Numsense! Data Science for the Layman: No Math Added

Soo, Kenneth,Ng, Annalyn

ISBN 10: 9811110689 / ISBN 13: 9789811110689

Published by Annalyn Ng & Kenneth Soo, 2017

About this Item

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Bibliographic Details

• Title: Numsense! Data Science for the Layman: No Math Added
• Publisher: Annalyn Ng & Kenneth Soo
• Publication Date: 2017
• Language: English
• ISBN 10: 9811110689
• ISBN 13: 9789811110689
• Binding: Paperback
• Condition: Good

About this title

Synopsis

Reference text in top universities like Stanford and Cambridge
Sold in over 85 countries, 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

Features:

• 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 is an AI/ML specialist at Google Cloud. Her data science career spans across commercial and public sectors; she has held roles in Amazon, Disney Research, as well as in the Singapore government, specifically in the manpower and military ministries. Annalyn earned her bachelor's degree from the University of Michigan (Ann Arbor), where she also volunteered as an undergraduate statistics tutor, and subsequently completed her MPhil degree at the University of Cambridge.
 
Kenneth Soo has 7 years of experience applying data science to public policy for the Singapore government, and he currently leads a team managing bilateral relations with European partners. During the COVID-19 pandemic, he drove nationwide digitalization initiatives for Singapore's Smart Nation and Digital Government Office, including the implementation of contact tracing systems. He completed his MS degree in Statistics at Stanford University, and 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.