Syllabus

Master of Business Administration

BUMG 520: Business Analytics

Credits - 3

Description

This course equips students with data-driven decision-making skills through statistical analysis, predictive modeling, and data visualization. Emphasizing real-world applications, students learn to interpret data, generate strategic insights, and apply analytics to optimize business performance.

 

 

 

 

 

 

Materials

Texts:

Software/ Tools:

  • R and RStudio; R scripts and Quarto documents 
  • PositCloud web platform for above (later, optional download onto personal machine)
  • MyOpenMath (free, open source platform used throughout the course for math/ statistics practice and support) 

 

 

 

 

 

Learning Objectives and Outcomes

Students successfully completing the course will be able to:

  • Import, clean, and pre-process data.
  • Perform basic exploratory data analysis including descriptive statistics and visualization
  • Build, tune, and deploy a range of statistical and machine learning models
  • Assess model performance using techniques such as cross validation, using a range of metrics.
  • Apply models to domain specific problems in business
  • Use statistical models to explain relationships between variables.
  • Produce professional report documents 

 

 

 

 

 

 

Assignments

Weekly Summative Assignments

Weekly assignments are designed to help students apply core business analytics concepts using real-world datasets and guided analytical workflows in R and Posit/ RStudio. These assignments build progressively across the course, beginning with structured, instructor-provided templates and transitioning toward more independent analysis as students gain confidence.

Assignments typically involve:

  • Data cleaning and preparation
  • Exploratory data analysis (EDA)
  • Basic statistical reasoning
  • Visualization and model interpretation
  • Clear written explanations of analytic decisions and results

Each assignment emphasizes interpretation and communication, not just technical execution, and is framed around realistic business or organizational contexts.

Discussions

Weekly discussions provide space for reflection, interpretation, and peer exchange around the human, organizational, and ethical dimensions of analytics. Prompts are intentionally non-technical and focus on:

  • Communicating insights to non-technical stakeholders
  • Explainability and trust in models
  • The role of analytics and AI in decision-making
  • Project framing and collaboration

Discussions are graded on the quality of engagement, clarity of thought, and connection to course ideas rather than technical detail. Some weeks intentionally omit a discussion to allow additional focus on applied work or group collaboration.

Application Exercises (AE’s)

Application Exercises (AEs) are short, targeted practice activities designed to reinforce specific skills introduced in readings, videos, or demonstrations. Early in the course, these exercises use pre-built Posit projects and templates so students can focus on learning concepts without technical setup overhead.

AEs may include:

  • Writing or modifying short R code segments
  • Interpreting outputs, tables, or visualizations
  • Answering focused analytic questions
  • Extending examples slightly beyond what is demonstrated

These exercises are formative in nature and are intended to build fluency and confidence ahead of larger assignments.

MyOpenMath Practice

MyOpenMath activities provide structured, low-stakes practice with foundational statistical concepts that support the analytic work in R. These exercises are primarily focused on:

  • Descriptive statistics
  • Probability and distributions
  • Interpreting statistical outputs

The goal of MyOpenMath practice is conceptual leveling, not advanced mathematics. Students are encouraged to use these activities as a learning resource and skills refresher alongside the applied analytics work.

Final Course Project (Group-Based)

The Final Course Project is a cumulative, group-based analysis completed over the second half of the course. Working in small groups, students select a real dataset and a meaningful business or organizational question that can be addressed using the methods covered in the course.

Project components include:

  • A project proposal and analytic plan
  • Data exploration and preparation
  • Application of appropriate modeling or analytic techniques
  • Interpretation of results with attention to limitations and assumptions
  • A final written analysis including visuals 

The final project emphasizes analytic reasoning, collaboration, and stakeholder-focused communication, mirroring how analytics work is typically conducted in professional settings.

 

 

 

 

 

Grading Policy

Your grade in this course will be determined by the following criteria:

Grade Breakdown

Grade CategoryWeight
Weekly Assignments50%
Discussions10%
AE (Application Exercises)10%
Final Project Components20%
MyOpenMath Exercises10%

Grade Scale

Grade Points Grade Point Average (GPA)
A 94 – 100% 4.00
A- 90 – 93% 3.75
B+ 87 – 89% 3.50
B 84 – 86% 3.00
B- 80 – 83% 2.75
C+ 77 – 79% 2.50
C 74 – 76% 2.00
C- 70 – 73% 1.75
D 64 – 69% 1.00
F 00 – 63% 0.00

 

 

 

 

 

 

 

Schedule

Term Dates: 3/18/2026 – 5/8/2026

Week 1: Wednesday – Sunday
Week 2: Monday – Sunday
Week 3: Monday – Sunday
Week 4: Monday – Sunday
Week 5: Monday – Sunday
Week 6: Monday – Sunday
Week 7: Monday – Sunday
Week 8: Monday – Friday

WEEK TOPICS ASSESSMENTS DUE
1
  • Introduction to Business Analytics & R
  • Statistics “Leveling”: Materials and Practice
  • Week 1 Discussion: Introductions and Everyday Data Analysis
  • Week 1 Assignment: Ames Housing
  • Application Exercises (AE-01, AE-02, AE-03)
  • Discussion: Initial Post Due Friday by 11:59 PM. Responses due Sunday by 11:59 PM
  • Assignment: due Sunday by 11:59 PM
  • AE(s): due Sunday by 11:59 PM
2
  • R Basics & Data Structures
  • Week 2 Discussion: Communicating Analytics to Stake Holders
  • Assignment 2: nycflights wrangling
  • AE-04
  • Discussion: Initial Post Due Thursday by 11:59 PM. Responses due Sunday by 11;59 PM
  • Assignment: due Sunday by 11:59 PM
  • AE(s): due Sunday by 11:59 PM
3
  • Data Manipulation & Basic Statistics
  • Week 3 Discussion: The Role of Explainability
  • Assignment 3: Bike Shop Sales
  • AE-05
  • Project Proposal Draft (Group)
  • Discussion: Initial Post Due Thursday by 11:59 PM. Responses due Sunday by 11:59 PM
  • Assignment: due Sunday by 11:59 PM
  • AE(s): due Sunday by 11:59 PM
  • Project Proposal Draft: due Sunday by 11:59 PM
4
  • Data Exploration (EDA)
  • No Discussion this Week
  • Week 4 Assignment A
  • Week 4 Assignment B
  • Final Project Proposal
  • Assignment(s): due Sunday by 11:59 PM
  • Final Project Proposal: due Sunday by 11:59 PM
5
  • Data Visualization with R
  • Week 5 Discussion: AI in Society and in Work
  • Week 5 Assignment: Regression & Classification with tidymodels
  • Discussion: Initial Post Due Thursday by 11:59 PM. Responses due Sunday by 11:59 PM
  • Assignment: due Sunday by 11:59 PM
6
  • Predictive Analytics I (Regression Models)
  • Week 6 Discussion: Final Project Check-In
  • Week 6 Assignment: Predictive Analytics
  • Discussion: Initial Post Due Thursday by 11:59 PM. Responses due Sunday by 11:59 PM
  • Assignment: due Sunday by 11:59 PM
7 Predictive Analytics II & Validation
  • No Discussion this week. Use the time to work with your groups on your final project. 
  • Week 7 Assignment A
  • Week 7 Assignment B
  • Assignment(s): due Sunday by 11:59 PM
8 Descriptive Analytics & Advanced Methods
  • Week 8 Discussion – Final Project Presentation
  • Final Project Submission

 

  • Post Project in Discussion area (share with peers) Friday by 11:59 PM
  • Final Project Submission Due: Friday 11:59 PM

 

 

 

 

 

 

Student Resources

 

 

 

 

 

 

 

Online Student Support

Your Enrollment and Retention Counselor is a resource for you. Please don't hesitate to contact them for assistance, including, but not limited to course planning, current problems or issues in a course, technology concerns, or personal emergencies.

Questions? Please reach out to your student success team member.

UNE Libraries:

  • Library Access for all students: Your library login ID and password are the same as the ones you use to log into Brightspace.
  • Research by Subject: Business & Entrepreneurship Collection
  • Library Questions: Ask a librarian or phone library staff at (207) 602-2361 or (207) 221-4330.

UNE Student Academic Success Center

UNE's Student Academic Success Center (SASC) offers a range of free online services to support your academic achievement. Writing support, ESOL support, study strategy and learning style consultations, as well as downloadable resources, are available to all matriculating students. To make an appointment for any of these services, go to une.tutortrac.com. For more information and to view and download writing and studying resources, please visit:

Accommodations

Any student who would like to request, or ask any questions regarding, academic adjustments or accommodations must contact the Student Access Center at (207) 221-4438 or pcstudentaccess@une.edu. Student Access Center staff will evaluate the student's documentation and determine eligibility of accommodation(s) through the Student Access Center registration procedure.

Online Peer Support

Togetherall is a 24/7 communication and emotional support platform monitored by trained clinicians. It’s a safe place online to get things off your chest, have conversations, express yourself creatively, and learn how to manage your mental health. If sharing isn’t your thing, Togetherall has other tools and courses to help you look after yourself with plenty of resources to explore. Whether you’re struggling to cope, feeling low, or just need a place to talk, Togetherall can help you explore your feelings in a safe supportive environment. You can join Togetherall using your UNE email address.

Information Technology Services (ITS)

Students should notify their student success team member and instructor in the event of a problem relating to a course. This notification should occur promptly and proactively to support timely resolution.

ITS Contact: Toll-Free Help Desk 24 hours/7 days per week 

Phone: Mon-Fri: (207) 602-2487

After Hours/Weekends: (877) 518-4673

Career Ready Program

UNE Online supports its online students and alumni in their career journey!

The Career Ready Program provides tools and resources to help students explore and hone in on their career goals, search for jobs, create and improve professional documents, build professional network, learn interview skills, grow as a professional, and more. Come back often, at any time, as you move through your journey from career readiness as a student to career growth, satisfaction, and success as alumni.

Accommodations

Any student who would like to request, or ask any questions regarding, academic adjustments or accommodations must contact the Student Access Center at (207) 221-4438 or pcstudentaccess@une.edu. Student Access Center staff will evaluate the student's documentation and determine eligibility of accommodation(s) through the Student Access Center registration procedure.

Online Peer Support

Togetherall is a 24/7 communication and emotional support platform monitored by trained clinicians. It’s a safe place online to get things off your chest, have conversations, express yourself creatively, and learn how to manage your mental health. If sharing isn’t your thing, Togetherall has other tools and courses to help you look after yourself with plenty of resources to explore. Whether you’re struggling to cope, feeling low, or just need a place to talk, Togetherall can help you explore your feelings in a safe supportive environment. You can join Togetherall using your UNE email address.

Information Technology Services (ITS)

Students should notify their Student Support Specialist and instructor in the event of a problem relating to a course. This notification should occur promptly and proactively to support timely resolution.

ITS Contact: Toll-Free Help Desk 24 hours/7 days per week at 1-877-518-4673.

Career Ready Program

The College of Professional Studies supports its online students and alumni in their career journey!

The Career Ready Program provides tools and resources to help students explore and hone in on their career goals, search for jobs, create and improve professional documents, build professional network, learn interview skills, grow as a professional, and more. Come back often, at any time, as you move through your journey from career readiness as a student to career growth, satisfaction, and success as alumni.

Policies

 

 

 

 

 

 

 

Passing Grade Statement

A grade of 80% or higher is required to pass the course. A grade lower than 80% will result in you having to repeat the course. Obtaining two "Fs" in the program will result in dismissal from the program.

Artificial Intelligence (AI) Statement

Generative AI (GenAI) applications (like ChatGPT) have proven to be powerful and effective tools, and students are encouraged to become familiar with and use them. However, as with any tool, students must use GenAI in ways that support their roles as learners and professionals. The use of AI in academic work falls under our academic integrity agreement, ensuring that all AI applications are used in alignment with our commitment to honest and responsible learning.

In situations in which AI tools are used as a resource, students must:

  • Ensure that all submitted academic work adequately demonstrates student learning (i.e., that the student, rather than a machine, has met the learning outcomes related to the assessment).
  • Acknowledge, in written assessments and extra-curricular applications, the role played by AI tools in producing the student’s work (this can usually be done in a citation or by including a session transcript).
  • Take ultimate responsibility for accuracy of results, think critically about them, and never substitute them for professional human judgment.
  • Monitor GenAI output for bias and risks for vulnerable populations and underrepresented groups.

As GenAI continues to evolve, students should also stay abreast of best practices and changing risks and benefits.

Please note that individual courses or assignments may have specific guidelines regarding AI use — please refer to your faculty’s directions or assignment instructions for details.

The Student Orientation has a module "Artificial Intelligence Literacy for Students", please refer to this module for more information about navigating the use of AI.

Turnitin Originality Check and Plagiarism Detection Tool

UNE uses Turnitin to help deter plagiarism and to foster the proper attribution of sources. Turnitin provides comparative reports for submitted assignments that reflect similarities in other written works. This can include, but is not limited to, previously submitted assignments, internet articles, research journals, and academic databases.

Make sure to cite your sources appropriately as well as use your own words in synthesizing information from published literature. 

You can learn more about Turnitin in the guide on how to navigate your Similarity Report.

Technology Requirements

Please review the technical requirements for UNE Online Graduate Programs: Technical Requirements.

Late Policy

Students are responsible for submitting work by the date indicated in Brightspace.

Please make every effort ahead of time to contact your instructor and your enrollment and retention counselor if you are not able to meet an assignment deadline. Arrangements for extenuating circumstances may be considered by faculty.

Student Handbook - Policies and Procedures

The policies contained within this document apply to all students at the University of New England. It is each student's responsibility to know the contents of this handbook.

UNE Student Handbook

UNE Course Withdrawal

Please contact your Enrollment and Retention Counselor if you are considering dropping or withdrawing from a course. Tuition charges may still apply. Students are strongly urged to consult with Student Financial Services, as course withdrawals may affect financial aid or Veterans benefits.

Attendance Policy

Students taking online graduate courses through the College of Business will be administratively dropped for non-participation if a graded assignment/discussion post is not submitted before Sunday at 11:59 p.m. ET of the first week. Reinstatement is at the purview of the Dean’s Office.

Academic Integrity

The University of New England values academic integrity in all aspects of the educational experience. Academic dishonesty in any form undermines this standard and devalues the original contributions of others. It is the responsibility of all members of the University community to actively uphold the integrity of the academy; failure to act, for any reason, is not acceptable. For information about plagiarism and academic misconduct, please visit UNE Plagiarism Policies.

Academic dishonesty includes, but is not limited to the following:

  1. Cheating, copying, or the offering or receiving of unauthorized assistance or information.
  2. Fabrication or falsification of data, results, or sources for papers or reports.
  3. Action which destroys or alters the work of another student.
  4. Multiple submissions of the same paper or report for assignments in more than one course without permission of each instructor.
  5. Plagiarism, the appropriation of records, research, materials, ideas, or the language of other persons or writers and the submission of them as one's own.

Attendance Policy

6- to 8-week courses: Students taking online graduate courses through the College of Professional Studies will be administratively dropped for non-participation if a graded assignment/discussion post is not submitted before Sunday at 11:59 pm ET of the first week of the term. Reinstatement is at the purview of the Dean's Office.

10+ -week courses: Students taking online graduate courses through the College of Professional Studies will be administratively dropped for non-participation if a graded assignment/discussion post is not submitted before Friday at 11:59 pm ET of the second week of the term. Reinstatement is at the purview of the Dean's Office.

Student Handbook Online - Policies and Procedures

The policies contained within this document apply to all students in the College of Professional Studies. It is each student's responsibility to know the contents of this handbook.

UNE Online Student Handbook

UNE Course Withdrawal

Please contact your Enrollment and Retention Counselor if you are considering dropping or withdrawing from a course. Tuition charges may still apply. Students are strongly urged to consult with Student Financial Services, as course withdrawals may affect financial aid or Veterans benefits.

Academic Integrity

The University of New England values academic integrity in all aspects of the educational experience. Academic dishonesty in any form undermines this standard and devalues the original contributions of others. It is the responsibility of all members of the University community to actively uphold the integrity of the academy; failure to act, for any reason, is not acceptable. For information about plagiarism and academic misconduct, please visit UNE Plagiarism Policies.

Academic dishonesty includes, but is not limited to the following:

  1. Cheating, copying, or the offering or receiving of unauthorized assistance or information.
  2. Fabrication or falsification of data, results, or sources for papers or reports.
  3. Action which destroys or alters the work of another student.
  4. Multiple submissions of the same paper or report for assignments in more than one course without permission of each instructor.
  5. Plagiarism, the appropriation of records, research, materials, ideas, or the language of other persons or writers and the submission of them as one's own.

Charges of academic dishonesty will be reviewed by the Program Director. Penalties for students found responsible for violations may depend upon the seriousness and circumstances of the violation, the degree of premeditation involved, and/or the student’s previous record of violations. Appeal of a decision may be made to the Dean whose decision will be final. Student appeals will take place through the grievance process outlined in the student handbook.