BUS 3700 - Business Analytics
Fall 2026 Syllabus, Section 04, CRN 42731,
Credit hours: 3
Course Meeting Times
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Instructor
Courtney Borruso
Email: cdborruso@ysu.edu
Course Description
3700. Business Analytics. Business analytics involves the acquisition, evaluation, and analysis of information for decision-making. This course introduces students to the application of business statistics enabling evidence-based decision-making and problem solving and will cover the four areas of analytics. These are: 1) descriptive (what happened?); 2) diagnostic (why did it happen?); 3) predictive (what will happen and is there a pattern?); and prescriptive (how can we make it happen?). Prereq.: BUS 2600, 2.5 GPA. 3 s.h.
Course Readings
| Group | Title | Author | ISBN |
|---|---|---|---|
| Optional | Business Analytics |
Author: Evans, James
Edition: 3rd
Publisher: Pearson+
ISBN: 8220145132486
The course readings are subject to change in the event of extenuating circumstances, research developments, current events, and/or to ensure better learning.
Assignments/Assessments
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Schedule of Topics and Assignments
| Week of | Reading(s) | Proposed Topic | Due/To Prepare for Class |
|---|---|---|---|
| 8/24 | Module 1: What is Business Analytics | Tuesday: Course Introduction, Syllabus Review, & Course Expectations Thursday: Module 1 What is Business Analytics? |
Due 8/27 @ End of Class: ICA # 1 & 2 |
| 8/31 | Module 2: Descriptive Statistics | Tuesday: Module 2 Descriptive Statistics Part 1: Types of data, Central Tendency, Frequency Tables, & Proportions Thursday: Module 2 Descriptive Statistics Part 2: Dispersion, Visualization, & Outliers |
Due 9/1 @ End of Class: ICA # 3 Due 9/3 @ End of Class: ICA # 4 |
| 9/7 | Tuesday: Module 2 Descriptive Statistics Excel Lab & Group Project Sign-Ups Thursday: Introducing Group Project and Discuss Expectations: Q & A |
Due 9/8 @ End of Class: ICA # 5 Due 9/10 @ End of CLass: ICA # 6 |
|
| 9/14 | Module 3: Data Quality, Preparation, & Data Cleaning | Tuesday: Module 3 Data Quality, Preparation, & Data Cleaning Thursday: Module 3 Data Quality, Preparation, & Data Cleaning Excel Lab |
Due 9/15 @ End of Class: ICA # 7 Due 9/17 @ End of Class ICA # 8 |
| 9/21 | Module 4: Probability Distributions | Tuesday: Module 4 Probability Distributions Thursday: Module 4 Probability Distributions Excel Lab |
Due 9/22 @ End of Class: ICA # 9 Due 9/24 @ End of Class: ICA # 10 Due SUNDAY, 9/27/26 @ 11:59 PM in Blackboard: Group Project Phase 1 & Peer Feedback Phase 1 |
| 9/28 | Module 5: Confidence Intervals and Correlation | Tuesday: Special Topics: TBD Thursday: Module 5 Confidence Intervals and Correlation |
Due 9/29 @ End of Class: ICA # 11 Due 10/1 @ End of Class: ICA # 12 |
| 10/5 | Module 6: Up to an including One Sample t-Test | Tuesday: Module 5 Confidence Intervals and Correlation Excel Lab Thursday: Module 6 Hypothesis Testing Part 1: Hypothesis Testing Basics and One-Sample t-Test |
Due 10/6 @ End of Class: ICA # 13 Due 10/8 @ End of Class: ICA # 14 Due SUNDAY, 10/11/26 @ 11:59 PM in Blackboard: Group Project Phase 2 & Peer Feedback Phase 2 |
| 10/12 | Module 6: From Two-Sample & Paired t-Test, ANOVA, Chi-Squared | Tuesday: Module 6 Hypothesis Testing Part 2: Two Sample & Paired t-Test, ANOVA, Chi-Squared Thursday: Module 6 Hypothesis Testing Excel Lab |
Due 10/13 @ End of Class: ICA # 15 Due 10/15 @ End of Class: ICA # 16 |
| 10/19 | Module 7: Simple Linear Regression Part 1: Regression Basics, Building Model, and Interpreting Output and Equation | Tuesday: In-Class Group Project Workday Thursday: Module 7 Simple Linear Regression Part 1: Regression Basics, Building a Model, and Interpreting Output and Equation |
Due 10/20 @ End of Class: ICA # 17 Due 10/22 @ End of Class: ICA # 18 Due SUNDAY, 10/25/26 @ 11:59 PM in Blackboard: Group Project Phase 3 & Peer Feedback Phase 3 |
| 10/26 | Module 7 Simple Linear Regression Part 2: Regression Residuals, Assumptions, Predictions, and Limitations | Tuesday: Midterm Case Study and Mock Trial In-Person Presentations Thursday: Module 7 Simple Linear Regression Part 2: Regression Residuals, Assumptions, Predictions, and Limitations |
Due 10/27 @ End of Class: ICA # 19 Due 10/29 @ End of Class: ICA # 20 Due SUNDAY, 11/1/26 @ 11:59 PM in Blackboard: Midterm Case Study and Mock Trial Analysis and Reflections |
| 11/2 | Module 8: Multiple Linear Regression | Tuesday: Module 7 Simple Linear Regression Excel Lab Thursday: Module 8 Multiple Linear Regression: Extend Simple Linear Regression, Dummy Variables, Interpreting Output, Multicollinearity, Predictions, Limitations |
Due 11/3 @ End of Class: ICA # 21 Due 11/5 @ End of Class: ICA # 22 Due SUNDAY, 11/8/26 @ 11:59 PM in Blackboard: Group Project Phase 4 & Peer Feedback Phase 4 |
| 11/9 | Module 9: Descriptive & Diagnostic Analytics Module 10: Predictive & Prescriptive Analytics |
Tuesday: Module 9: Descriptive & Diagnostic Analytics Applied Business Case Examples Thursday: Module 10: Predictive & Prescriptive Analytics Applied Business Case Examples |
Due 11/10 @ End of Class: ICA # 23 Due 11/12 @ End of Class: ICA # 24 |
| 11/16 | Tuesday: In-Class Workday for Group Project Thursday: In-Class Workday for Group Project |
Due 11/17 @ End of Class: ICA # 25 Due 11/19 @ End of Class: ICA # 26 Due SUNDAY, 11/22/26 @ 11:59 PM in Blackboard: Group Project Phase 5 & Peer Feedback Phase 5 |
|
| 11/23 | Tuesday: NO CLASS: Remote Workday for Group Project Thursday: NO CLASS UNIVERSITY HOLIDAY |
Due SUNDAY, 11/29/26 @ 11:59 PM in Blackboard: OPTIONAL EXTRA CREDIT # 1 | |
| 11/30 | Tuesday: In-Class Workday for Group Project Thursday: In-Class Workday for Group Project |
Due 12/1 @ End of Class: ICA # 27 Due 12/3 @ End of Class: ICA # 28 Due FRIDAY, 12/4/26 @ 11:59 PM in Blackboard: OPTIONAL EXTRA CREDIT #2 |
|
| 12/7 | Final Group Project In-Person Presentations: THURSDAY, 12/10/26 from 1:00 PM – 3:00 PM | Due WEDNESDAY, 12/9/26 @ 11:59 PM in Blackboard: Group Project Phase 6 Final & Peer Feedback Phase 6 Final Final Group Project In-Person Presentations: THURSDAY, 12/10/26 from 1:00 PM – 3:00 PM |
The course schedule, policies, procedures, and assignments in this course are subject to change in the event of extenuating circumstances, by mutual agreement, and/or to ensure better learning.