group submits if you genuinely contributed to the work. Work completely independently on exams, using only the materials that are indicated as allowed. Failure to observe academic honesty results in substantial penalties that can include failing the course.

  2. Disabilities:

    If you are a student with a disability requesting reasonable accommodations in this course, you need to contact Disability Accommodations and Support Services (DASS) located on the second floor of Arroyo Hall, via email or call 805-437-3331. All requests for reasonable accommodations require registration with DASS in advance of need: Faculty, students and DASS will work together regarding classroom accommodations. You are encouraged to discuss approved.

  3. Emergency Procedure Notice to Students:

    CSUCI is following guidelines and public orders from the California Department of Public Health and Ventura County Public Health for the COVID-19 pandemic as it pertains to CSUCI students, employees and visitors on the campus. Students are expected to adhere to all health and safety requirements as noted on the University’s Spring 2023 Semester website or they may be subject to removal from the classroom.