Southern Utah University

Course Syllabus

Southern Utah University
Southern Utah University
Fall Semester 2025

Introduction to Data Science (Face-to-Face)

MATH 1190-01

Course: MATH 1190-01
Credits: 3
Term: Fall Semester 2025
Department: MATH
CRN: 31644

Course Description

This course introduces the analysis and visualization of data using a high-level computer programming language (e.g., R). It emphasizes conceptual understanding of the data science lifecycle, which includes data collection and organization, sampling and sampling bias, exploratory data analysis and visualization, statistical inference, and prediction. Inference and prediction topics include confidence intervals, hypothesis tests with one and two samples, chi-square tests, ANOVA, and linear regression. (Fall, Spring) [Graded (Standard Letter)] Prerequisite(s): MATH 1010 or MATH 1031 or adequate test score within the last two (2) years - Prerequisite Min Grade: C Is there a standardized test that serves as a prerequisite? If yes, what is the name of the test and minimum score? ACT Math Subscore (21) or ALEKS PPL (40)

Required Texts

Introduction to Data Science: Data Analysis and Prediction Algorithms with R., by Irizarry. This book is available for free here: https://rafalab.dfci.harvard.edu/dsbook/.

Learning Outcomes

This is a general education (GE) course. By the end of it students should demonstrate 
1. Communication (Students develop and express ideas and will be able to do so in a variety of ways, namely in writing, by speaking, visually, kinesthetically, through design or aurally).
2. Problem solving (Students design, evaluate, and implement strategies to answer open-ended questions or achieve a desired goal).
3. Quantitative Literacy (Students can understand and create sophisticated arguments supported by quantitative evidence and clearly communicate those arguments in a variety of formats using words, tables, graphs, mathematical equations, etc., as appropriate).

Course Requirements

Homework (15% of final grade): Working hard on the homework is how you will succeed in this course, so please take the homework seriously. You may work with classmates on homework assignments, but you must write up your own solutions in your own words. There will be two components to your homework:
1. “Short” Homework Assignments: These will be assigned nearly every day after class and will focus on the material covered that day in class. These assignments will be due at the end of the next week day. You may either submit them to me or through Canvas. These assignments will be graded for completion, not for being correct. Once you submit the assignment, the solutions will become available to you. These are not accepted late, but the lowest four will be dropped.
2. “Long” Homework Assignments: These are longer homework assignments that focus on the material covered from a particular set of notes. These problems will also not be graded for correctness, but feedback will be provided. They will typically be due on Fridays. You can generally submit these assignments up to one day late, but at a 10% grade reduction.

Smaller Projects (10% of final grade): There will be two smaller projects assigned during the semester. The purpose of these projects is to allow you to have some “hands-on” experience with data analysis, collection, and/or other statistical considerations.

R Quiz (5% of final grade): Not long after we finish Notes 4, we will have an in-class quiz on R that involves doing some important R operations that we have learned in class. This will be open note.

Midterms (36% of final grade): There will be two midterm exams in the testing center. You will be allowed to bring a sheet of notes for these tests and you can use R on them.

Final Exam (21% of final grade): There will be a “semi-cumulative” Final Exam given in the testing center during finals week. You are also allowed a sheet of notes on the final. The final exam is required, but the score on the final will replace your lower midterm score if you do better on the final.

Final Project (13% of final grade): There will be a project toward the end of the summer session where each student must use inferential statistics to answer questions about the data they collect during the semester. More information on the project will come later.

Course Outline

The outline below is tentative:

Week 1: Introduction to statistics and data science
Week 2: More introduction and intro to R
Week 3: R basics 
Week 4: More R and descriptive statistics
Week 5: More descriptive statistics and Project 1
Week 6: Data visualization in R
Week 7: Intro to probability and Exam 1
Week 8: Fall break, more probability, and the R Quiz
Week 9: Normal variables and the CLT
Week 10: Confidence intervals and hypothesis tests for one parameters and Project 2
Week 11: Exam 2 and more statistical inference
Week 12: Confidence intervals and hypothesis tests for multiple parameters
Week 13: Final statistical inference and correlation
Week 14: Thanksgiving break
Week 15: Regression
Week 16: Finals: Final Exam and Final Project

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

Short homework assignment are not accepted late.

Long homework assignments are accepted up to one day late, but with a 10% grade penalty. 

Make-ups for tests will be given under special and extenuating circumstances such as a family emergency or illness. It is your responsibility to notify me as soon as you know that you will miss any test and to make sure that a make-up is scheduled. If you are unable to contact me in person, then please send an email so that it will be convenient to get back to you in order to schedule a make-up. Early final exams will not be given.

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.

Course Fees

There is a fee of $3.75 to take this course.

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 on homework assignments. 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 the R Quiz, exams, 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.