Ebook Numsense! Data Science for the Layman: No Math Added, by Annalyn Ng, Kenneth Soo
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Numsense! Data Science for the Layman: No Math Added, by Annalyn Ng, Kenneth Soo
Ebook Numsense! Data Science for the Layman: No Math Added, by Annalyn Ng, Kenneth Soo
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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.
- Sales Rank: #159112 in Books
- Published on: 2017-03-24
- Original language: English
- Dimensions: 9.00" h x .35" w x 6.00" l,
- Binding: Paperback
- 146 pages
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.
Most helpful customer reviews
3 of 3 people found the following review helpful.
Excellent introduction to the basic algorithms and techniques
By Nicholas
I've been trying to teach myself data science for a while now using a combination of textbooks and online tutorials, but more often than not I have been frustrated by a lack of 'true' understanding of the logic underlying the algorithms. While I could maximize the Lagrangians, find the eigenvectors and reproduce most of the proofs in the textbooks, I never really 'got' how the algorithms worked. Ng and Soo's book is thus a perfect complement to the math-heavy textbooks: it omits all the equations (there are already many other books for that) and presents just the intuition in crisp prose and thoughtfully designed graphics. Highly recommended for any beginner in data science.
2 of 2 people found the following review helpful.
Five Stars
By Amazon Customer
Love this book! Amazingly written and very easy to digest even the most complex and confusing algorithms. Good job!!
2 of 2 people found the following review helpful.
The book is worthy of its name
By Hang
The book presented hard-to-understand topic in the friendly way. I would recommend this book to people who need to have an overview of algorithm used in data mining field.
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