Numsense! Data Science for the Layman: No Math Added

Numsense! Data Science for the Layman: No Math Added - Kenneth Soo

Numsense! Data Science for the Layman: No Math Added


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.

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PRP: 224.67 Lei

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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.

Citeste mai mult

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