Southern Utah University

Course Syllabus

Southern Utah University
Southern Utah University
Spring Semester 2026

Fundamentals of Data Science (Face-to-Face)

MATH 3190-01

Course: MATH 3190-01
Credits: 3
Term: Spring Semester 2026
Department: MATH
CRN: 11315

Course Description

This class will be an introduction to the mathematics and algorithms underlying the analytic techniques of data science. Topics covered will include regression and cross validation, gradient descent, dimension reduction, clustering and classification. Database management will also be discussed. Students will use a programming language and GitHub throughout this course. (Spring - Even Years) [Graded (Standard Letter)] Prerequisite(s): (MATH 2140 or MATH 3150 or MATH 3700) and (MATH 2170 or MATH 2270) and a working knowledge of a programming language - Prerequisite Min. Grade: C

Required Texts

There is no required textbook. The course notes are comprehensive. Here are some optional and recommended resources, each of which is available online for free:

An Introduction to Statistical Learning with Applications in R, 2nd edition, By Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, New York: Springer, 2013.

Modern Data Science with R, 2nd edition, By Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton, Chapman and Hall/CRC, 2021.

Introduction to Data Science: Data Analysis and Prediction Algorithms with R, 1st edition, By Rafael A. Irizarry, Chapman and Hall/CRC, 2020. https://rafalab.dfci.harvard.edu/dsbook

Learning Outcomes

This class serves as an introduction to some of the modern tools and algorithms for data science. Students will use and develop competency with the statistical software package R using Quarto in either R Studio or Positron and they will demonstrate proficiency in the basics of Unix, git and GitHub for archiving assignments. In addition, students will demonstrate proficiency in using SQL and in verification methods, optimization algorithms, advanced regression techniques, dimension reduction methods, Bayesian modeling, classification algorithms, and time series analysis. Students will  be able to apply these statistical learning methods and tools on real-world data sets.

Course Requirements

Homework/Submission Comment/Labs (20% of final grade): Please take the homework seriously and think of each one as writing a report. There will be typically be, on average, a little less than one homework assignment per week. You may work with classmates on homework assignments, but you must create your own solutions in your own words. Most homework should be completed using Quarto. You’ll submit your homework on GitHub and the outputted html file on Canvas (to unlock the solutions). The lowest homework score will be dropped.

In addition to the written homework submission, you will also be required to submit a self-critique comment. The idea is that you should submit the assignment, thoroughly look over the solutions, and then indicate in the comment anything you noticed that was incorrect or anything you have questions about. The homework is not considered submitted until you leave your comment.

Labs are assignments done (or at least started) in class. You will be expected to finish the lab before the next class period in most cases. There will not be quizzes on lab assignments.

Late homework will generally be accepted up to one day late, but at a 50% grade reduction.


Quizzes (50% of final grade): After nearly all homework assignments, there will be a quiz for you to take in the testing center. The quiz will feature a few questions similar to those you had to answer on the homework for that week. If you understand the homework, these quizzes should go smoothly. These quizzes will not focus on coding and will instead focus on interpretation, reading R code output, and occasionally answering more theoretical questions that you will explore in the homework.


Final Project (30% of final grade): There will be a group project toward the end of the semester where each group must research a data science/machine learning algorithm, learn the math and theory of it, apply it to a data set, write a report, and give a presentation to the class. Presentations will take place during the final period and the two or three classes period before, depending on the number of students enrolled in the class. Presentations should be about 40 minutes in length (this may change depending on the number of students in the class). Collaboration should take place through GitHub. More information on the project will come later in the semester.

Course Outline

This outline is tentative.

Week 1: Introduction and intro to unix, git, and GitHub
Week 2: Git and GitHub Lab and Intro to R
Week 3: R Lab and intro to Quarto
Week 4: Quarto and databases 
Week 5: SQL and verification methods
Week 6: Verification and resampling methods
Week 7: Optimization
Week 8: Optimization and advanced regression
Week 9: Advanced regression and dimension reduction
Week 10: Spring break
Week 11: Dimension reduction and Bayesian methods
Week 12: Bayesian methods
Week 13: Classification
Week 14: Time series
Week 15: Finish content and final project presentations
Week 16: Final project presentations

Instructor's policies on late assignments and/or makeup work

Homework may be submitted up to one day late, but at a 50% grade reduction.

Make-up quizzes can be arranged with the instructor for extenuating circumstances before the quiz closes. 

The final project is not accepted late. 

Attendance Policy

This class has a face-to-face designation and you are expected to attend class every day unless you are feeling ill. I will be recording class sessions and will post the videos on Canvas. Attendance is not counted toward your grade. 

Course Fees

There is a fee of $3.75 to take this course (one of the lowest fees on campus).

AI Statement

The goal of this class is for your to learn this material. If using artificial intelligence can help you learn, then you may use it. Do not use it as a crutch or use it to completely do your assignments. Always attempt each problem yourself first. Students who over-rely on AI tend to do significantly worse on quizzes and projects.

ADA Statement

Students with medical, psychological, learning, or other disabilities desiring academic adjustments, accommodations, or auxiliary aids will need to contact the Disability Resource Center, located in Room 206F of the Sharwan Smith Center or by phone at (435) 865-8042. The Disability Resource Center determines eligibility for and authorizes the provision of services.

If your instructor requires attendance, you may need to seek an ADA accommodation to request an exception to this attendance policy. Please contact the Disability Resource Center to determine what, if any, ADA accommodations are reasonable and appropriate.

Academic Credit

According to the federal definition of a Carnegie credit hour: A credit hour of work is the equivalent of approximately 60 minutes of class time or independent study work. A minimum of 45 hours of work by each student is required for each unit of credit. Credit is earned only when course requirements are met. One (1) credit hour is equivalent to 15 contact hours of lecture, discussion, testing, evaluation, or seminar, as well as 30 hours of student homework. An equivalent amount of work is expected for laboratory work, internships, practica, studio, and other academic work leading to the awarding of credit hours. Credit granted for individual courses, labs, or studio classes ranges from 0.5 to 15 credit hours per semester.

Academic Freedom

SUU is operated for the common good of the greater community it serves. The common good depends upon the free search for truth and its free exposition. Academic Freedom is the right of faculty to study, discuss, investigate, teach, and publish. Academic Freedom is essential to these purposes and applies to both teaching and research.

Academic Freedom in the realm of teaching is fundamental for the protection of the rights of the faculty member and of you, the student, with respect to the free pursuit of learning and discovery. Faculty members possess the right to full freedom in the classroom in discussing their subjects. They may present any controversial material relevant to their courses and their intended learning outcomes, but they shall take care not to introduce into their teaching controversial materials which have no relation to the subject being taught or the intended learning outcomes for the course.

As such, students enrolled in any course at SUU may encounter topics, perspectives, and ideas that are unfamiliar or controversial, with the educational intent of providing a meaningful learning environment that fosters your growth and development. These parameters related to Academic Freedom are included in SUU Policy 6.6.

Academic Misconduct

Scholastic honesty is expected of all students. Dishonesty will not be tolerated and will be prosecuted to the fullest extent (see SUU Policy 6.33). You are expected to have read and understood the current SUU student conduct code (SUU Policy 11.2) regarding student responsibilities and rights, the intellectual property policy (SUU Policy 5.52), information about procedures, and what constitutes acceptable behavior.

Please Note: The use of websites or services that sell essays is a violation of these policies; likewise, the use of websites or services that provide answers to assignments, quizzes, or tests is also a violation of these policies. Regarding the use of Generative Artificial Intelligence (AI), you should check with your individual course instructor.

Emergency Management Statement

In case of an emergency, the University's Emergency Notification System (ENS) will be activated. Students are encouraged to maintain updated contact information using the link on the homepage of the mySUU portal. In addition, students are encouraged to familiarize themselves with the Emergency Response Protocols posted in each classroom. Detailed information about the University's emergency management plan can be found at https://www.suu.edu/emergency.

HEOA Compliance Statement

For a full set of Higher Education Opportunity Act (HEOA) compliance statements, please visit https://www.suu.edu/heoa. The sharing of copyrighted material through peer-to-peer (P2P) file sharing, except as provided under U.S. copyright law, is prohibited by law; additional information can be found at https://my.suu.edu/help/article/1096/heoa-compliance-plan.

You are also expected to comply with policies regarding intellectual property (SUU Policy 5.52) and copyright (SUU Policy 5.54).

Mandatory Reporting

University policy (SUU Policy 5.60) requires instructors to report disclosures received from students that indicate they have been subjected to sexual misconduct/harassment. The University defines sexual harassment consistent with Federal Regulations (34 C.F.R. Part 106, Subpart D) to include quid pro quo, hostile environment harassment, sexual assault, dating violence, domestic violence, and stalking. When students communicate this information to an instructor in-person, by email, or within writing assignments, the instructor will report that to the Title IX Coordinator to ensure students receive support from the Title IX Office. A reporting form is available at https://cm.maxient.com/reportingform.php?SouthernUtahUniv

Non-Discrimination Statement

SUU is committed to fostering an inclusive community of lifelong learners and believes our university's encompassing of different views, beliefs, and identities makes us stronger, more innovative, and better prepared for the global society.

SUU does not discriminate on the basis of race, religion, color, national origin, citizenship, sex (including sex discrimination and sexual harassment), sexual orientation, gender identity, age, ancestry, disability status, pregnancy, pregnancy-related conditions, genetic information, military status, veteran status, or other bases protected by applicable law in employment, treatment, admission, access to educational programs and activities, or other University benefits or services.

SUU strives to cultivate a campus environment that encourages freedom of expression from diverse viewpoints. We encourage all to dialogue within a spirit of respect, civility, and decency.

For additional information on non-discrimination, please see SUU Policy 5.27 and/or visit https://www.suu.edu/nondiscrimination.

Pregnancy

Students who are or become pregnant during this course may receive reasonable modifications to facilitate continued access and participation in the course. Pregnancy and related conditions are broadly defined to include pregnancy, childbirth, termination of pregnancy, lactation, related medical conditions, and recovery. To obtain reasonable modifications, please make a request to title9@suu.edu. To learn more visit: https://www.suu.edu/titleix/pregnancy.html.

Disclaimer Statement

Information contained in this syllabus, other than the grading, late assignments, makeup work, and attendance policies, may be subject to change with advance notice, as deemed appropriate by the instructor.