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

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!

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

I feel bad as I had to return the book.I would rate it 5 stars if I did not know anything about the topic and wanted some general exposure so I would not sound stupid in a conversation. The tone is conversational and the authors convey very well what each methods does and does not do. Kudos to them for making it easy for the layman.However, the book lacks details if you actually want to implement one of the method. In practice you would have to do (a lot) more research to actually get something going and this is where I was disappointed. The Kindle preview does not do justice to the actual content but this is common.I think I will look for more in-depth books from those authors because they have a knack to convey difficult concepts in simple fashion.

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.

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.

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.

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.

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.

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.

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.

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.

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.

This is a great book. It is hard to explain Data Science without the maths but this book does an amazing job. It balances both the simplicity and the depth. The pictures are also clear with a concise glossary covering. The book covers all the major algorithms