Exploratory Data Analysis

Exploratory data analysis (EDA) is an investigative process in which you use summary statistics and graphical tools to get to know your data and understand what you can learn from it.

With EDA, you can uncover patterns in your data, understand potential relationships between variables, and find anomalies, such as outliers or unusual observations. The goal is to generate interesting questions or hypotheses that you can test using more formal statistical methods.

Estimated time to complete this module: 6 to 7 hours

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Specific topics covered in this module include:

Describing Data

Introduction to Descriptive Statistics
Types of Data
Histograms
Measures of Central Tendency and Location
Measures of Spread — Range and Interquartile Range
Measures of Spread — Variance and Standard Deviation
Visualizing Continuous Data
Describing Categorical Data

Probability Concepts

Introduction to Probability Concepts
Samples and Populations
Understanding the Normal Distribution
Checking for Normality
The Central Limit Theorem

Exploratory Data Analysis for Problem Solving

Introduction to Exploratory Data Analysis
Exploring Continuous Data: Enhanced Tools
Pareto Plots
Packed Bar Charts and Data Filtering
Tree Maps and Mosaic Plots
Using Trellis Plots and Overlay Variables
Bubble Plots and Heat Maps
Summary of Exploratory Data Analysis Tools

Communicating with Data

Introduction to Communicating with Data
Creating Effective Visualizations
Evaluating the Effectiveness of a Visualization
Designing an Effective Visualization
Communicating Visually with Animation
Designing for Your Audience
Understanding Your Target Audience
Designing Visualizations for Communication
Designing Visualizations: The Do's and Don'ts

Saving and Sharing Results

Introduction to Saving and Sharing Results
Saving and Sharing Results in JMP
Saving and Sharing Results Outside of JMP
Deciding Which Format to Use

Data Preparation for Analysis

Data Tables Essentials
Common Data Quality Issues
Identifying Issues in the Data Table
Identifying Issues One Variable at a Time
Restructuring Data for Analysis
Combining Data
Deriving New Variables
Working with Dates