Numsense! Data Science for the Layman No Math Added

Used in Stanford's CS102 Big Data (Spring 2017) course.

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.

Paperback: 146 pages

Publisher: Annalyn Ng & Kenneth Soo; 1 edition (March 24, 2017)

Language: English

Product Dimensions: 6 x 0.4 x 9 inches

Shipping Weight: 9.9 ounces

Review

"... Having been familiar with the work of Annalyn Ng and Kenneth Soo for some time, it comes as no surprise that the book delivers on its titular promise. This is data science for the layman, and the often-complex math--which the book describes at a high level--is intentionally not covered in detail. But don't be misled: this does not mean that the contents are in any way watered down. In fact, the information contained within is robust, with its strength being that it is abridged and concise..."

- Matthew Mayo

Data Scientist and Deputy Editor of KDnuggets

"... Numsense! is a convenient graphical description of key data science algorithms, useful as an introduction for new data scientists, an overview for business people who work with analysts, or a stimulating read for anyone who wants to know what happens to their data."

- Dr. David Stillwell

Deputy Director of The Psychometrics Centre,

Lecturer in Big Data Analytics and Quantitative Social Science,

Cambridge University Judge Business School

"This is a great book. It is hard to explain data science without the math but this book does an amazing job. It balances both the simplicity and the depth."

- Ajit Jaokar

Data Science for Internet of Things

University of Oxford

"Numsense's excellent visualizations of machine learning concepts helped students coming from non-technical backgrounds to grasp these abstract concepts intuitively. It presents such a succinct and precise summary for what non-technical students need to know while navigating the world of data science for the first time."

- Ethan Chan

Lecturer for CS102 Big Data

Stanford University

"While there is no Royal Road to machine learning and data science, Numsense! comes pretty close--with plenty of figures and relatable examples, it succeeds in covering most important techniques in a clear, intuitive way that is perfect for novices and those seeking to improve their practice alike. I recommend Numsense! as a fantastic way to optimize your machine learning learning function!"

- Barton Yadlowski

Data Scientist at Pandata LLC

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.