Applying Business Analytics
Online Certification
Michigan State University
MSU Programs > Business Analytics > Applying Business Analytics
Course Overview
Instructor: Dr. Bronlyn Wassink
Course Objectives
After successfully completing this course, students will be able to:
-
Explore data-driven science
-
Compare averages between populations
-
Calculate simple linear regression
-
Practice working with exponential, logarithmic, and power regression models
-
Create and interpret multiple linear regression models
-
Create and interpret logistic regression models
-
Analyze text, networks, location and imagery data
Students will meet the course objectives through the following actions:
-
Completing learning content pages, which includes
-
-
Watching videos
-
Reading text and studying charts and tables
-
Listening to audio
-
-
Posting to the discussion board
-
Completing eight exams
Course Requirements
-
Internet connection (DSL, LAN, or cable connection desirable)
-
Access to Canvas
-
Read content, watch videos, listen to audio, and complete assignments
-
Microsoft Excel
Course Structure
The content will be delivered in Canvas with a variety of media components. Students will need to be able to play video.
Course Outline/Schedule
Module 1: Statistics - A Data-Driven Science
-
Variables
-
Inferential Statistics Techniques
-
Confidence Intervals
Module 2: Extracting Data from a Database
-
The four Vs of data
-
Different types of data sources generated by the "Internet of Things"
-
The creation and/or magnitude of change analytics can create within an industry and a firm
Module 3: Comparing Populations - t-Tests and ANOVA
-
p-Values
-
Comparing average values of two populations: t-tests
-
Comparing average values of multiple populations: ANOVA
Module 4: Exponential, Logarithmic, and Power Regression
-
Exponential model
-
Non-linear models
Module 5: Multiple Linear Regression
-
Multiple linear regression
-
Categorical variables
-
Quantitative variables
Module 6: Data Mining and Inferential Statistics
-
Comparing percentages between two population
-
Logistic regression
Module 7: Analyzing Text and Networks
-
Text Analytics One
-
Text Analytics Two
-
Network Analysis
-
Spatial-Temporal Analysis
Module 8: Locations and Imagery Data
-
Mobile Location Based Analysis One
-
Mobile Location Based Analysis Two
-
Imagery Analytics
-
Redux – Data Visualization