Southern Utah University

Course Syllabus

Southern Utah University
Southern Utah University
Spring Semester 2026

Big Data Analytics (Face-to-Face)

CS 4200-01

Course: CS 4200-01
Credits: 3
Term: Spring Semester 2026
Department: CSIS
CRN: 10549

Course Description

This course will introduce fundamental concepts in the field of data analysis, along with some widely used techniques and tools. Students will have the chance to delve into and analyze substantial amounts of observational data to uncover significant patterns and valuable insights that can aid decision-making in various contexts. (Spring - Even Years) [Graded (Standard Letter)] Prerequisite(s): CSCY 3200 or ANLY 3250 - Prerequisite Min. Grade: C-

Required Texts

  The textbook for this course is an Inclusive Access Textbook. You already have purchased this text through your student fees and it is required for this course. You are not allowed to opt out of this product as it has required software for this course. Jamsa, Kris(2021). Navigate Premier Access for Introduction to Data Mining and Analytics (1st ed.). Jones and Bartlett Learning. ISBN:  9781284180923. 

Learning Outcomes

 

By their efforts in this course, students should improve in the following course learning outcomes from each chapter: 
Chapter 1: Define and describe the following: data mining, machine learning, data visualization, data quality, clustering, classification, predictive analytics.
Chapter 2: Perform key data-mining and machine-learning operations. Compare and contrast supervised and unsupervised learning. Compare and contrast training and testing data sets. Define and describe dimensionality reduction. Define and describe primary-component analysis. Know when and how to apply data-set standard scaling.


Chapter 3: Define database and describe the role of databases in data analytics. Create an entity relationship diagram (ERD) that represents entities and their relationships. Compare and contrast the conceptual, logical, and physical data models. Compare and contrast databases, data warehouses, data marts, and data lakes. Explain the purpose of data normalization and understand the processing required to achieve third-normal form (3NF). Compare and contrast relational, NoSQL, object-oriented, and graph databases.

Chapter 4: Define and describe data visualization. Compare and contrast chart types and the appropriate use of each. Create a variety of charts using Excel. Create HTML-based charts on the web. Use best practices when creating charts.

Chapter 5: Sort and filter data using Excel. Create charts to visualize data using Excel.
Apply conditional formatting to highlight key values. Compare and contrast spreadsheet file formats. Use pivot tables to analyze data and to produce reports. Perform “what if” processing within Excel.

Chapter 6: Define and describe the components of a relational database. Compare and contrast DCL, DDL, and DML queries. Perform complex SQL queries. Compare and contrast SQL JOIN operations. Use SQL aggregation functions and query techniques to group data for reporting.

Chapter 7: Compare and contrast relational and NoSQL databases. Compare and contrast NoSQL database management systems. Perform NoSQL query operations. Understand the role of JSON within NoSQL solutions. Define and describe managed database services.

Chapter 8: Use Python to perform common machine-learning and data-mining operations. Use R to perform common machine-learning and data-mining operations. Compare and contrast Python and R solutions.

Chapter 9: Use Python to perform common machine-learning and data-mining operations. Use R to perform common machine-learning and data-mining operations. Compare and contrast Python and R solutions.

Chapter 10: Define and describe data clustering. Compare and contrast hard and soft clustering. Compare and contrast different clustering algorithms. Describe the purpose of a dendrogram. Visually represent cluster assignments.

Chapter 11: Define and describe data classification. Compare and contrast binary and multiclass classification. Compare and contrast classification algorithms. Define and describe the role of training and testing data sets. Describe the steps to perform the classification process.

Chapter 12: Define and describe predictive analysis. Compare and contrast predictive and prescriptive analysis. Define and describe the regression process. Define and describe regression techniques. Compare and contrast regression algorithms.

Chapter 13: Define and describe data association. Define and describe market-basket analysis. Define and describe support, confidence, conviction, and lift. Use visual programming to implement machine-learning and data-mining solutions.

Chapter 14: Perform text sentiment analysis and categorization. Perform facial recognition. Perform image classification. Understand that text and image mining use the same data-mining techniques you have used throughout this text.

Chapter 15: Perform text sentiment analysis and categorization. Perform facial recognition. Perform image classification. Understand that text and image mining use the same data-mining techniques you have used throughout this text.

Chapter 16: Define and describe data governance. Describe and calculate a return on investment (ROI). Describe and perform a SWOT analysis. Define and describe the PDCA process.

 

Course Requirements

 

Your final grade will be determined as follows: 40% Labs  10% Discussion  50% Exams Letter grades will be assigned approximately as indicated in the Grading Scale. A grade of UW (unofficial withdrawal) will be assigned to students who have no course engagement after the official withdrawal deadline for this course or do not complete the course.


Labs
40%


Discussions
10%


Exams
50%



Grading Scale:           


                    A     93%                              B-    80%                          D+  67%


                 
  A-    90%                              C+   77%                          D    63%


                    B+   87%                              C      73%                          D-  60%


                    B      83%                              C-    70%                          F    Below 60%


 

Course Outline

 

Tentative Schedule:


Monday | Wednesday
1/5 – No Class  | 1/7 – Syllabus, 1.1
1/12 – 1.2,  Lab Time  | 1/14 –  2
1/19 – No Class | 1/21 – Chapter 3.1, 3.2 
1/26 –  4.1, Lab Time | 1/28 – 4.2 4.3
2/2 – 5.1, 5.2 | 2/4 –  6.1 Lab Time 
2/9 – 6.2 6.3   | 2/11 – 7.1 Lab Time
2/16 – 7.2 Lab Time  | 2/18 – 8.1 8.2 
2/23 – Review  | 2/25 – 9.1 9.2
3/2 – 10 Lab Time | 3/4 – 11.1 11.2
3/9 – No Class | 3/11 – No Class
3/16 – 12.1 12.2 | 3/18 – 13 Lab Time
3/23 – 14.1 14.2 | 3/25 – 15.1 Lab Time
3/30 – 15.2 16.1  | 4/1 – 16.2 Lab Time
4/6 – 16 Applications   | 4/8 – Lab Day
4/13 – Final Review pt. 1 | 4/15 – Final Review pt. 2
4/20 – Finals | 4/22 – Finals 

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

All Assignments will be accepted up to 10 days after their initial due date, with a 10% deduction each day. Quizzes, Projects, and Exams are not accepted late, unless given prior exception by the instructor. 

Attendance Policy

Attendance is expected but not required. If you miss class for any reason, you are responsible for the material and information, including announcements, presented during lecture. How to Best Succeed: Adequately prepared students should expect to spend a minimum of 9 hours per week working on this course. This involves attending all course lectures, engaging in online discussions, attending study sessions, completing homework and other assignments, seeking assistance from the instructor, tutors, or fellow classmates, and studying for and taking exams. This minimal time commitment will likely lead to a passing grade of a C. Much more time may be required to achieve a higher grade or for students with less than adequate preparation. Students have great flexibility in how they schedule their time toward this course. They are encouraged to utilize evenings and weekends if those time work best for their schedules, but be aware that many study resources may only be available during regular business hours. To maximize success in this course, each student is encouraged to create an individual weekly study schedule for this course, with specifics about days and times when one will attend class, complete lab assignments, participate in discussions, study, and take exams. Students should make concrete goals about viewing all lectures and completing all lab assignments and should stick with their goals. 

Course Fees

 There is an $11 per credit hour fee for this course. 

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.