Numsense! Data Science for the Layman: A Book Review
Data science is a buzzword that has been used in various contexts and domains, but what does it really mean? How can we learn the basics of data science without getting bogged down by complex mathematical formulas and jargon? This is where Numsense! Data Science for the Layman: No Math Added by Annalyn Ng and Kenneth Soo comes in handy. This book is a gentle introduction to data science and its algorithms, written in layman's terms and accompanied by intuitive explanations and visuals.
The book covers 10 popular data science algorithms, each with its own dedicated chapter. The algorithms are:
Numsense! Data Science for the Layman: No Math Added Annalyn Ng
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A/B Testing
Anomaly Detection
Association Rules
Clustering
Decision Trees and Random Forests
Regression Analysis
Social Network Analysis
Neural Networks
For each algorithm, the authors explain how it works, what are its pros and cons, and what are some real-world applications where it can be used. They also provide a point summary at the end of each chapter, a reference sheet comparing the algorithms, and a glossary of commonly-used terms. The book does not require any prior knowledge of statistics or programming, and is suitable for anyone who wants to get a practical understanding of data science.
The book has been used as a reference text in top universities like Stanford and Cambridge, and has been sold in over 85 countries, translated into more than 5 languages[^1^] [^2^]. It has also received positive reviews from readers who praised its simplicity, clarity, and relevance[^2^] [^3^]. If you are looking for a book that can demystify data science for you, Numsense! Data Science for the Layman: No Math Added is a great choice.
Why should you read Numsense! Data Science for the Layman: No Math Added? Here are some reasons:
It is written in a clear and engaging style that makes data science accessible and fun.
It covers a wide range of algorithms that can be applied to various domains and industries.
It provides real-world examples and case studies that illustrate the power and impact of data science.
It helps you develop a data-driven mindset and a critical thinking approach to problem-solving.
If you are looking for more data science books to read, here are some recommendations based on your preferences and goals:
If you want to learn data science using Python, you can check out Data Science from Scratch: First Principles with Python by Joel Grus. This book covers the basics of data science using Python 3 and shows you how to implement solutions from scratch using a mix of statistics and coding.
If you want to learn data science using R, you can check out R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Garrett Grolemund and Hadley Wickham. This book covers the basics of data science using R and RStudio and shows you how to use popular packages such as ggplot2, tidyr, and more.
If you want to learn more about statistics for data science, you can check out Naked Statistics: Stripping the Dread from Data by Charles Wheelan. This book explains the core concepts of statistics in a simple and humorous way that makes them easy to understand and apply.
Data science is an exciting and rewarding field that can help you make better decisions and create value from data. Whether you are a beginner or an expert, there is always something new to learn and explore. We hope that this article has inspired you to read more data science books and expand your knowledge and skills.
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