501 Data Science Questions: An essential checklist. Ask the right questions, avoid dead ends, and supercharge your project (Tiny Data Science)

Yarberry, Bill

ISBN 10: 1689568240 / ISBN 13: 9781689568241

Published by Independently published, 2019

About this Item

121 pages. 9.00x6.00x0.28 inches. In Stock.

Seller Inventory # zk1689568240

Bibliographic Details

• Title: 501 Data Science Questions: An essential checklist. Ask the right questions, avoid dead ends, and supercharge your project (Tiny Data Science)
• Publisher: Independently published
• Publication Date: 2019
• Language: English
• ISBN 10: 1689568240
• ISBN 13: 9781689568241
• Binding: Paperback
• Condition: Brand New
• Weight: 0.24 kilograms

About this title

Synopsis

501 Data Science Questions overclocks your planning, coding, and analysis of results

Simply by asking relevant questions inspired by the latest tools and algorithms, you’ll improve your project design and avoid pursuing “rabbit holes.”  What are your unknown unknowns? Are there important statistical packages, functions or algorithms you have forgotten or never used?

The unknown unknowns

This book does not include answers (otherwise it would be three feet thick).  Asking the right questions is the hard part. Once you have nailed down good questions, books and websites with detailed solutions are readily available. What is critical to your success is identifying a) what you already know but have not considered in your analysis and b) the “unknown unknowns”—approaches you are not aware of.

 

Both newbies and pros benefit from the right questions

Most data science books focus on either beginners or experienced practitioners. Here, everything is thrown in the mix. Newbies will learn from the simple questions and ignore the rest. Deeply experienced practitioners gain from a review of alternatives they may already know but have not brought to bear on their current projects.

 

With this unique set of questions, you’ll:  a) save time b) dramatically expand your awareness of real-world tools and algorithms, and c) assess limitations of models and algorithms.

 

• Model planning, building, and testing
• First cut sizing  of datasets
• Data preparation, transformation, cleansing, and validation
• Data anonymization
• Machine learning/deep learning
• Time series
• Survival analysis
• Text analytics
• Visualization techniques
• Technical infrastructure factors affecting efficiency and run times (real world stuff)

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No single action can save more time or improve your results more than asking the right questions and then thinking through all your options. Click the buy now button and get your copy of 501 Data Science Questions.