Data analysis is a difficult process largely because few people can describe exactly how to do it. It's not that there aren't any people doing data analysis on a regular basis. It's that the process by which we state a question, explore data, conduct formal modeling, interpret results, and communicate findings, is a difficult process to generalize and abstract. Fundamentally, data analysis is an art. It is not yet something that we can easily automate. Data analysts have many tools at their disposal, from linear regression to classification trees to random forests, and these tools have all been carefully implemented on computers. But ultimately, it takes a data analyst—a person—to find a way to assemble all of the tools and apply them to data to answer a question of interest to people.
This book writes down the process of data analysis with a minimum of technical detail. What we describe is not a specific "formula" for data analysis, but rather is a general process that can be applied in a variety of situations. Through our extensive experience both managing data analysts and conducting our own data analyses, we have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of our experience in a format that is applicable to both practitioners and managers in data science.
Author(s): Roger Peng, Elizabeth Matsui
Edition: null
Publisher: Lulu.com
Year: 2018
Language: English
Pages: 163
Table of Contents
Data Analysis as Art
Epicycles of Analysis
Setting the Scene
Epicycle of Analysis
Setting Expectations
Collecting Information
Comparing Expectations to Data
Applying the Epicycle of Analysis Process
Stating and Refining the Question
Types of Questions
Applying the Epicycle to Stating and Refining Your Question
Characteristics of a Good Question
Translating a Question into a Data Problem
Case Study
Concluding Thoughts
Exploratory Data Analysis
Exploratory Data Analysis Checklist: A Case Study
Formulate your question
Read in your data
Check the Packaging
Look at the Top and the Bottom of your Data
ABC: Always be Checking Your ``n''s
Validate With at Least One External Data Source
Make a Plot
Try the Easy Solution First
Follow-up Questions
Using Models to Explore Your Data
Models as Expectations
Comparing Model Expectations to Reality
Reacting to Data: Refining Our Expectations
Examining Linear Relationships
When Do We Stop?
Summary
Inference: A Primer
Identify the population
Describe the sampling process
Describe a model for the population
A Quick Example
Factors Affecting the Quality of Inference
Example: Apple Music Usage
Populations Come in Many Forms
Formal Modeling
What Are the Goals of Formal Modeling?
General Framework
Associational Analyses
Prediction Analyses
Summary
Inference vs. Prediction: Implications for Modeling Strategy
Air Pollution and Mortality in New York City
Inferring an Association
Predicting the Outcome
Summary
Interpreting Your Results
Principles of Interpretation
Case Study: Non-diet Soda Consumption and Body Mass Index
Communication
Routine communication
The Audience
Content
Style
Attitude
Concluding Thoughts
About the Authors
