Data Science by John Kelleher

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Data Science by John Kelleher Description

Data Science by John Kelleher: Your Essential Guide to Data Analysis

Unlock the world of data science with Data Science by John Kelleher. This illustrative paperback edition from The MIT Press provides a comprehensive introduction to the fundamental concepts and techniques in data analysis. Whether you’re a student or a professional in the field, this book serves as an insightful resource to enhance your understanding of modern data science practices.

Key Features and Benefits of Data Science by John Kelleher

  • Comprehensive Coverage: The 280-page book offers in-depth knowledge about various data science methodologies, including statistics, machine learning, and data visualization. Readers can effectively apply concepts in real-world scenarios.
  • Illustrated Edition: The inclusion of illustrations enhances understanding, making complex topics more accessible to readers at all levels.
  • ISBN Information: The book is identified with ISBN-10: 0262535432 and ISBN-13: 978-0262535434, ensuring it’s easy to find and reference.
  • Target Audience: Specifically designed for grades 10-12, this book is suitable for high school students, aspiring data scientists, and educators seeking a foundational text in data science.
  • Compact Size: At dimensions of 5 x 0.72 x 7 inches and weighing just 2.31 pounds, this paperback is portable and convenient for both classroom and personal use.

Price Comparison Across Different Suppliers

The Data Science by John Kelleher is competitively priced across various leading bookstores. Prices may vary depending on promotions and regional availability. It’s essential to check multiple suppliers to ensure you get the best deal. With continuous market fluctuations, now is the perfect time to compare prices!

Notable Trends from the Price History

Analyzing the 6-month price history chart for Data Science by John Kelleher, a steady trend shows a gradual decrease in price during the summer months, potentially due to sales and educational discounts. This trend indicates an opportunity for buyers to consider purchasing during these times for optimal savings.

Customer Reviews: Insights and Feedback

Customer reviews for Data Science by John Kelleher reveal a largely positive reception. Many readers praise the book’s clarity, organization, and practical approach to teaching data science fundamentals. Here are some highlighted points from user feedback:

  • Positive Aspects: Readers appreciate the well-structured chapters and the balance between theoretical concepts and practical applications. The illustrations significantly contribute to the learning experience, offering visual context for complex topics.
  • Noted Drawbacks: Some users mentioned a desire for more advanced topics to be covered, particularly for those already familiar with basic data science principles.

Explore Unboxing and Review Videos

To see Data Science by John Kelleher in action, check out related YouTube review and unboxing videos. These resources provide visual insights and further context about the book’s content and layout, helping potential buyers make informed decisions.

Why Choose Data Science by John Kelleher?

This book stands out in a crowded field of data science literature. Its straightforward language and illustrative approach make complex topics palatable for younger readers or beginners. Furthermore, the book’s emphasis on real-world applications ensures that readers are not just learning theory; they are preparing for practical use in future data-driven careers.

Whether you’re pursuing a career in data analysis, machine learning, or statistics, Data Science by John Kelleher equips you with the tools necessary to succeed. With its engaging content, it serves as a fantastic entry point for high school students while providing essential knowledge for educators and entry-level professionals.

Don’t hesitate! Compare prices now and elevate your data science knowledge by purchasing Data Science by John Kelleher today!

Data Science by John Kelleher Specification

Specification: Data Science by John Kelleher

Publisher

The MIT Press, Illustrated edition (April 13, 2018)

Language

English

Paperback

280 pages

ISBN-10

0262535432

ISBN-13

978-0262535434

Grade level

10 – 12

Item Weight

2.31 pounds

Dimensions

5 x 0.72 x 7 inches

Paperback (pages)

280

Item Weight (pounds)

2.31

Data Science by John Kelleher Reviews (13)

13 reviews for Data Science by John Kelleher

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  1. Dr. Nabeel Murshed

    The book has 7 chapters, five of which provide very basic introduction to Data Science. It discusses the definition of data science, data types, databases, machine learning, and data science tasks. The book has lots of texts and very few illustrative examples. It does not describe data processing, data presentation, analysis, and interpretation. In summary, the book is very, very basic and should not be titled as Data Science.

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  2. MHD YASSER AL LAHHAM

    As a developing science, this book provides an essential introduction to Data Science, in very simple way, not much of technical or theoretical, but scientifically precise, everyone has to read it

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  3. leonardo pacifico

    Os autores se propuseram a apresentar e discutir os fundamentos da ciência de dados.
    Para tanto, ao longo do texto aprsentam as principais definições da área, além de discutirem a questão da privacidade dos dados e ética na aquisição/uso dos dados.

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  4. Michael George

    Data science is put in excellent perspective in this book. I think the book is especially oriented toward giving people interested in “specializing” in this field or utilizing data science some good, basic information. As a multidisciplinary field, and one oriented toward business, government and surveillance interests, generally, it is a field that encompasses and extends into practical areas that its associated traditional area, namely statistics, has not in the past much-addressed. Data science is an extremely interesting, technical field with broad social and ethical implications explored in this book. Statistics is only one tool. The authors lucidly discuss the focus on the huge amounts of valuable, unstructured data. They point out that to make all of this useful for the goals and purposes of business, surveillance, medicine, government, etc. requires an enormous time investment in putting appropriate data together and extracting information in a usable form. The discussion of mathematical modeling, machine learning, and the overall use of algorithms is very insightful. The authors make it clear that data science is not merely “deep learning”, despite the fact that the extraordinary advances in using neural nets represented by deep learning is largely responsible for much of the importance of data science today. There are excellent perspectives of data science available on the Internet, but I think the authors of this book have provided a good supplement for this information in a deeper way. One of the real problems in picking information out from the Internet is escaping the “hype” surrounding a subject that is currently “hot” like data science. This book definitely allows the interested person to separate some of the solid pieces of knowledge about what the field involves from the huge amount of “noise” surrounding the entire area of “weak” AI and machine learning. I would recommend this book strongly to anyone seriously considering going into this field. A point the authors stress is that weak AI, namely specialized applications, rather than broadly “intelligent” systems competitive with general human intelligence, has opened up a world of opportunity, promise, progress, as well as ethical dilemmas. I personally think that data science is a great field for an enormous spectrum of technicians at all educational levels. The book opens a window a bit on the enormous implications for our future. It is a good start on the climb to a satisfactory knowledge of this field and its potential. I especially recommend the book to business executives and entrepreneurs as a useful and insightful view, for developing a strategic picture of this field, that does not get into unnecessarily technical details, and is not subject to the “hype” and “noise” from the Internet.

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  5. Brandon Fosdick

    For a basic introduction this book does fairly well, but it’s seriously lacking in details or specifics. If you’re completely new to data science then this might be for you. If you already have even the most basic understanding of data science then you can easily skip this book.

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  6. e.k.

    The last chapter was really good. wish authors provided more insights into successful data science projects. The rest of the book was very generic information.

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  7. Eric

    The authors do an excellent job of giving a very high level overview of the following for Data Science:
    -History
    -Applications (Prediction, clustering, anomaly detection)
    -Tools of Data Science (Bayes Rule, Logistic Regression, Neural Networks, Decision Trees)
    -Ethical concerns (Where do we cross the line between privacy, security and applications of the Data Science?)
    -Growth of Data Science (I wish the authors would’ve shared how to get into the career field more. Since applying association rule here, anyone that reads the book is likely to be interested in Data Science).

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  8. Shuaib

    I found this book to be an excellent way of familiarising myself with Data Science, coming from a non-Computer Science (Economics) background.

    It covers the recent literature on such computational methods from, the current applications and the challenges behind Data Science. The book also talks about the various types of data along with the use cases like nominal/ordinal (categorical) and numeric data. Eventually, getting to what I think is the best chapter in the book is ‘Machine Learning 101’, which easily explains the types of what’s the difference between supervised learning (classification/regression problems) and unsupervised learning (clustering, segmentation etc.). Only Maths (Algebra/statistics) up to high school/college level is needed to understand the principles of how most of the algorithms are set-up.
    The only thing I think this book was disappointing at was the explanation of Deep Learning, which I feel was slightly brushed over compared to Machine Learning, when in some way, Deep Learning may have deserved its own chapter.
    Finally, the book ended on the legislation side of Data Ethics, such as GDPR and the trade-off between accurate analysis and privacy among users of the internet/digital applications, again illustrating the future path for Data Science.

    I would recommend this book as a handy Data Science reference.

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  9. C. Bennett

    Good introductory book for data science. Use it for a lot of my college courses for the last couple years.

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  10. Cai

    Well-written and easy-to-understand, this book gives a new-comer like me a conceptual framework to think about problems in data science. It helps me to understand what the field really is and what the workflow of a data science project looks like. Particularly interesting is the chapter on data ethics and regulation. I think it is an area that is often overlooked by technical textbook, but should really be emphasized to readers who might someday become a data practitioner. Overall, it’s a very good book and worths your effort to delve into.

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  11. Sulaiman Khan

    CRISP- DM, Supervised Learning, Data Modelling, Linear Regression etc.., well versed concepts ~

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  12. Oscar Matías

    Buen libro. Directo al tema desde los primeros capítulos. Bien explicados. Intro que te sirve para entender más el tema y entrar a detalle luego de conocer el panorama completo

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  13. Yogesh Kumar

    This book covers core concepts in data science in an easy to read manner. Infrastructure for handling big data and the data science ecosystem are introduced along with Machine Learning basics and some useful concepts at a high level(like CRISP-DM, clustering, anomaly detection etc.). A chapter on the privacy and ethics covers GDPR and biases in algorithms. Overall, a good general introduction.

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