Numsense! Data Science for the Layman: No Math Added

Numsense! Data Science for the Layman: No Math Added

Kenneth Soo, Annalyn Ng
$28.99
This product is not returnable.
Publication Date: March 24th, 2017
Publisher:
Annalyn Ng & Kenneth Soo
ISBN:
9789811110689
Pages:
146

Description

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 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.