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Thinking with Data
作者 Max Shron
出版社 O'Reilly Media
ISBN 9781449362935
分類 Computer & Information Technology > Information Technology
價格 HK$190.00
 
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Many analysts are too concerned with tools and techniques for cleansing, modeling, and visualizing datasets and not concerned enough with asking the right questions. In this practical guide, data strategy consultant Max Shron shows you how to put the why before the how, through an often-overlooked set of analytical skills.

Thinking with Data helps you learn techniques for turning data into knowledge you can use. You’ll learn a framework for defining your project, including the data you want to collect, and how you intend to approach, organize, and analyze the results. You’ll also learn patterns of reasoning that will help you unveil the real problem that needs to be solved.

* Learn a framework for scoping data projects
* Understand how to pin down the details of an idea, receive feedback, and begin prototyping
* Use the tools of arguments to ask good questions, build projects in stages, and communicate results
* Explore data-specific patterns of reasoning and learn how to build more useful arguments
* Delve into causal reasoning and learn how it permeates data work
* Put everything together, using extended examples to see the method of full problem thinking in action


About the Author:

Max Shron runs a small data strategy consultancy in New York, working with many organizations to help them get the most out of their data. His analyses of transit, public health, and housing markets has been featured in The New York Times, Chicago Tribune, Huffington Post, WNYC, and more. Prior to becoming a data strategy consultant, he was the data scientist for OkCupid.


Chapter 1 Scoping: Why Before How
Context (Co)
Needs (N)
Vision (V)
Outcome (O)
Seeing the Big Picture

Chapter 2 What Next?
Refining the Vision
Deep Dive: Real Estate and Public Transit
Deep Dive Continued: Working Forward
Deep Dive Continued: Scaffolding
Verifying Understanding
Getting Our Hands Dirty

Chapter 3 Arguments
Audience and Prior Beliefs
Claims
Evidence, Justification, and Rebuttals
Deep Dive: Improving College Graduation Rates

Chapter 4 Patterns of Reasoning
Categories of Disputes
General Topics
Special Arguments

Chapter 5 Causality
Defining Causality
Designs
Intervention Designs
Observational Designs
Natural Experiments
Statistical Methods

Chapter 6 Putting It All Together
Deep Dive: Predictive Model for Conversion Probability
Deep Dive: Calculating Access to Microfinance
Wrapping Up

Appendix Further Reading


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