Numsense! Data Science for the Layman: No Math Added - Tapa blanda

Used as course material 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, as well as lots of visuals, all of which are colorblind-friendly.

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.

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.