Southern Utah University

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Southern Utah University
Southern Utah University
Spring Semester 2026

Data Analytics I (Online)

ANLY 4100-31I

Course: ANLY 4100-31I
Credits: 3
Term: Spring Semester 2026
Department: MESA
CRN: 13743

Course Description

An introduction to data science methods in business, finance, and economics. Includes an introduction to an appropriate programming language for data manipulation and modeling. Provides an overview of descriptive, predictive, and prescriptive methods in data analytics. (As Needed) [Graded (Standard Letter)]

This course uses Python programming tool for data preparation and analysis. The following topics will be covered:

  • Constructing and loading a Data Frame.
  • Various techniques for describing data.
  • Using a Linear Regression model for regression tasks.
  • Applying a logistic regression model for classification tasks.
  • Utilizing the Naefve Bayesian classifier for classification tasks.
  • Implementing a Time-Series model.
  • Employing different techniques to evaluate model performance

Required Texts

Textbook

Required Course Textbook: None

Course Website and Materials

The course website will be on Canvas. Various files will be distributed via the class site (e.g., lecture notes, homework assignments and sample solutions, etc.), and the site will also be used for announcements and email communications. Basically, all materials (learning materials  lecture notes and lecture videos, datasets, quizzes, homework assignments and project work) and instructions will be available on Canvas.

Software

Course Software: Python (all available free of charge)

The primary software packages we will use for this class are Python, R, and Tableau.

Python: Students usually find it easiest to use Python through the Anaconda distribution: .

For this class we will use Google Collaboratory  a cloud-based tool

Learning Outcomes

Upon completion of this course, students will be able to:

  • Utilize the Python programming language to acquire data from different sources.
  • Use diverse data description techniques to understand the data numerically and visually.
  • Develop supervised machine learning models to recognize patterns and make intelligent use of large amounts of data.
  • Recognize various types of time series data and time series forecasting models.
  • Understand Bayes theorem and utilize the Naefve Bayes classification algorithm for classification task.
  • Optimize both the predictive and classification models using different techniques.
  • Carry out a full-scale, real-world data analytics project, including business summaries and presentation of results.

Course Requirements

Standard SUU Grading Scale

Please be advised that any course counting toward graduate work requires at least a C to qualify for credit. Any work below a C will automatically become an F when applied to a graduate degree.

FCC+B-BB+A-A
< 7474-7677-7980-8384-8687-8990-9394-100
Grading Policy

Your final numerical score for the semester will be determined by various deliverables according to the following system of weights:

CategoryFrequencyPercent of Total Grade
Participation ActivitiesOne/ Module5%
Reflection PostsOne/Module5%
QuizzesOne/Module20%
Homework AssignmentsOne/Module35%
Exams and project work35%
Mid-Term Exam
Final Exam
Final Project
Total:100%
Participation

There will be some participation activities in each module. This generally includes a topic of my choice from corresponding weeks course content. These participation activities may include a short answer question, calculation or coding problem. A list of topics will be available at the beginning of each module (Module starts on Monday) and you will have to answer these questions by the end of each module (Module ends on Sunday). Generally, this assignment will be locked after the due date therefore you wont be able to submit after the due date. Please dont email for the due date extension.

Reflection Posts

A pool of discussion topics will be provided, and you will have to choose one question/topic for the reflection post. In this post you will have to answer the question in detail. This post will be graded based on the quality of your response. Generally, this assignment will be locked after the due date therefore you wont be able to submit it after the due date. Please dont email for the due date extension.

Homework

There will be approximately 7 homework assignments throughout the course of the semester. Each individual assignment typically consists of a set of problems requiring the application of programming skills, software familiarity, and data analysis.

While it is acceptable to discuss aspects of the homework and solution strategy with others, your submission should represent your own work. For instance, it would be acceptable to ask for help if you keep getting an error message in Python, but it would be unacceptable to take someone elses Python code, run it, and report the results.

Sample answer key will be released 7 days after the due date. Assignments should be submitted online by the due date and time. If an assignment is late but submitted before the sample answer key is posted, it will be graded with a 20% penalty. Any assignment submitted after the answer key is posted will get a Zero.

Quizzes

Each module will have an accompanying quiz to test student knowledge of the material. You will have 7 quizzes in this course. Each quiz will have 2 attempts available, and the average score will be recorded in your gradebook.

Each quiz will consist of multiple choice, short answer, and essay questions. Quizzes are open note, open book, and even open Google. However, students may not work with others on these quizzes, either students registered for the class or others outside the class. The deadlines to submit the quizzes are listed on Canvas.

Exams

Mid-Term exam: This exam will be taken on the 4th module. This exam may contain multiple choice, short answers, coding, and essay type questions. You will have only one attempt available for this exam. Exam is proctored and should be completed during the given time frame.

Sample answer will be released after 7 days of the due date. Exam submitted after the due date but before the sample answer key is posted will be graded with a 20% penalty. Exam submitted after the answer key is posted will get a Zero.

Final Exam: This exam will be taken on the last module. This may contain multiple choice, short answer, coding, and essay type questions. You will have only one attempt available for this exam. Exam is proctored and should be completed during the given time frame.

Sample answer will be released immediately after the due date and no late submission will be accepted.

Projects

In this course, we mainly cover two data analysis techniques; regression and classification. You will have to use one of the two techniques in the final project. This project will enable you to implement the concepts you have learned throughout the course. This is a group project.

Final project instructions and resources will be provided along with the module 1.

Course Outline

Weeks (Module)AssignmentsPointsContents
Module -0
Start: Jan 7
End: Jan 18		Quiz -0
Homework-0		Introduction & Course Orientation:
Introduction to Python, Google Colab
Variables, Lists, Tuples, Dictionary, Conditional Statements/Loops, List comprehension and Functions.
Module -1
Start: Jan 19
End: Feb 1		Introduction
Reflection Post-1
Quiz-1
Homework-1
Module -2
Start: Feb 2
End: Feb 15		Participation -2
Reflection Post-2
Quiz-2
Homework-2
Module -3
Start: Feb 16
End: Feb 22		Participation -3
Reflection Post-3
Quiz-3
Homework-3		Simple Linear Regression
Linear regression between two variables,
Measures of association,
Interpreting correlations, Fit a straight line, Least square estimation, Measures of regression fit
Module -4
Start: Feb 23
End: Mar 1		Participation-4
Reflection Post-4
Quiz-4
Homework-4
Mid-Term Exam		Multiple linear regression
Introduction to multiple linear regression, Standard error and Adjusted R-Squared, Pros and Cons, Overfitting and Underfitting, introduction to Regularization, L1 and L2 regularization.
Module -5
Start: Mar 2
End: Mar 22		Participation -5
Reflection Post-5
Quiz-5
Homework-5		Logistic Regression:
Introduction to logistic regression, Setting up threshold, Performance Measures, Evaluation of model.
Module -6
Start: Mar 23
End: Apr 5		Participation-6
Reflection Post-6
Quiz-6
Homework-6		Naefve Bayes Classifier in Python
Introduction to Naefve Bayes algorithm
Types of Naefve Bayes algorithm
Application of Naefve Bayes algorithm
Implementation of Naefve Bayes algorithm with one sample data set.
Module -7
Start: Apr 6
End: Apr 19		Participation -7
Reflection Post-7
Quiz-7
Homework-7
Final Project
Final Exam		Time -Series Analysis in Python
Introduction to Time-Series data
Types of Time-Series forecast
Introduction to ARIMA model
Introduction to ACF and PACF plot

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

Participation

Generally, this assignment will be locked after the due date therefore you wont be able to submit after the due date. Please dont email for the due date extension.

Reflection Posts

Generally, this assignment will be locked after the due date therefore you wont be able to submit it after the due date. Please dont email for the due date extension.

Homework

Sample answer key will be released 7 days after the due date. Assignments should be submitted online by the due date and time. If an assignment is late but submitted before the sample answer key is posted, it will be graded with a 20% penalty. Any assignment submitted after the answer key is posted will get a Zero.

Quizzes

The deadlines to submit the quizzes are listed on Canvas.

Mid-Term Exam

Sample answer will be released after 7 days of the due date. Exam submitted after the due date but before the sample answer key is posted will be graded with a 20% penalty. Exam submitted after the answer key is posted will get a Zero.

Final Exam

Sample answer will be released immediately after the due date and no late submission will be accepted.

Attendance Policy

 As this course is fully online, attendance is not taken. Student engagement is instead measured through participation assignments and reflection activities, which serve as evidence of active involvement in the class.

Course Fees

Students will work with three different programming languages during this course. All tools used are free, so there are no extra costs involved. 

Prerequisites

Course Prerequisites: None

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.