# Michigan State University

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)

• 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