Applying Business Analytics

Online Certification

Michigan State University

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