Hybrid Asynchronous Course | Spring 2026
1. Instructor
Dr. Sonya Zhang
E-mail: xszhang@cpp.edu
- If you email me, please use “GBA 6050” in the subject line, spell your full name in the email body, and send it from your cpp.edu account.
- Please do not submit any homework to me via email – it will not be accepted or graded. All homework should be submitted to either Canvas or Cengage as instructed.
- I typically aim to reply to emails within 24 hours. However, please note that this timeframe does not include weekends, as we are advised not to work on weekends or outside the 8-hour weekday limit according to university policy. Therefore, I recommend emailing on Thursday or early Friday if you would like to receive a response before the weekend.
- Office hours and the instructor’s Zoom link are provided in Canvas – Modules – Welcome to Class.
2. Class meetings
This course follows a hybrid asynchronous format. There are seven in-person meetings and eight asynchronous sessions. Learning materials and activities are posted Canvas. During asynchronous sessions, students learn knowledge and skills through online activities, including reading assigned textbook chapters, watching lecture and demonstration videos, and working on homework and projects.
| Week | Instruction Mode | Topic | Assignments |
| Week 1 (Monday 1/19) | MLK day no class | ||
| Week 2 (Monday 1/26) | Face-to-face | Chapter 1 Introduction | Chapter 1 Quiz |
| Week 3 (Monday 2/2) | Asynchronous | Chapter 2 Descriptive Statistics | Chapter 2 Quiz |
| Week 4 (Monday 2/9) | Face-to-face | Chapter 4 Descriptive Data Mining (1) | Analytic Solver Assignments: Hierarchical Clustering, K-means Clustering Chapter 4 Quiz (1) |
| Week 5 (Monday 2/16) | President’s Day Asynchronous | Chapter 4 Descriptive Data Mining (2) | Analytic Solver Assignments: Association Rules, Text Mining Chapter 4 Quiz (2) |
| Week 6 (Monday 2/23) | Asynchronous | Chapter 3 Data Visualization | Chapter 3 Quiz |
| Week 7 (Monday 3/2) | Face-to-face | Chapter 7 Linear Regression | Analytic Solver Assignment: Linear Regression Chapter 7 Quiz |
| Week 8 (Monday 3/9) | Asynchronous | Chapter 8 Time Series Analysis | Analytic Solver Assignment: Time Series Chapter 8 Quiz |
| Week 9 (Monday 3/16) | Face-to-face | Chapter 9 Predictive Data Mining (1) | Analytic Solver Assignments: Logistic Regression, KNN Regression & Classification Chapter 9 Quiz (1) |
| Week 10 (Monday 3/23) | Face-to-face | Chapter 9 Predictive Data Mining (2) | Analytic Solver Assignments: Regression & Classification Tree Chapter 9 Quiz (2) |
| Spring Break (Saturday 3/28 – Friday 4/3) | |||
| Week 11 (Monday 4/6) | Asynchronous | Chapter 10 Spreadsheet Models Group project | Chapter 10 Quiz Discussion Post 1: Project proposal, Data Collection and Cleaning |
| Week 12 (Monday 4/13) | Asynchronous | Group project | Discussion Post 2: Exploratory Data Analysis and Visualization |
| Week 13 (Monday 4/20) | Asynchronous | Group project | Discussion Post 3: Machine Learning |
| Week 14 (Monday 4/27) | Face-to-face | Final project presentations (1) | Group project deliverables |
| Week 15 (Monday 5/4) | Face-to-face | Final project presentations (2) | Group project deliverables |
3. Course Description
Catalog Description: Data analytics methods and business applications. Uses of data and information in organizational decision-making. Data gathering and sharing. Data mining. Descriptive, predictive, and prescriptive modeling. Ethical issues in data analytics.
Prerequisites: Completion of all MBA prereqs.
4. Course Learning Objectives and Module Learning Objectives
By the end of this course, students will be able to:
- Describe what is business data analytics and modeling, the process, and the fundamentals.
- Module 1 Learning Objectives: Describe what Business (Data) Analytics means and how it can help organizations decide. Describe three major categories of analytical methods and models and the four characteristics of big data. Describe the applications of data analytics in various business fields. Prepare and process data to get them ready for further exploration and analysis.
- Apply exploratory data analysis, visualize and discuss descriptive statistics results.
- Module 2 Learning Objectives: Describe different data types and describe the dataset using descriptive statistics, including summarizing data using numerical measures.
- Module 3 Learning Objectives: Apply data visualization to analyze data and convey your analysis to others.
- Apply descriptive data mining techniques.
- Module 4-5 Learning Objectives: Apply descriptive data-mining methods, also called unsupervised learning techniques, including clustering, association rules, and text mining.
- Apply predictive data mining techniques.
- Module 6 Learning Objectives: Apply linear regression to examine the relationship between a dependent variable and one or more independent variables, discuss results, and evaluate model performance.
- Module 7 Learning Objectives: Apply Time series regression to the data collected over some time, discuss results, and evaluate model performance.
- Module 8 Learning Objectives: Apply classification techniques such as logistic regression, KNN classification, and classification tree, discuss results, and evaluate model performance. Apply regression or estimation techniques such as KNN regression and regression tree, discuss results and evaluate model performance.
- Apply various prescriptive analytics & modeling techniques such as what-if analysis.
- Module 9 Learning Objectives: Describe principles of building good spreadsheet models, conduct what-if analysis using data table and goal seek and other Excel functions for data modeling.
5. Textbook and Software
Required Textbook
Business Analytics, 3rd Edition (instant access via Canvas)
Jeffrey D. Camm et al.
ISBN-10: 1-337-61555-2
Publisher: Cengage
Required Software
- Tableau Desktop and Tableau Prep (free; apply for a student license using your CPP email address)
- Microsoft Excel for Windows (recommended) or an Office365 subscription with Excel add-ins allowed.
- Analytic Solver for Excel (see instructions in Canvas Module 1)
Privacy
Optional Software
- RapidMiner Educational license (free academic license, apply using CPP email address)
Useful Resources:
Useful Data Sources:
1) Government and Public Organizations
- Data.gov: The U.S. government’s open data portal offers a wide range of datasets across sectors.
- European Data Portal: Similar to Data.gov, but focused on European data.
- World Bank Open Data: Access to global development data.
- UNData: A repository of datasets from the United Nations.
- Congressional and Federal Government Web Harvest
- Digest of Education Statistics
2) Academic and Research Resources
- Kaggle.com
- UCI Machine Learning Repository: A classic repository for machine learning datasets.
- Harvard Dataverse: An open repository for research datasets.
- Open ICPSR: Social science research data.
- UC Irvine Machine Learning Repository
- CORGIS: The Collection of Really Great, Interesting, Situated Datasets
- English Corpora: The most widely used online corpora for teachers and researchers.
3) Corporate and Technology Companies
- Google Dataset Search: A search engine for datasets across the web.
- Amazon Web Services (AWS) Open Data: A collection of datasets hosted on AWS.
- Microsoft Azure Open Datasets: Ready-to-use datasets optimized for machine learning.
- Sample data from Tableau Public
4) Specialized Data Portals
- Statista: For market and industry statistics (requires a subscription for some data).
- Quandl: Financial and economic data (free and paid).
- OpenStreetMap: Geospatial data.
- OpenDataSoft: A platform for sharing open data from various sources.
5) Health and Medical Data
- Kaggle Datasets from Health Organizations: Despite being on Kaggle, this category is often overlooked.
- CDC WONDER: Health-related data from the U.S. Centers for Disease Control and Prevention.
- PhysioNet: Physiological signals and medical research data.
6) Social Media and Web Scraping
- Twitter API: For tweet datasets (requires API access).
- Reddit: Use the Reddit API for user-generated content.
- Common Crawl: A massive dataset of web pages.
7) Competitions and Challenges
- DrivenData: Focused on social impact datasets and machine learning competitions.
- Zindi: African-centric machine learning challenges with unique datasets.
- Yelp Data Challenge: Restaurant and service provider reviews
8) Domain-Specific Data
- Baseball Reference: For sports data.
- Spotrac: Financial data for sports players and teams.
- Lending Club: Loan and finance-related data.
- IMDB Datasets: Movie and entertainment industry data.
6. Assignments and Projects
MindTap Assignments
Students will complete a MindTap quiz for each chapter. The assignments are auto-graded, and grades from the MindTap assignments will be transferred automatically to the Canvas grade book.
Analytic Solver Assignments
Students will use Analytic Solver to solve multiple data analytics problems use follow instructions and demo videos.
Final Group Project
In the final group project, students will apply what they have learned about data analytics to solve a business problem, including: 1) Problem Statement and Background, 2) Data Collection and Preparation, 3) Data Visualization and Exploratory Data Analysis, 4) Descriptive Analysis, 5) Predictive Analysis, and 6) Insights and Recommendations. Students will work in groups of 4 students. The deliverables include a final written report and in-class presentation. The project will be graded according to the rubric in Canvas.
Make-up policy: Make-up exams are not offered except for serious and compelling reasons substantiated by formal and authoritative documents.
Late Submission: Late assignments or projects will be accepted and subject to a 50% penalty within the first 24 hours past the deadline. No late work will be accepted after.
AI Use Policy
Generative AI tools such as ChatGPT, Gemini, Claude, Perplexity, and Copilot may be used for brainstorming, code, image or video generation, creating study materials, and text editing. However, you must clearly indicate what the AI produced and what you contributed, and you must disclose your use of AI in the assignment (e.g., in a note or footnote).
I expect you to be the author of all work you turn in. If I ask about your assignments and projects, you should be able to explain them in depth and demonstrate mastery of the material without assistance.
AI tools may support your analysis, but they cannot replace your own reasoning or interpretation. You are responsible for explaining results and showing your understanding.
Reflections on readings and assignments must be written entirely by you without AI assistance. These are meant to demonstrate the quality of your own ideas and the personal nature of your reflection.
Student Responsibilities
- Each student is responsible for completing and submitting all assignments and projects. Corrupted files or incomplete submissions will not be credited. Students are also responsible for keeping a backup copy of each submission.
- To ensure fairness, the instructor will NOT review, fix problems, or provide direct answers or step-by-step solutions to student assignments or projects before the deadline. The instructor will, however, help students understand expectations, clarify requirements, provide guidance and examples, and help students gain the knowledge and skills needed.
- Students must have spent a significant and reasonable amount of time and effort researching and working on the issue independently before asking for help.
7. Grading
| Grade | Percentage |
| A | 93.00-100.00 |
| A- | 90.00-92.99 |
| B+ | 87.00-89.99 |
| B | 83.00-86.99 |
| B- | 80.00-82.99 |
| C+ | 77.00-79.99 |
| C | 73.00-76.99 |
| C- | 70.00-72.99 |
| D+ | 67.00-69.99 |
| D | 63.00-66.99 |
| D- | 60.00-62.99 |
| F | 0-59.99 |
| Item | % |
|---|---|
| MindTap Quizzes | 20 |
| Analytic Solver Assignments | 40 |
| Final Group Project | 40 |
| Total | 100 |
9. Course Policies
Classroom environment: The classroom is a special environment where students and faculty unite to promote learning and growth. It is essential to this learning environment to maintain respect for the rights of others seeking to learn, respect for the instructor’s professionalism, and the general goals of academic freedom. Student conduct that disrupts the learning process shall not be tolerated and may lead to disciplinary action and/or removal from class.
10. University Policies
Accessibility: Cal Poly Pomona is committed to student success as a learning-centered university. Students with disabilities are encouraged to contact the instructor privately or to visit the Disability Resource Center to coordinate course accommodations.
Computing Resources: At Cal Poly Pomona, computers and communications links to remote resources are recognized as being integral to the education and research experience. Every student must have access to a computer with all the required software for this course. Contact I&IT if you need help.
Academic Integrity: The University is committed to maintaining academic integrity throughout the university community. Academic dishonesty is a serious offense that can diminish the quality of scholarship, the educational environment, the academic reputation, and the quality of a Cal Poly Pomona degree. Plagiarism or cheating will not be tolerated in this course.
Copyright Policy: Copyright laws and fair use policies protect the rights of those who have produced the material. The copy in this course has been provided for private study, scholarship, or research. Other uses may require permission from the copyright holder. The user of this work is responsible for adhering to the copyright law of the U.S. (Title 17, U.S. Code). The course website contains material protected by copyrights held by the instructor, other individuals, or institutions. Such material is used for educational purposes following copyright law and/or with permission given by the owners of the original material. Students may download one copy of the materials on any single computer for non-commercial, personal, or educational purposes only, provided that (1) do not modify it, (2) use it only for the duration of this course, and (3) include both this notice and any copyright notice originally included with the material. Beyond this use, no material from the course website may be copied, reproduced, republished, uploaded, posted, transmitted, or distributed in any way without the original copyright holder’s permission. The instructor assumes no responsibility for individuals who improperly use copyrighted material placed on the website.