Numsense! Data Science for the Layman: No Math Added

Numsense! Data Science for the Layman: No Math Added

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Features

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Description

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'

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.

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