Free PDF Numsense! Data Science for the Layman: No Math Added
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Numsense! Data Science for the Layman: No Math Added
Free PDF Numsense! Data Science for the Layman: No Math Added
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Used as course material in top universities like Stanford and Cambridge.
Sold in over 85 countries and 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, as well as lots of visuals, all of which are colorblind-friendly.
Popular concepts covered include:
Features:
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.
Product details
Paperback: 145 pages
Publisher: Annalyn Ng & Kenneth Soo; 1 edition (March 24, 2017)
Language: English
ISBN-10: 9811110689
ISBN-13: 978-9811110689
Product Dimensions:
6 x 0.4 x 9 inches
Shipping Weight: 9.9 ounces (View shipping rates and policies)
Average Customer Review:
4.7 out of 5 stars
82 customer reviews
Amazon Best Sellers Rank:
#123,197 in Books (See Top 100 in Books)
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.
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
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!
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.
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.
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.
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.
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.)
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