BUAN 244: Business Analytics – Summer 2025 (CRN: 21275)
As of 6/20/2025
Recommended Texts
1. R for Data Science – Hadley Wickham & Garrett Grolemund, 2017.
Click here to access this freely available textbook.
2. Introduction to Data Cleaning with R – de Jong and van der Loo
(Statistics Netherlands, 2013). Click here to access this freely available manuscript.
Software This course will use R, R Studio, and Tableau. Installation guides will be provided.
Course Description
This course introduces data visualization, emphasizing how data should be explored to reveal unexpected patterns, trends, and anomalies. Information will be leveraged to generate information for business problems, develop new perspectives, and provide actionable insights for business decision-makers. An exploration of data sources, cleaning and transformations, and storage will be conducted. (ETL) Using visualization techniques, stored data sets can be prepared to provide insights, answer questions of interest, and assist in enabling value-delivering actions. The course will include implementing data analysis and visualization through hands-on programming using R and Tableau. Even though the software will be used extensively, this is not a software training course. The focus is on understanding the underlying methodology and best practices for data management, exploratory and descriptive analyses, and developing techniques for creating stories for the domains of interest.
Course Learning Goals
Upon completion of this course, students will:
A. Gain hands-on experience with business analytics software.
B. Criteria: R and Tableau Projects, Proficiencies Evaluations, and Integrative Evaluation
C. Be able to apply the skills from this class in your future career.
D. Integrated evaluations will be performed via assigned data sets.
During this course, students will learn:
A. Extract, transform, and load (ETL) data using a platform. (e.g., R).
B. Create interactive dashboards via a platform. (e.g., Tableau) that can be used for business decision-making.
C. Gain hands-on experience with business analytics software.
D. Critical thinking skills in the usage of data visualizations in a business context.
Course Schedule
**** Please note that content and class schedule are subject to change at the course instructor's discretion. Any changes will be announced during class and posted to the Coursesite in the Course Information Section.
Assignment Due Dates
• Homework #1 (7/10)
• Homework #2 (7/15)
• Homework #3 (7/17)
• R Practical Exam (7/22)
• Homework #4 (7/31)
• Homework #5 (8/5)
• Homework #6 (8/5)
• Final Project (8/7)
• Final Exam (8/9) (8:00 ~ 11:00 am)
Note: All Activities are to be completed independently; please see the course and GenAI policies below
Course Policies for Submissions
My late submission policy allows you to submit the assignment two days past the due date. A point deduction will apply for late submissions. After three days, the submission box will be closed, and missing assignments will receive a score of 0. Any variances, accommodations, or extension requests must be approved one day before the due date. No accommodations or extensions will be granted after this time. If you have any questions about an exam or assignment grade, you must raise them within three days of posting the grade; after four days, all grades are considered final, and no further discussion will be entertained.
Course Evaluative Criteria
Note: The Activities* and Points* are subject to change without prior notice.
Course Activities
Participation and Homework Assignment: [~55%]
A) Worksheets - Participation:
In-class activities demonstrate student knowledge and critical thinking skills related to the course content. Five worksheets are to be completed as an in-class assignment. There are no make-ups for these assignments.
B) Homework Assignments:
Students will complete six homework assignments to reinforce their understanding of various concepts and tools related to the course content. Details will be provided with each assignment.
C) Semester Project:
The objectives of the assignment are to assess your ability to (1) apply Tableau skills covered in class to analyze a new dataset and (2) perform. independent research to fill in gaps in the analysis to answer real-life business questions.
More details will be provided when the project is assigned. Due Date (8/7)
Exams: [~45%]
D) The R Practical Exam will consist of a visualization theory evaluation (14 points) and an R coding assignment completed in class (16 points).
E) The final Tableau Practical Exam will be given during the course's scheduled final examination period. Your analytic and visualization skills will be evaluated via Ad Hoc Analytics requests.
Make-up exams: Make-up exams will generally not be given. Exceptions are granted at the instructor’s discretion and typically reserved for extreme circumstances, such as documented hospitalization or an excused absence note from the Dean of Students’ office recommending the privilege. If a student can take a make-up exam, the instructor may substitute an alternate exam with different content. Students may find the make-up exam content more challenging than the original. Therefore, it is in a student’s best interest to attend each exam at the scheduled time and take it with the rest of the class.
Exam format: Exams will be “Open Book," but you may only use the materials from this class (BUAN 244) available on the Course Site. Sharing information or utilizing other collaborative resources (e.g., message boards, email, text messages) will violate Academic Integrity. AI tools may not be used to complete the R Practical Exam or the Final-Tableau Practical Exam.
Course Grading Scale
The following conversion table will be used to assign letter grades at the end of the semester.