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Numsense! Data Science for the Layman: No Math Added by [Ng, Annalyn, Soo, Kenneth]
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Numsense! Data Science for the Layman: No Math Added Kindle Edition

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Length: 147 pages Word Wise: Enabled Enhanced Typesetting: Enabled
Page Flip: Enabled Language: English
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Product description

Product Description

Reference text for data science in top universities like Stanford and Cambridge.
Sold in over 85 countries and translated into more than 5 languages.


Want to get started on data science?
Our promise: no math added.

This book has been written in layman's terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations and visuals.

Popular concepts covered include:

- A/B Testing
- Anomaly Detection
- Association Rules
- Clustering
- Decision Trees and Random Forests
- Regression Analysis
- Social Network Analysis
- Neural Networks


- Intuitive explanations and visuals
- Real-world applications to illustrate each algorithm
- Point summaries at the end of each chapter
- Reference sheets comparing the pros and cons of algorithms
- Glossary list of commonly-used terms

With this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions.

About the Author

Annalyn Ng graduated from the University of Michigan (Ann Arbor), where she also was an undergraduate statistics tutor. She then completed her MPhil degree with the University of Cambridge Psychometrics Centre, where she mined social media data for targeted advertising and programmed cognitive tests for job recruitment. Disney Research later roped her into their behavioral sciences team, where she examined psychological profiles of consumers.

Kenneth Soo is due to complete his MS degree in Statistics at Stanford University by mid-2017. He was the top student for all three years of his undergraduate class in Mathematics, Operational Research, Statistics and Economics (MORSE) at the University of Warwick, where he was also a research assistant with the Operational Research & Management Sciences Group, working on bi-objective robust optimization with applications in networks subject to random failures.

Product details

  • Format: Kindle Edition
  • File Size: 13979 KB
  • Print Length: 147 pages
  • Sold by: Amazon Australia Services, Inc.
  • Language: English
  • ASIN: B01N29ZEM6
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Enabled
  • Screen Reader: Supported
  • Enhanced Typesetting: Enabled
  • Average Customer Review: Be the first to review this item
  • Amazon Bestsellers Rank: #53,989 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Most helpful customer reviews on 4.7 out of 5 stars 87 reviews
Sarah Sarah
5.0 out of 5 starsGreat resource
25 July 2017 - Published on
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8 people found this helpful
Tathagata Dasgupta
5.0 out of 5 starsBuilds a vocabulary for business leaders who want to leverage Data Science to make business decisions
27 July 2017 - Published on
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6 people found this helpful
Fábio de Salles
4.0 out of 5 starsThe book to go for basics of/introduction to Data Science - a must have!
11 June 2018 - Published on
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2 people found this helpful
Michael D. Pechner
5.0 out of 5 starsA useful read.
10 September 2018 - Published on
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3 people found this helpful
4.0 out of 5 starsGood Overview of Data Science
3 March 2019 - Published on
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One person found this helpful