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Numsense! Data Science for the Layman: No Math Added Kindle Edition

by Annalyn Ng (Author), Kenneth Soo (Author) Format: Kindle Edition
4.5 out of 5 stars 680 ratings

Reference text in top universities like Stanford and Cambridge
Sold in over 85 countries, 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 and visuals.

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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  1. Print length
    147 pages
  2. Language
    English
  3. Accessibility
    Learn more
  4. Publication date
    February 3, 2017
  5. File size
    13.7 MB
  6. Page Flip
    Enabled
  7. Word Wise
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  8. Enhanced typesetting
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Editorial Reviews

Review

"... 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
University of Cambridge


"... this is an excellent book to 'de-mystify' the black box of advanced statistics for business leaders who are launching studies that leverage big data."
-- Dr. Tathagata Dasgupta
Head of Data Science & Advanced Analytics
Viacom


"Numsense's examples illustrate and illuminate, but the real clarity comes from the direct writing style... This book is not just for the non-technical reader -- any programmer looking to understand data science and machine learning will quickly gain an appreciation of algorithms that other works introduce in a far less digestible form." (5/5 stars)
-- The Magpi
Leading UK tech magazine


"... 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 Editor
KDnuggets


"I like the book and highly recommend Numsense! by Ng and Soo for anyone who would like to grasp the essence of data science and machine learning, but do not want to be bugged down by mathematical and programming details... Numsense! strikes a balance between breadth and depth of data science and gives no nonsense introduction to the field."
-- Prof. Chuin-Shan Chen
Department of Civil Engineering
National Taiwan University


"... 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."
-- Barton Yadlowski
Senior Data Scientist
Pandata LLC


"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


"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

From the Author

Numsense! is dedicated to you by two data science enthusiasts, Annalyn Ng (University of Cambridge) and Kenneth Soo (Stanford University).

We noticed that while data science is increasingly used to improve workplace decisions, many know little about the field. Hence, we compiled these tutorials into a book so that more people can learn--be it an aspiring student, enterprising business professional, or anyone with a curious mind.

Product details

  • ASIN ‏ : ‎ B01N29ZEM6
  • Accessibility ‏ : ‎ Learn more
  • Publication date ‏ : ‎ February 3, 2017
  • Edition ‏ : ‎ 1st
  • Language ‏ : ‎ English
  • File size ‏ : ‎ 13.7 MB
  • Screen Reader ‏ : ‎ Supported
  • Enhanced typesetting ‏ : ‎ Enabled
  • X-Ray ‏ : ‎ Enabled
  • Word Wise ‏ : ‎ Enabled
  • Print length ‏ : ‎ 147 pages
  • ISBN-13 ‏ : ‎ 978-9811127007
  • Page Flip ‏ : ‎ Enabled
  • Customer Reviews:
    4.5 out of 5 stars 680 ratings

About the authors

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  1. Annalyn Ng

    Annalyn Ng

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    Annalyn Ng is an AI/ML specialist at Google Cloud. Her data science career spans across commercial and public sectors; she has held roles in Amazon, Disney Research, as well as in the Singapore government, specifically in the manpower and military ministries. Annalyn earned her bachelor's degree from the University of Michigan (Ann Arbor), where she also volunteered as an undergraduate statistics tutor, and subsequently completed her MPhil degree at the University of Cambridge.

    See more on the author's page
  2. Kenneth Soo

    Kenneth Soo

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    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.

    See more on the author's page
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Customer reviews

4.5 out of 5 stars
680 global ratings

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Customers say

Customers find the book to be a great introductory guide to data analysis, providing a concise summary of machine learning techniques. Moreover, the book is easy to read, with one customer noting it's beautifully written without jargon. Additionally, they appreciate its readability and consider it a must-read, and one customer mentions it's well worth the time and money.

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53 customers mention "Information quality"53 positive0 negative

Customers find the book informative, particularly as an introductory guide for data analysis novices, providing a concise summary of machine learning techniques and interesting analyses.

"...and managers of data science teams, to give them enough depth for meaningful discussions with the data scientists and ML engineers on the team, to..." Read more

"...BI field for almost two decades, this book is by far the best introduction to Data Mining..." Read more

"...the topics covered. It gives descriptions, analyses, and insights about the most popular algorithms on various topics, and it covers..." Read more

"Easy to follow. Explores applications and describes strengths and weaknesses. I especially liked the summaries at the end of each chapter." Read more

46 customers mention "Ease of reading"40 positive6 negative

Customers find the book easy to read and understand, with concise and crisp prose that makes it accessible to non-experts.

"...This book is written at the perfect level of details for technical product managers and managers of data science teams, to give them enough depth..." Read more

"...The book has an almost highschool structure, easy to read and understand...." Read more

"Easy to follow. Explores applications and describes strengths and weaknesses. I especially liked the summaries at the end of each chapter." Read more

"...The book is well-written and edited, and the illustrations look amazing and work well together with the text...." Read more

19 customers mention "Readability"19 positive0 negative

Customers find the book superb and must read, with one customer noting that the content works well with the text.

"This is an excellent book both in depth and breadth of the topics covered. It gives descriptions, analyses, and insights..." Read more

"Good book but not worth the high price tag." Read more

"...accessible the concepts in the book are, as the authors have done a great job in condensing the wealth of information into very easily understood..." Read more

"...machine learning models are generally computing results, this is a great book...." Read more

6 customers mention "Value for money"6 positive0 negative

Customers find the book provides good value for money.

"...It is a quick read and the ROI (return on investment) is high." Read more

"...This was great. The kindle version was my first. Excellent experience." Read more

"...I am about halfway through and so far it is well worth the time and money...." Read more

"...Great value!" Read more

Top reviews from the United States

  • S. Tzanev
    5.0 out of 5 stars The best introductory book for technology managers
    Reviewed in the United States on June 6, 2020
    Verified Purchase
    I am an experienced software product manager and ex-software developer. As of recently I've been involved in managing products that use ML and in managing data science teams.
    I've tried many different ways to educate myself in the field of AI/ML and data science. Unfortunately, most of the educational materials (books, online courses, articles, webinars,...) fall in two major categories - either too shallow (high level concepts and buzz words, making them unsuitable for practice) or too technical (making them suitable only for aspiring data scientists or ML engineers).
    This book is written at the perfect level of details for technical product managers and managers of data science teams, to give them enough depth for meaningful discussions with the data scientists and ML engineers on the team, to be able to effectively apply product/project management skills to ML projects, and to gain the credibility and trust needed for staying in control of the direction and execution of the project.
    In addition, the language and organization of the book are top notch.
    This is the best book on data science I've found so far for my needs as a product manager. Strongly recommend.
    2 people found this helpful
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  • Fábio de Salles
    4.0 out of 5 stars The book to go for basics of/introduction to Data Science - a must have!
    Reviewed in the United States on June 10, 2018
    Verified Purchase
    Forget the nonsense of IT media! Read Numsense and get to understand what "Data Science" is all about! Being in the BI field for almost two decades, this book is by far the best introduction to Data Mining (the real name behind buzzwords and hype like Machine Learning and Data Science.)

    If you are already schooled in Statistics and Mathmatic model developement,this book will be of no help.

    If, however, you don't know anything about how to use data to improve business and answer questions, this is your book. You're in to get a stream of "a-ha!" moments.

    The book has an almost highschool structure, easy to read and understand. Each method is introduced by describing the problem it solves best - forecasting for regression, profiling for clusters and so on. Then it solves the problem using a high level, descriptive analysis. After problem is solved some concepts are made clearer or in a more formal language. And that's it. At the end you are asking for more because it was sooo nice!
    18 people found this helpful
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  • Amazon Customer
    5.0 out of 5 stars Excellent breadth as well as depth
    Reviewed in the United States on February 10, 2017
    Verified Purchase
    This is an excellent book both in depth and breadth of
    the topics covered. It gives descriptions, analyses, and insights
    about the most popular algorithms on various topics, and it covers
    many more areas than most books. The book is well integrated across
    the broad diversity of topics that are covered, and connections between
    methods and topics are pointed out throughout the book. For most of the
    important topics, a lot of detail is provided in terms of algorithm description
    and pseudo-code. In some cases, interesting analyses are also provided.
    For instance, in the case of frequent pattern mining algorithms,
    not only are more algorithms discussed
    than most of the other books, but a discussion of multiple choices
    of data structures for the same algorithm is provided,
    along with their relative trade-offs. The relationships among various
    algorithms are also discussed. I have seen quite a
    few textbooks on data mining, and I have not seen anything close to this
    level of detail in any of the other books. Overall, my impression is
    that the authors have done an excellent job of calibrating detail level
    to topic importance. Therefore, it can serve both as a textbook and
    as a reference book. On the other hand, this is certainly
    not an implementation or programming-centric book. The book is good at
    teaching principles and concepts.
    4 people found this helpful
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  • Roy
    5.0 out of 5 stars Excellent Introduction
    Reviewed in the United States on June 3, 2024
    Verified Purchase
    Easy to follow. Explores applications and describes strengths and weaknesses. I especially liked the summaries at the end of each chapter.
    Helpful
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  • F. Medeiros
    3.0 out of 5 stars Overpriced
    Reviewed in the United States on December 9, 2018
    Verified Purchase
    Good book but not worth the high price tag.
    Helpful
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  • Xuan Hao
    5.0 out of 5 stars Great entry level book for Data Science, but also great for people with experience in the subject!
    Reviewed in the United States on February 12, 2017
    Verified Purchase
    The book is really what it's described as: data science for the layman, without any math! I'm pleasantly surprised at how accessible the concepts in the book are, as the authors have done a great job in condensing the wealth of information into very easily understood ideas.
    The book is well-written and edited, and the illustrations look amazing and work well together with the text. The examples chosen made it easier for me to understand the concepts.
    As someone without any data science background, this book was definitely a great read for me! Even for someone who has experience in data science, I feel that after reading the book, it'll be easier for you to share the subject with your friends through the simplified concepts and relatable examples. Would definitely recommend the book!
    3 people found this helpful
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  • Anonamouse
    4.0 out of 5 stars Good Overview of Data Science
    Reviewed in the United States on March 2, 2019
    Verified Purchase
    Too many writings, I've read on data science tend to instantly delve into the weeds and yet never cover what the methodologies really are, much less when and why to use the methodology. Not with Numsense. This is a well-written book that does the opposite - it tells what and why for each methodology.

    For me it would have been better if the examples were more focused on other areas other than business & marketing, such as manufacturing. I can put a few uses cases together, though.

    Overall, very good book on the topic and one in which every manager should add to their immediate reading list. Unless they are already leading data science in their organization.
    2 people found this helpful
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Top reviews from other countries

  • Client Kindle
    5.0 out of 5 stars Excellent overview for beginners
    Reviewed in France on November 16, 2019
    Verified Purchase
    I highly recommend this book, that was very useful in my case. It describes all the techniques in common word, so that you'll be able to dig in the good direction.
    Report
  • Mick S
    5.0 out of 5 stars Data science for dummy and easy
    Reviewed in Germany on June 12, 2021
    Verified Purchase
    Very good and easy to understand the explanation..
    Report
  • carlo occhiena
    5.0 out of 5 stars TERRIFIC VALUE FOR SO LITTLE PRICE!
    Reviewed in Italy on August 24, 2020
    Verified Purchase
    what a terrific book. One of the best I ever read, it really gets you going in a few pages.
    It cover all the most relevant topics related to DS, including the jargon, the most common algos, the best practice. All is simply written, understandable, actionable.
    It's the kind of book I wished I could have read 10 years ago, long time ago before cracking my head open on more difficult book.
    Great one!
    Report
  • MISS Chan
    5.0 out of 5 stars Good introductory read to the increasingly popular field of data science.
    Reviewed in the United Kingdom on June 29, 2017
    Verified Purchase
    I am not at all familiar with data science - and so I took this book as an introductory read to what is ostensibly a rising field. It's written in an accessible format for beginners - clear explanations, and a range of easily understood examples that allow you to apply each algorithm and 'test' your understanding. I like too that it was easy to finish - I have no problem with math, but books of this sort tend to be dry and I had no issue here.
    Report
  • Don Vaillancourt
    5.0 out of 5 stars This book was the perfect read to get me into data sciences
    Reviewed in Canada on July 27, 2017
    Verified Purchase
    This book was the perfect read to get me into data sciences, which we all know is the root to ML, AI and DL. It gets to the point fast with a lot of information, yet not too much to kill the mood. If you're someone who dislikes long-winded reads, this book is for you. It was the introductory book I needed to get my foot into the ML space. It was a good step into being able to make sense of all those other algorithms on ML. The content contains a lot of key words; they don't dumb it down too much. So I'm able to continue my research once I'm done with this great reference book.
    Report

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