The Art of Data Science
A Guide for Anyone Who Works with Data
This book describes the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and this book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science. Printed copies are available through Lulu.
This book describes the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and this book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science. Printed copies are available through Lulu.
About
About the Book
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.
If you are interested in obtaining a printed copy of this book, you can purchase one at Lulu.
The package containing the lecture videos offers short commentaries on each of the chapters and contains addtional explanatory material for each of the topics. In addition there is some material in the lectures that is not included in the book.
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The Book + Lecture Videos
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Suggested price30ドル.00This package includes the book and lecture video files. The videos and chapters are aligned so that together they make an ideal self-learning curriculum in which students interested in data science can pair video lectures with reading material. The videos complement the reading material by extending concepts covered in the book and by providing visual and auditory presentation of the concepts. This self-guided curriculum can be covered at any pace and the completion of material should provide students with a solid foundation for thinking about the data science process. The complete package should be of interest to students interested in doing their own data analyses and to people who need to manage data science teams.
$20.00
- Lecture Videos
Contents
Table of Contents
1.Data Analysis as Art
2.Epicycles of Analysis
- 2.1Setting the Scene
- 2.2Epicycle of Analysis
- 2.3Setting Expectations
- 2.4Collecting Information
- 2.5Comparing Expectations to Data
- 2.6Applying the Epicycle of Analysis Process
3.Stating and Refining the Question
- 3.1Types of Questions
- 3.2Applying the Epicycle to Stating and Refining Your Question
- 3.3Characteristics of a Good Question
- 3.4Translating a Question into a Data Problem
- 3.5Case Study
- 3.6Concluding Thoughts
4.Exploratory Data Analysis
- 4.1Exploratory Data Analysis Checklist: A Case Study
- 4.2Formulate your question
- 4.3Read in your data
- 4.4Check the Packaging
- 4.5Look at the Top and the Bottom of your Data
- 4.6ABC: Always be Checking Your "n"s
- 4.7Validate With at Least One External Data Source
- 4.8Make a Plot
- 4.9Try the Easy Solution First
- 4.10Follow-up Questions
5.Using Models to Explore Your Data
- 5.1Models as Expectations
- 5.2Comparing Model Expectations to Reality
- 5.3Reacting to Data: Refining Our Expectations
- 5.4Examining Linear Relationships
- 5.5When Do We Stop?
- 5.6Summary
6.Inference: A Primer
- 6.1Identify the population
- 6.2Describe the sampling process
- 6.3Describe a model for the population
- 6.4A Quick Example
- 6.5Factors Affecting the Quality of Inference
- 6.6Example: Apple Music Usage
- 6.7Populations Come in Many Forms
7.Formal Modeling
- 7.1What Are the Goals of Formal Modeling?
- 7.2General Framework
- 7.3Associational Analyses
- 7.4Prediction Analyses
- 7.5Summary
8.Inference vs. Prediction: Implications for Modeling Strategy
- 8.1Air Pollution and Mortality in New York City
- 8.2Inferring an Association
- 8.3Predicting the Outcome
- 8.4Summary
9.Interpreting Your Results
- 9.1Principles of Interpretation
- 9.2Case Study: Non-diet Soda Consumption and Body Mass Index
10.Communication
- 10.1Routine communication
- 10.2The Audience
- 10.3Content
- 10.4Style
- 10.5Attitude
11.Concluding Thoughts
About the Authors
Contributor
About the Contributors
Maggie Matsui
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