A Guide for Anyone Who Works with Data

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 teach to a computer. 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.

We sincerely believe that this book should be useful for anyone who has to work with data

If you are interested in obtaining a printed copy of this book, you can purchase one at Lulu.

Roger D. Peng

Roger D. Peng is an Associate Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He is also a Co-Founder of the Johns Hopkins Data Science Specialization, which has enrolled over 1.5 million students, and the Simply Statistics blog where he writes about statistics for the general public. Roger can be found on Twitter and GitHub @rdpeng.

Elizabeth Matsui

Elizabeth Matsui is a Professor of Pediatrics, Epidemiology and Environmental Health Sciences at Johns Hopkins University and a practicing pediatric allergist/immunologist. She directs a data management and analysis center with Dr. Peng that supports epidemiologic studies and clinical trials and is co-founder of Skybrude Consulting, LLC, a data science consulting firm. Elizabeth can be found on Twitter @eliza68.

  1. Data Analysis as Art
  2. Epicycles of Analysis
  3. Stating and Refining the Question
  4. Exploratory Data Analysis
  5. Using Models to Explore Your Data
  6. Inference: A Primer
  7. Formal Modeling
  8. Inference vs. Prediction: Implications for Modeling Strategy
  9. Interpreting Your Results
  10. Communication
  11. Concluding Thoughts