Tap into the power of data science with this comprehensive resource for non-technical professionals
Data Science: The Executive Summary – A Technical Book for Non-Technical Professionals is a comprehensive resource for people in non-engineer roles who want to fully understand data science and analytics concepts. Accomplished data scientist and author Field Cady describes both the "business side" of data science, including what problems it solves and how it fits into an organization, and the technical side, including analytical techniques and key technologies.
Data Science: The Executive Summary covers topics like:
- Assessing whether your organization needs data scientists, and what to look for when hiring them
- When Big Data is the best approach to use for a project, and when it actually ties analysts’ hands
- Cutting edge Artificial Intelligence, as well as classical approaches that work better for many problems
- How many techniques rely on dubious mathematical idealizations, and when you can work around them
Perfect for executives who make critical decisions based on data science and analytics, as well as mangers who hire and assess the work of data scientists, Data Science: The Executive Summary also belongs on the bookshelves of salespeople and marketers who need to explain what a data analytics product does. Finally, data scientists themselves will improve their technical work with insights into the goals and constraints of the business situation.
Table of Contents:
1 Introduction 1
1.1 Why Managers Need to Know About Data Science 1
1.2 The New Age of Data Literacy 2
1.3 Data-Driven Development 3
1.4 How to Use this Book 4
2 The Business Side of Data Science 7
2.1 What Is Data Science? 7
2.2 Data Science in an Organization 19
2.3 Hiring Data Scientists 25
2.4 Management Failure Cases 36
3 Working with Modern Data 41
3.1 Unstructured Data and Passive Collection 41
3.2 Data Types and Sources 42
3.3 Data Formats 43
3.4 Databases 47
3.5 Data Analytics Software Architectures 50
4 Telling the Story, Summarizing Data 55
4.1 Choosing What to Measure 56
4.2 Outliers, Visualizations, and the Limits of Summary Statistics: A Picture IsWorth a Thousand Numbers 58
4.3 Experiments, Correlation, and Causality 60
4.4 Summarizing One Number 62
4.5 Key Properties to Assess: Central Tendency, Spread, and Heavy Tails 63
4.6 Summarizing Two Numbers: Correlations and Scatterplots 68
4.7 Advanced Material: Fitting a Line or Curve 72
4.8 Statistics: How to Not Fool Yourself 77
4.9 Advanced Material: Probability Distributions Worth Knowing 86
5 Machine Learning 101
5.1 Supervised Learning, Unsupervised Learning, and Binary Classifiers 102
5.2 Measuring Performance 107
5.3 Advanced Material: Important Classifiers 113
5.4 Structure of the Data: Unsupervised Learning 124
5.5 Learning as You Go: Reinforcement Learning 135
6 Knowing the Tools 141
6.1 A Note on Learning to Code 141
6.2 Cheat Sheet 142
6.3 Parts of the Data Science Ecosystem 143
6.4 Advanced Material: Database Query Crash Course 153
7 Deep Learning and Artificial Intelligence 161
7.1 Overview of AI 161
7.2 Neural Networks 164
7.3 Natural Language Processing 172
7.4 Knowledge Bases and Graphs 177
Postscript 181
Index 183
About the Author :
Field Cady, is a data scientist and author in the Seattle area. Most of his career has focused on consulting, for clients of all sizes in a range of industries. More recently he focused on using AI to mine scientific literature at the Allen Institute for Artificial Intelligence. His previous book, The Data Science Handbook, was published in 2017. His work has been covered in Wired, MIT Press and the Wall Street Journal among others.
