BUSI 305 Business Analysis for Decision Making

Data analysis is an essential element of any business, whether it provides a product or service to its customers. The field is both evolving and growing. This course is an introduction to the field of data analysis. The topics covered include both strategic issues and practical applications. Specific aspects of data analysis to be considered are data sourcing, statistical analysis, exploratory analysis, confirmatory analysis, marketing analytics, operational analytics, financial analytics, and reporting findings and results.

Textbook readings and lecture presentations

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Course Requirements Checklist

After reading the Course Syllabus and Student Expectations, the student will complete the related checklist found in the Course Overview.

Read & Interact Assignments (9)

Each student will complete a Read & Interact assignment covering the chapters assigned in each Module: Week. The Read & Interact assignments provide the student an opportunity to review concepts discussed in the reading material for the assigned Module: Week(s).

SWOT and TOWS Analysis Assignment

The student will upload an APA formatted MS Word document with a SWOT and SWOT Bivariate (TOWS) Matrix created in the form of a chart.

IFE, EFE, and IE Matrix Submission Assignment

Using the same corporation used for the SWOT Matrix, the student will research and create an IFE, EFE, and IE Matrix.

Lab Assignments (7)

The student will complete 7 labs utilizing excel data sets that are uploaded and manipulated utilizing Tableau Desktop. A brief description of each lab is listed below: 

• Tableau Lab 3.2 – Calculating and utilizing descriptive statistics to analyze sales data.
• Tableau Lab 3.5 – Performing Regression Analysis on data to better understand cost drivers and how to be more efficient with overhead allocation.
• Tableau Lab 5.4 – Using Predictive Analytics to forecast product demand and identify trends in the data.
• Tableau Lab 6.3 – Combining Descriptive and Diagnostic Analytics to perform a drill down analysis into sales revenues.
• Tableau Lab 7.6 – Use regression and correlation to determine the relationship between a data set containing advertising expenses and sales revenues.
• Tableau Lab 8.6 – Use Tableau tools to forecast future sales and earnings and understand why forecasting earnings is important.
• Tableau Lab 10.5 – Using Predictive Analytics, forecast the demand for a hoverboard product, and determine the relationship between time and unit demand.

SOAR Data Case Project Assignment

The student will follow along with Project 12.3 – Completing Your Own Project Using the SOAR Analytics Model (p. 855-857). The only deliverable element that the student is responsible for is the written report.