Numsense! Data Science for the Layman: No Math Added

Numsense! Data Science for the Layman: No Math Added

(53 customer reviews)

Original price was: $28.99.Current price is: $5.49.

Product details :
    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

    1. 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.

    2. 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.

    3. Ranjan B. Kini (verified owner) –

      Nicely done!

    4. 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.

    5. Naga Kamisetti (verified owner) –

      I liked the book. very clear explanations especially the support vector kernal explanations.

    6. 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.

    7. eilsel (verified owner) –

      good but a bit too much

    8. 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.

    9. 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!

    10. 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.

    11. North Sea (verified owner) –

      Able to get a top quality background within a short period of time

    12. 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.

    13. 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.

    14. 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.

    15. 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.

    16. sathishkumar vp (verified owner) –

      Not sure why the logistic regression not covered. I reAlly enjoyed while reading this book. Thank you. . . .

    17. CHONG Heung Lam (verified owner) –

      The book title exactly describes its content. And no math involved.

    18. 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.

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