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 laymanβs 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.
Xuan Hao –
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!
Adele Huang –
I know nuts about data science but this book is surprisingly easy to understand! I particularly love the examples that accompany each chapter. The examples relate to everyday life and effectively narrate the concepts in each chapter. I would recommend this to anyone who wants to have a quick but yet in depth overview of what Data Science is about.
NC Boy –
Fantastic primer on data analytics. Highly recommend to anyone interested in learning more about this fascinating subject area. Introduces the major methods in simple terms.
Yeo Wee Teck –
Data Science / Analytics is a rather complex subject matter. It is tough to describe to a layperson what it entails. Annalyn and Kenneth did a good job in “translating” the difficult subject matter to bring across the key essence of each part of the entire data science / analytics work flow. A very good piece of work. A must share, especially with my staff and bosses.
Gaurav Madan –
Finished the book from cover to cover in 3 days (just few hrs a day)
The book is a gentle introduction to data science algorithms. Builds a good foundation of what are various types of DS problems and what techniques can be employed to solve them.
This is however a small book, less than 150 pages. You will have to buy a detailed book to continue the subject further.
It is also a good read for Product Managers who just need to know whats possible with data science. They can subsequently engage relevant teams to get things done.
Shinealious –
Have been struggling to find a suitable book on data science I can understand – all the others are so technical! This one hits just the right spot. Everything is explained for a someone with no math background but wants to apply data science to my business. Highly recommended for beginners.
Vamsi Nellutla –
Love this book! Amazingly written and very easy to digest even the most complex and confusing algorithms. Good job!!
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.
M_M –
Excellent book for data science introduction, it was a good recap for me on most of the models used in machine learning.
Shajey Rumi –
The author has kept the book simple deliberately. I am about halfway through and so far it is well worth the time and money.
I would have loved and rationale of why we use one or the other algorithm…but may be that is coming in next chapters. (
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.
Christopher Dunn –
This is a tremendous resource for anyone looking for a refresher or a basic introduction to machine learning. The examples were easy to understand and the lessons are heavy on theory.
Akin –
All of these terms I’ve heard floating around my head have finally concretely been defined in easily digestible format. Buy this book and start your journey on the right foot into the world of data science!
J. Patterson –
Have been trying to learn machine learning, read a few different books but most of them covered alot of equations that I find hard to understand. Numsense made me comprehend many of the underlying logic and intuition behind the algorithms. Each chapter describes an important algorithm and is supported by fully coloured diagrams and an interesting example that keeps me going for more. This is a great book that is worth its price.
Oscar Acevedo –
Es un libro corto que puedes leer en menos de una semana, es bueno para introducirte en la ciencia de datos, no vas a poder implementar los algoritmos despues de leerlo pero si vas a tener la idea general de que hace cada algoritmo, aparte por lo que cuesta es muy buen bang for your buck
MISS Chan –
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.
Kenneth J Cottrell –
Excellent intro to ML. Goes into enough plain English description of relevant techniques to help you understand the mathematics which you read about in more technical books.
dgd –
I knew very little about Data Science. Now I know a lot more. I need to check the appendix for DS exercises to explore more. Four π because it ended too soon.
Michael O’Flaherty –
I took the Coursera John Hopkins Data Science certification a few years ago. This book would have been great intro before starting that trek. I enjoyed the authors’ simplicity and brevity. Highly recommended for dipping your toe into the data science data lake (or whatever moniker is being used today.)
Sarah Sarah –
As other reviewers mentioned, it’s great entry level introduction to Data Science and Machine Learning. More importantly, it’s a great resource for those of us who are buried deep in the technical side of Data Science, but need to surface from time to time and explain what we are doing and how we go about it to our business partners. I will definitely steal language and examples from this book for my business presentations
Tathagata Dasgupta –
As a leader, student, and educator of Data Science, I think 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. As Social and media research takes on a new dimension with huge sources of structured and unstructured data, Numsense shows numerous examples of how to make sense of data and make decisions based on how they inform us. Several algorithms have been described in English, and some fundamentals have been discussed for beginners. Data Scientists may already know many of these, but middle managers and business leaders will find this educational – they can plan on leveraging data using many of these cool and evolving methodologies. This type of book creates an opportunity for the business community to speak in a common vocabulary as industries transform from gut-feel to scientific, structured analysis based decision making.
Don Vaillancourt –
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.
DF –
The beauty of Numsense lies in that it stands out from other Data Science text manuals in bringing to life a unique and well constructed portrait of a complex subject matter without recourse to the technical mathematical or coding intricacies. Annalyn’s and Kenneth’s work achieves that with grace and proves as entertaining as a story well told at the same time. Should you be foreign to DS, should you require to understand the work of data scientists in your work place or probably should you feel the need to put neat order to your ideas and knowledge, look no further and wait no longer to open this gentle but robust door. It will help you to access on firm ground a whole fascinating world of limitless possibilities. It is up to you to stay out or step in and go for it, but the world is changing fast. Learn to use these tools, make your contribution. Today may be already late.
SRINIVASAN VARADHARAJAN –
An excellent book. It meets the expectations it sets out to.
A very crisp and clear introduction to the concepts with indicative scenarios where they can be applied.
I hope there is a next book with a bit more deep dive and some mathematics too, for the readers who have finished this book.
BookWyrm –
So you need to analyze a lot of data… and you aren’t certain what your analysis options are, or which is better for your case. This is a great resource to help you determine where to put your effort… or… to evaluate a proposed analysis effort. If nothing else, the strengths and weaknesses summaries for each method will give you intelligent observations to make and questions to raise.
But do not expect to learn how to perform any of these methods from this text. The devil of the detail must come from somewhere else. However, you will have a good idea of what to look for.
Sriram Ramanathan –
This is great as last minute interview prep or to explain the subject to a colleague. Good comparison of different techniques.
Chuin-Shan Chen –
I like the book and highly recommend Numsense! by Ng and Soo for any knowledgeable individuals 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. Despite no math added, Numsense! strikes a balance between breadth and depth of data science and gives no nonsense introduction to the field. Ng and Soo used real world problems to motivate the use of unsupervised, supervised and reinforcement learning algorithms. They also portrayed the contents lively and beautifully without abused jargons. Two thumbs up!
Ranjan B. Kini –
Nicely done!
Mithun –
No math, no jargons, easy examples to make you grasp the concept. You would appreciate reading it before diving deeper into technical and mathematical details.
Scott H –
This is a very informative book. I have been studying statistics and machine learning for a couple years, so I was already familiar with some of the topics covered in the book, such as decision trees, random forests, and neural networks. However the problem with studying things on your own, as I have done, is that you can frequently miss whole fields of topics. That is why this book was useful to me. I had previously studied a few topics deeply (depth first learning), and this book was a good opportunity to study a number of topics more quickly (breadth first learning)
This book gives an overview of how machine learning works, and then tells how 11 different machine learning algorithms work. For me, I was very familiar with 4 of the algorithms, had a passing understanding of 4 more of them, and little to no knowledge of 3 of them before reading the book. After reading the book, I got some useful analogies and real life use cases for the 4 topics that I already knew well. I deepened my understanding and spotted some errors in what I thought I knew about the 4 topics I had seen before, and I kicked the other 3 up from a level where I had no idea what they where to one where I would be comfortable using them in a machine learning toolkit such as Python’s Sklearn, or could as intelligent questions if I’m in a meeting where someone is discussing them.
AA –
It’s a very good read. Insightful. It’s suitable for readers without data analysis background.
Otaner O –
Easy read for people without data analysis background.
Vladimir Panjkovic –
I like this book for two reasons:
1) This is the best introduction to data science / machine learning for absolute beginners I have found so far. Succinct, clear, unburdened with gory details. Great stepping stone.
2) Nowadays I often come across books with an amazing number of 5-star reviews, only to find out after purchase that they are atrocious (poorly written, horrible grammar, spelling mistakes galore, and/or worse). It is quite refreshing to find a book like this where every five-star rating by readers is genuine and well deserved.
Bharani Mamidoju –
This gave me a clear picture of what algorithms to be used and when. Best book and I would suggest it to all those who want to learn machine learning!!
Irvin TMD –
As its title suggests, the book offers a light-hearted introduction to the world of Data Science. It is by no means exhaustive, but it covers an essential spread of topics that are foundational to most Data Science problems. Focusing on a highly conceptual delivery, readers will be able to understand the concepts with ease, even for the non-technical layman without a relevant background. Certainly, this book gives you a good starting foot toward your plunge into Data Science.
Alan Kong –
Great intro to data science with all the major statistical topic explained in a way that newcomers can understand. A must read for anyone just getting started!
Tomer Ben David –
All I can say is this is how technical books should be written. I can only learn from how clear the authors are.
Cheung Wai Lok –
This is a very short and concise introduction to the very basic concept. Perfect for those who want to learn ML but have no idea of what it is.
Y –
Useful intro that touches on advanced concepts in a very clear and concise way. I’d love to see a sequel focusing on machine learning, model building and iteration and going to the next level of detail on that.
Massimiliano Masi –
This is not an easy book to read, but itβs written in the most effective way to explore in a few Hours the core topics of data science. Great book.
Scott D –
Very clear, straightforward explanations make this book easy to follow for the beginner. Would recommend for anyone wanting to get an overview of machine learning without being buried by overly-technical explanations. Great value!
RangerJ –
Clear and easy to read. Outlines the data science techniques without resorting to the maths that might put people off. I would certainly recommend it for anyone who keeps being told that data science and data scientists are key to you companies digital future. Read this it will give you an idea of what they actually do and therefore where they fit into the picture.
Radu Balaban –
Itβs a great quick introduction to the most common ML & Data Science methods and the intuitions behind them. While not very detailed, it does a good job for what it sets out to do.
CrystalLA –
This is a very good introductory level machine learning book, especially for those without very strong math background. It tells the algorithms in a very clear and simple way. I will recommend this book for machine learning beginners.
Sonia –
Excellent source for students who studied data mining and is looking at either a refresher or how to bring everything together.
Ismael –
Easy to read. It gives a good grasp of different machine learning algorithms. It explains complex techniques in a very comprehensive way.
Clever Girl –
I am in marketing and not a math person and even I find this book an fairly easy read.
I may update this review but today Facebook is all over the news for exactly what is mentioned in this book. Here is an excerpt from the book:
Personality traits are also a good way to group customersβas done in the following survey of Facebook users. Users were invited to complete a questionnaire in order to check how they scored on four personality traits: extraversion (how much they enjoyed social interactions), conscientiousness (how hardworking they were), emotionality (how often they got stressed) and openness (how receptive they were to novelty). Initial analysis revealed positive associations among these personality traits. Highly conscientious people were likely to be more extraverted, and, to a smaller extent, highly emotional people tended to be more open.
Hence, to better visualize these personality traits, we paired themβconscientiousness with extraversion, and emotionality with opennessβand aggregated scores within each pair”.
Jeremy Leipzig –
I can see why this is so popular. This book is mostly machine learning – no data engineering, munging, code (or math) here.
Marc G. –
Provides an excellent and easy to read overview on data science concepts. Iβd recommend this book to anybody looking for an introduction to data science.
Sachin S. Phadnis –
The book gives a perfect understand of the various techniques and algorithms used in data science. It provides a great high level view without going into the math.
The book is a great resource for people who are beginners to data science and trying to learn on their own, not too interested in the math behind the works, leaders or managers looking for quick understand.
Samson S Liu –
As the title accurately indicates this book gives a very informative overview where I felt Iβve learned something very meaningful at the laymanβs level. And an excellent starting point for someone who would be interested in pursuing the topic on a more technical basis.
Elisha Olivas –
So easy (and fun) to read!
Edward Kahana –
This was a great introduction into data science and artificial intelligence principles and how you can apply them to optimize different outcomes. Fantastic reference material.
Sandhya Kodamarti –
Authors had tried to present the concepts of ML in a concise manner with examples and they have succeeded. Would recommend this for all Data Science beginners.
TurboChicken –
The data science profession is being sought after over other data professions. This was a great read to understand the data science job role in relation to other data jobs such as BI.
Kayla Siddell –
This is a great resource for learning data science
Natarajan Lalgudi –
Numsense is a fabulous read. For any current or aspiring students of Data Science, it makes the process of discovering new algorithms and what they do a lot of fun. The authors have done this by relating each technique with popular use cases in daily life. And thereby demystified the whole process of understanding how the algorithm works in application. Great effort by Annalyn and Kenneth. Kudos
Michael D. Pechner –
I am a software engineer, but not a mathematician or a statistician. I am a devops engineer that works with data scientists. Understanding their work a little better makes servicing their needs easier. I did this backwards. Purchased the book, read it, then asked one of the data scientists to look at the book. I did not waste my time. Well written, covers the topic well. I did have to reread sections and study the images carefully to understand the topic being covered.
Steve h. –
Reading this as an accompaniment to a deep dive In to the study of Data Science has proven to be a great choice. Reading book has made it so much easier to follow along with the sometimes confusing explanations by other authors in this field. Excellent book!
A curious reader –
Gives you a good sense of what different data science methods are, how they work, and why people use them. Presentation is qualitative and pictorial, driven by examples, rather than deductive or overly mathematical.
RC –
A good book to learn the high level knowledge of some machine learning algorithms, without needing to worry about the underlying math.
Alfredo Monasi –
Excelente libro introductorio al mundo de data science con la descripciΓ³n de los principales algoritmos.
Sudhakar –
I just finished this book. I am trying to get into machine learning but was always boggled by the terminology like overfit, underfit, supervised vs unsupervised models, accuracy, tuning etc. This book gives perfect context and gives a concise summary of world of machine learning. It is perfect to get a sense of what this is all about, how models work, limitations of each model. Each algorithm is explained by a real world use case which a person can relate to.
I guess even practicing machine learning professionals should buy this book to deepen their understanding.
IAmExtra8 –
This book provided a great review for me as I headed into a Data Science role after years of working as a Software Engineer. I didnβt need a review of the actual math behind each model type, so it was perfect finding a book that would remind me of the methods and use cases without requiring hundreds of pages of reviewing the statistical underpinnings.
K. Petersen –
I am teaching myself python and wanted to know more about the packages within the numpy package. This was a very good resource for me, but, I have taught descriptive and inferential statistics for a while. I would recommend this book to anyone learning python and data science techniques.
Avi T. –
Numsense is an excellent book that I categorically recommend to everyone. It explains the fundamental concepts of data science in a way that is concise, fun, and well explained. As this field increasingly influences our ability to make better predictions and ripples across more and more areas of our economy, these are concepts that are important for all laypeople to understand.
Helene Davis –
For someone with a math or science background itβs a good concept review. Also gives non-mathematicians a great introduction to data science.
Tanzeel Haider –
Appreciate the effort taken by the authors to write such a book. I can imagine the tough ask of making Data Science easy to understand to newbies. The author has done a commendable job to initiate the curiosity to the readers.