MSU Programs > Business Analytics > Applying Business Analytics

Course Overview

Instructor: Dr. Bronlyn Wassink

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Course Objectives 

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After successfully completing this course, students will be able to:

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• 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

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Students will meet the course objectives through the following actions:

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• 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

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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

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Course Structure

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The content will be delivered in Canvas with a variety of media components. Students will need to be able to play video.

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Course Outline/Schedule

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Module 1: Statistics - A Data-Driven Science

• Variables

• Inferential Statistics Techniques

• Confidence Intervals

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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

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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

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Module 4: Exponential, Logarithmic, and Power Regression

• Exponential model

• Non-linear models

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Module 5: Multiple Linear Regression

• Multiple linear regression

• Categorical variables

• Quantitative variables

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Module 6: Data Mining and Inferential Statistics

• Comparing percentages between two population

• Logistic regression

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Module 7: Analyzing Text and Networks

• Text Analytics One

• Text Analytics Two

• Network Analysis

• Spatial-Temporal Analysis

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Module 8: Locations and Imagery Data

• Mobile Location Based Analysis One

• Mobile Location Based Analysis Two

• Imagery Analytics

• Redux – Data Visualization