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Numsense! Data Science for the Layman: No Math Added

Numsense! Data Science for the Layman: No Math Added

Original price was: $28.99.Current price is: $9.99.

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Numsense! Data Science for the Layman: No Math Added

In today’s data-driven world, it’s easy to feel overwhelmed by the sheer amount of information available. With the rise of big data and analytics, it’s becoming increasingly important for individuals to have a basic understanding of data science concepts. However, for many, the idea of delving into data science can be daunting, particularly when it comes to the complex mathematical equations that often accompany it.

This is where “Numsense! Data Science for the Layman: No Math Added” comes in – a revolutionary approach to learning data science that eliminates the need for advanced mathematical knowledge. This innovative approach focuses on providing a comprehensive understanding of data science principles, using simple, easy-to-understand language and relatable examples.

The Problem with Traditional Data Science Education

Traditional data science education often focuses on the technical aspects of data analysis, assuming a strong foundation in mathematics and statistics. While this approach may be suitable for those with a background in these fields, it can be alienating for individuals without a mathematical inclination. The result is a significant barrier to entry for those who want to learn about data science but are intimidated by the math.

A New Approach to Learning Data Science

“Numsense! Data Science for the Layman: No Math Added” takes a different approach. By stripping away the complex mathematical jargon and focusing on the practical applications of data science, this approach makes it possible for anyone to learn about data science, regardless of their mathematical background.

Through the use of real-world examples, analogies, and simple explanations, “Numsense!” provides a comprehensive understanding of data science concepts, including data visualization, machine learning, and statistical analysis. This approach enables learners to develop a deep understanding of data science principles, without getting bogged down in complex mathematical equations.

Key Benefits of the “Numsense!” Approach

The “Numsense!” approach offers several benefits, including:

  1. Accessibility: By eliminating the need for advanced mathematical knowledge, “Numsense!” makes data science accessible to a wider range of people.
  2. Practicality: The focus on practical applications and real-world examples enables learners to develop a deeper understanding of data science concepts and how they can be applied in everyday life.
  3. Engagement: The use of simple language and relatable examples makes learning data science fun and engaging, rather than intimidating and overwhelming.

Who Can Benefit from “Numsense!”?

“Numsense! Data Science for the Layman: No Math Added” is perfect for anyone who wants to learn about data science, regardless of their background or experience. This includes:

  1. Business professionals: Looking to gain a deeper understanding of data-driven decision making and how to apply data science concepts in their organization.
  2. Students: Interested in learning about data science, but without a strong mathematical background.
  3. Entrepreneurs: Wanting to leverage data science to drive innovation and growth in their business.
  4. Anyone curious about data science: With a desire to learn about the basics of data science and how it can be applied in everyday life.

Conclusion

In conclusion, “Numsense! Data Science for the Layman: No Math Added” offers a revolutionary approach to learning data science, one that eliminates the need for advanced mathematical knowledge and focuses on practical applications and simple explanations. By making data science accessible to a wider range of people, “Numsense!” has the potential to democratize data science education and enable anyone to develop a deep understanding of data science principles. Whether you’re a business professional, student, entrepreneur, or simply someone curious about data science, “Numsense!” is the perfect resource for you.

Customers say

Customers find this book to be a great introductory resource for data analysis novices, providing a concise summary of machine learning techniques. The book is easy to understand, written at the perfect level of detail, and without any math, making it accessible for laypeople. They appreciate its readability, design, effectiveness, and value for money.

12 reviews for Numsense! Data Science for the Layman: No Math Added

  1. Xuan Hao –

    Great entry level book for Data Science, but also great for people with experience in the subject!
    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!

  2. Fábio de Salles –

    The book to go for basics of/introduction to Data Science – a must have!
    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!

  3. Amazon Customer –

    Excellent breadth as well as depth
    This is an excellent book both in depth and breadth ofthe topics covered. It gives descriptions, analyses, and insightsabout the most popular algorithms on various topics, and it coversmany more areas than most books. The book is well integrated acrossthe broad diversity of topics that are covered, and connections betweenmethods and topics are pointed out throughout the book. For most of theimportant topics, a lot of detail is provided in terms of algorithm descriptionand pseudo-code. In some cases, interesting analyses are also provided.For instance, in the case of frequent pattern mining algorithms,not only are more algorithms discussedthan most of the other books, but a discussion of multiple choicesof data structures for the same algorithm is provided,along with their relative trade-offs. The relationships among variousalgorithms are also discussed. I have seen quite afew textbooks on data mining, and I have not seen anything close to thislevel of detail in any of the other books. Overall, my impression isthat the authors have done an excellent job of calibrating detail levelto topic importance. Therefore, it can serve both as a textbook andas a reference book. On the other hand, this is certainlynot an implementation or programming-centric book. The book is good atteaching principles and concepts.

  4. S. Tzanev –

    The best introductory book for technology managers
    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.

  5. F. Medeiros –

    Overpriced
    Good book but not worth the high price tag.

  6. Roy –

    Excellent Introduction
    Easy to follow. Explores applications and describes strengths and weaknesses. I especially liked the summaries at the end of each chapter.

  7. Anonamouse –

    Good Overview of Data Science
    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.

  8. Client Kindle –

    I highly recommend this book, that was very useful in my case. It describes all the techniques in common word, so that you’ll be able to dig in the good direction.

  9. Mick S –

    Very good and easy to understand the explanation..

  10. carlo occhiena –

    what a terrific book. One of the best I ever read, it really gets you going in a few pages.It cover all the most relevant topics related to DS, including the jargon, the most common algos, the best practice. All is simply written, understandable, actionable.It’s the kind of book I wished I could have read 10 years ago, long time ago before cracking my head open on more difficult book.Great one!

  11. 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.

  12. 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.

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