BZAN 6350: Quantitative Foundations for Business Analytics
1. About BZAN 6350
BZAN 6350: Quantitative Foundations for Business Analytics is a graduate-level course that introduces students to the statistical methods and analytical tools used to understand business performance and support decision-making.
The course focuses on descriptive, predictive, and prescriptive business analytics. Students learn how to explore data, identify patterns, build statistical models, and communicate analytical findings. The course uses R, Tableau, and other software to develop practical data analysis and modeling skills.
- Course format: Asynchronous online
- Prerequisite: Graduate standing and/or admission to the MS-BZAN program
- Section: 15879
- Semester: Fall 2026
2. What Do Students Learn in BZAN 6350?
Students develop a foundation in business analytics, with an emphasis on applying data mining methods to real-world business problems.
By the end of the course, students should be able to:
- Explain and apply the data mining process for descriptive and predictive analytics.
- Use R for data preparation, exploration, analysis, and predictive modeling.
- Apply data visualization, cluster analysis, association rules, regression, hypothesis testing, and decision trees.
- Conduct a complete data mining project, from research and data preparation to reporting results.
- Interpret analytical models and communicate their findings to support business decisions.
3. Main Topics Covered
The course introduces several core methods used in business analytics and data mining.
| Topic | What It Covers |
|---|---|
| Business Analytics | The role of analytics in organizations and its use in business planning and decision-making. |
| Data Visualization | Exploring and presenting data using Tableau. |
| Data Mining Process | Understanding the process of preparing, exploring, and analyzing data. |
| Cluster Analysis | Identifying groups or patterns within datasets. |
| Association Rules | Examining relationships and patterns among variables or items. |
| Multiple Linear Regression | Modeling relationships between variables to support analysis and prediction. |
| Logistic Regression | Applying regression methods to classification problems. |
| Classification and Regression Trees | Using tree-based methods for classification and predictive modeling. |
4. Course Structure
BZAN 6350 combines online instruction, software-based learning, skill-building exercises, and assessments.
Students work with Tableau to visualize data and use R and RStudio for statistical analysis and modeling. DataCamp assignments provide additional guided practice with R and selected analytical techniques.
The course includes:
- Online learning materials and course setup activities.
- Data visualization exercises using Tableau.
- R-based assignments covering data analysis and modeling.
- Two exams.
- Skill-building homework and DataCamp coursework.
- Optional bonus assignments for additional learning and points.
Although the course is asynchronous online, exams have scheduled dates and times.
5. Assignments and Assessments
Skill-Building Exercises and Homework
Students complete assignments throughout the semester to practice the methods and tools introduced in the course. Homework is submitted by the deadlines listed in the course schedule, and late submissions are not accepted.
Students may discuss course material with classmates, but submitted work must be their own. The syllabus specifies that every line of submitted text and code must be personally written by the student. Homework answers are submitted as a team through Blackboard.
DataCamp Assignments
Students complete five individual DataCamp courses designed to strengthen their practical R and analytics skills:
- Introduction to R
- Cluster Analysis in R
- Introduction to Regression in R
- Intermediate Regression in R
- Machine Learning with Tree-Based Models in R
Students receive free DataCamp access during the course period and must register using their UH email address.
Exams
The course has two exams, each worth 20% of the final grade.
The exams are not cumulative, although students may use code learned in previous modules. Students are expected to take exams on their own laptops with the required software and internet access available.
Exams cannot be retaken or taken at another time except under extreme circumstances. Approval must be obtained before a missed exam.
Bonus Assignments
Optional bonus assignments are available for students who want to develop additional analytics skills. Each bonus assignment adds two points to the final grade. Additional opportunities may be announced on Canvas.
6. Grading Breakdown
The course grade is determined by three components:
| Grade Component | Weight |
|---|---|
| Assignments | 60% |
| Exam 1 | 20% |
| Exam 2 | 20% |
| Total | 100% |
Assignments make up the largest portion of the grade, so completing the exercises and DataCamp coursework is especially important.
The syllabus notes that grading weights are subject to change.
7. Readings and Learning Materials
The following textbook is used as a reference but is not required for purchase:
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, 1st edition, by Galit Shmueli, Peter C. Bruce, Inbal Yahav, Nitin R. Patel, and Kenneth C. Lichtendahl Jr.
Students also use several software tools and learning platforms:
- Tableau: For data visualization. Students can obtain a free student copy using the activation code provided on Canvas.
- R: Open-source statistical software used for data preparation, exploration, analysis, and modeling.
- RStudio Desktop: An environment for working with R.
- DataCamp: An online learning platform used for assigned R courses and exercises.
Students are responsible for installing and using the required programs for coursework and exams.
8. Weekly Course Topics
The Fall 2026 schedule progresses from introductory analytics concepts to data mining and predictive modeling.
| Week | Dates | Topics and Activities |
|---|---|---|
| 1 | Aug 24–28 | Introduction to Business Analytics and course setup |
| 2 | Aug 31–Sep 4 | Data visualization using Tableau |
| 3 | Sep 7–11 | Data visualization using Tableau; introduction to R |
| 4 | Sep 14–18 | Data visualization using Tableau |
| 5 | Sep 21–25 | Data mining process; cluster analysis in R |
| 6 | Sep 28–Oct 2 | Cluster analysis |
| 7 | Oct 5–9 | Association rules; introduction to regression in R |
| 8 | Oct 12–16 | Exam 1 |
| 9 | Oct 19–23 | No topic specified in the schedule |
| 10 | Oct 26–30 | Intermediate regression in R |
| 11 | Nov 2–6 | Logistic regression |
| 12 | Nov 9–13 | Classification and regression trees; machine learning with tree-based models in R |
| 13 | Nov 16–20 | Classification and regression trees; bonus assignments |
| — | Nov 23–29 | Thanksgiving holiday |
| 14 | Nov 30–Dec 4 | Exam 2 |
The schedule also lists due dates for several DataCamp courses and bonus assignments. Students should consult the course schedule and Canvas for the applicable deadlines and any updates.
9. Skills Developed
BZAN 6350 helps students build practical skills in:
- Data visualization: Presenting and exploring data with Tableau.
- Statistical analysis: Applying regression and other statistical methods.
- Data preparation: Organizing and preparing datasets for analysis.
- Data mining: Using clustering, association rules, and tree-based methods to identify patterns.
- Predictive modeling: Developing models to analyze business outcomes.
- Programming with R: Using R and RStudio for data analysis and modeling.
- Analytical communication: Reporting findings and interpreting model results for business decisions.
- Problem-solving: Applying analytical methods to business questions.
10. What Should Students Expect?
Students should expect a practical, software-focused course that combines statistical concepts with hands-on analytics work.
A substantial portion of the course grade comes from assignments. Students will need to keep up with homework deadlines, complete individual DataCamp courses, and practice using R and Tableau. Since late homework is not accepted, staying organized throughout the semester is important.
The two exams are scheduled assessments rather than flexible, self-paced activities. Students should make sure their software is installed and functioning before exam dates.
The course also emphasizes independent work. Although students may discuss material with classmates, submitted text and code must be their own.
11. BZAN 6350 at a Glance
| Course Detail | Information |
|---|---|
| Course | BZAN 6350 – Quantitative Foundations for Business Analytics |
| Level | Graduate |
| Format | Asynchronous online |
| Prerequisite | Graduate standing and/or admission to the MS-BZAN program |
| Main Focus | Applied statistics, business analytics, and data mining |
| Key Software | R, RStudio, Tableau |
| Learning Platform | DataCamp |
| Major Assessments | Assignments and two exams |
| Assignments Weight | 60% |
| Exam Weight | 40% combined |
| Exams | – |
| Textbook | Reference only; purchase not required |
Final Takeaway
BZAN 6350 introduces students to the statistical methods and software used in business analytics. Through data visualization, data mining, regression, and tree-based modeling, students learn to explore data and develop insights that support business decisions. The course is particularly focused on practical application, with assignments and DataCamp activities forming most of the final grade.
