Numsense! Data Science for the Layman: No Math Added
Original price was: $28.99.$5.49Current price is: $5.49.
PDF 5 MB • Pages: 133
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.
53 reviews for Numsense! Data Science for the Layman: No Math Added
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Xuan Hao (verified owner) –
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 (verified owner) –
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.
Hang (verified owner) –
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 (verified owner) –
Excellent book for data science introduction, it was a good recap for me on most of the models used in machine learning.
Shajey Rumi (verified owner) –
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 (verified owner) –
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 (verified owner) –
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.
Kenneth J Cottrell (verified owner) –
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 (verified owner) –
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 🌟 because it ended too soon.
Michael O’Flaherty (verified owner) –
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 (verified owner) –
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 (verified owner) –
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.
BookWyrm (verified owner) –
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.
Chuin-Shan Chen (verified owner) –
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 (verified owner) –
Nicely done!
Mithun (verified owner) –
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.
Tomer Ben David (verified owner) –
All I can say is this is how technical books should be written. I can only learn from how clear the authors are.
SDuford (verified owner) –
The main tools and techniques of Data Science are explained with clear real-life examples and without resorting to math. A great introduction to Data Science and Machine Learning.
Cheung Wai Lok (verified owner) –
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.
Scott D (verified owner) –
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!
Radu Balaban (verified owner) –
It’s 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.
Naga Kamisetti (verified owner) –
I liked the book. very clear explanations especially the support vector kernal explanations.
Sonia (verified owner) –
Excellent source for students who studied data mining and is looking at either a refresher or how to bring everything together.
Ruben D. Trevino Gonzalez (verified owner) –
Great introduction to predictive analytics, without relying on math, it explains the most used models for business. If anything, it would need some introduction to optimizing, but other than that it’s a great read.
Jeremy Leipzig (verified owner) –
I can see why this is so popular. This book is mostly machine learning – no data engineering, munging, code (or math) here.
eilsel (verified owner) –
good but a bit too much
Sachin S. Phadnis (verified owner) –
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.
Alfie (verified owner) –
If you are looking for a overview and how to use each algorithm in certain scenarios, then grab this book. It is a quick read and the ROI (return on investment) is high.
Fábio de Salles (verified owner) –
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!
Samson S Liu (verified owner) –
As the title accurately indicates this book gives a very informative overview where I felt I’ve learned something very meaningful at the layman’s level. And an excellent starting point for someone who would be interested in pursuing the topic on a more technical basis.
TurboChicken (verified owner) –
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.
Michael D. Pechner (verified owner) –
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.
Alejandro Garciarrubio Granados (verified owner) –
Well written, well designed book. Read it if you don’t even know the names of standard methods in data science.
A curious reader (verified owner) –
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 (verified owner) –
A good book to learn the high level knowledge of some machine learning algorithms, without needing to worry about the underlying math.
North Sea (verified owner) –
Able to get a top quality background within a short period of time
Fábio (verified owner) –
O livro é uma introdução aos assuntos relacionados a Data Science. Achei um bom livro, pelo que se propõe, nele você não verá códigos, não há formulas. Nele encontramos introdução sobre os principais algorítimos, fases necessárias como tratamento dos dados.
Pra quem busca um livro que de uma visão geral sobre o assunto, em aspectos puramente teóricos, este é um bom livro.
Sudhakar (verified owner) –
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 (verified owner) –
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 didn’t 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.
Anonamouse (verified owner) –
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.
K. Petersen (verified owner) –
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. (verified owner) –
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 (verified owner) –
For someone with a math or science background it’s a good concept review. Also gives non-mathematicians a great introduction to data science.
Allan Cheng (verified owner) –
Never seen a book as amazing as this! Concise, easy to read and sum it up on data science!
fahad masood reda (verified owner) –
This book is very easy to read , Perfect match for those who doesn’t want a book with all the math fluff
I would recommend this book to anyone ( beginner) who wants to start his journey in Data Science
DAVID DIAZ ACHA (verified owner) –
Although the whole concept is keep it simple, sometimes it feels too light and that something is missing to connect the dots. Topics such as Neural Networks should be explained a bit deeper or removed at all.
Bhupesh (verified owner) –
This book gives very basic understanding of all data science techniques like supervised and unsupervised. It provides basic details and doesn’t go in detail of code.
sathishkumar vp (verified owner) –
Not sure why the logistic regression not covered. I reAlly enjoyed while reading this book. Thank you. . . .
Marlon (verified owner) –
As someone who did statistics at university purely out of necessity it was a great way to get back into the concepts around data analysis. Leaving the math out was a good way to make it accessible.
S. Tzanev (verified owner) –
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.
CHONG Heung Lam (verified owner) –
The book title exactly describes its content. And no math involved.
Steve Roughton (verified owner) –
For someone that needs to understand how predictive models and machine learning models are generally computing results, this is a great book. A decision maker that needs to understand terms like correlation, A/B testing, overfitting, and random forests will benefit from this book. I especially was enlightened by the section about neural networks.
Roy (verified owner) –
Easy to follow. Explores applications and describes strengths and weaknesses. I especially liked the summaries at the end of each chapter.