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Data Science For Dummies (For Dummies (Computer/Tech))

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Your ticket to breaking into the field of data science! Jobs in data science are projected to outpace the number of people with data science skills—making those with the knowledge to fill a data science position a hot commodity in the coming years. Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of an orga Your ticket to breaking into the field of data science! Jobs in data science are projected to outpace the number of people with data science skills—making those with the knowledge to fill a data science position a hot commodity in the coming years. Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of an organization's massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you'll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization. Provides a background in data science fundamentals and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark It's a big, big data world out there—let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.


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Your ticket to breaking into the field of data science! Jobs in data science are projected to outpace the number of people with data science skills—making those with the knowledge to fill a data science position a hot commodity in the coming years. Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of an orga Your ticket to breaking into the field of data science! Jobs in data science are projected to outpace the number of people with data science skills—making those with the knowledge to fill a data science position a hot commodity in the coming years. Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of an organization's massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you'll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization. Provides a background in data science fundamentals and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark It's a big, big data world out there—let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

30 review for Data Science For Dummies (For Dummies (Computer/Tech))

  1. 4 out of 5

    Daniella Araujo

    I turned to this book with hopes to get a light reading on introductory data science, but what I got was a poorly styled, boring and repetitive text with overly technical terms without defining them. Such passages are interspersed with introductions which I assume should cater to the non-techie reader, such as this one: "People care about things that matter to them and that affect their lives. Generally, people want to feel happy and safe. They want to have fulfilling relationships. They want to I turned to this book with hopes to get a light reading on introductory data science, but what I got was a poorly styled, boring and repetitive text with overly technical terms without defining them. Such passages are interspersed with introductions which I assume should cater to the non-techie reader, such as this one: "People care about things that matter to them and that affect their lives. Generally, people want to feel happy and safe. They want to have fulfilling relationships. They want to have good status among their peers." Really?!? What on earth does a book on data science needs to go over about how people want to feel happy and safe? It seems the author wanted this title to be too much for too many people, and it ends up being nothing to anyone.

  2. 4 out of 5

    Anthony.Uw.Ligmail.Com

    Just skimmed through it quickly. Not a very in-depth overview

  3. 5 out of 5

    Johan

    I am done with this book, but I haven't finished it. I usually like the "For Dummies"-books. You can read about a topic without prior knowledge, without having studied the topic or without experiences. The authors of those books assume no prior knowledge You quickly go to a level where you can talk with others about it. If you like the subject, you can go and read some more advanced books or enroll in an (online) class. If not, then at least you know what it is about. I recently started reading tw I am done with this book, but I haven't finished it. I usually like the "For Dummies"-books. You can read about a topic without prior knowledge, without having studied the topic or without experiences. The authors of those books assume no prior knowledge You quickly go to a level where you can talk with others about it. If you like the subject, you can go and read some more advanced books or enroll in an (online) class. If not, then at least you know what it is about. I recently started reading two books "Big Data for Dummies" and "Data Science for Dummies" simultaneously. I had to stop both of them because they expect a lot of prior knowledge: math, programming, machine learning, parallel computing, math, statistics (advanced level), ... It was useless trying to continue. I stopped, maybe one day I will pick them up again, but only after I have brushed up my math, statistics and programming skills and maybe have read some entry-level or dummy) books about machine learning, ... Both books may contain a lot of information, but they are no dummy-books. Unless, for the data science book, you consider someone with a bachelor's, master's or PhD in computer sciences or math, but without knowledge of data science a dummy. In case of the big data book, there is just too much jargon.

  4. 4 out of 5

    Angie Boyter

    See my Amazon review titled, "I guess I'm not even up to Dummy level". http://www.amazon.com/review/R2WOGI8H... See my Amazon review titled, "I guess I'm not even up to Dummy level". http://www.amazon.com/review/R2WOGI8H...

  5. 4 out of 5

    Oak

    Data Science for Dummies by Lillian Pierson is a 364-page educational book that introduces the reader to data science basics while delving into topics such as big data and its infrastructure, data visualization, and real-world applications of data science. It is a well-formatted book, and Pierson’s use of charts, graphs, and pictures helps the reader further understand the material. One of my favorite sections of the book was Chapter 9, “Following the Principles of Data Visualization and Design.” Data Science for Dummies by Lillian Pierson is a 364-page educational book that introduces the reader to data science basics while delving into topics such as big data and its infrastructure, data visualization, and real-world applications of data science. It is a well-formatted book, and Pierson’s use of charts, graphs, and pictures helps the reader further understand the material. One of my favorite sections of the book was Chapter 9, “Following the Principles of Data Visualization and Design.” In this chapter, Pierson talks about creating basic types of data visualizations, tailoring them to your audience, and crafting powerful visual messages using the right data graphics. I especially liked the part when she talked about incorporating design artistry into your data visualization that invokes an emotional response in your target audience. Throughout the book, readers can see how helpful and practical data science can be when applied to real-life situations. The book certainly covers a wide variety of topics; Chapter 4, for example, talks about machine learning, while Chapter 5 discusses math, probability, and statistical modeling. And you can go from learning about making maps from spatial data in Chapter 13, to learning about using Python for data science in Chapter 14. The variety of subject matters covered in the book makes the text fun and interesting to read. I believe that an introductory guide to a subject should follow two almost paradoxical rules: it should be easy enough for a beginner to understand, but also contain enough rich and engaging information for the beginner to truly learn something about the subject and set a good foundation for additonal studies on that subject. Fortunately, Data Science for Dummies follows both of those rules; the content is easy for a beginner to jump into, but the book is also thorough enough to really educate the reader about data science. *I received this book for review*

  6. 4 out of 5

    Song

    The book is only a general introduction about the diverse topics in Data Engineering and Data Science. It's good for the novice to have the quick glance about the domains in this area. The pro part of the book is not just focusing on programming or statistics, but also giving very good introductions about the database, Microsoft Excel, visual design, data storytelling and the various sources to find Open Data. The con part is people need find other books to study further for any given topics in The book is only a general introduction about the diverse topics in Data Engineering and Data Science. It's good for the novice to have the quick glance about the domains in this area. The pro part of the book is not just focusing on programming or statistics, but also giving very good introductions about the database, Microsoft Excel, visual design, data storytelling and the various sources to find Open Data. The con part is people need find other books to study further for any given topics in the book. This is a good book to enable the layman to quickly understand the buzz word Data Science. What's the real meaning of it and what's included in the area. Of course, it requires more efforts to make deep dive in any of these topics. Afterall this is the purpose of "dummies" series: give the reader a good starter.

  7. 4 out of 5

    Thomas

    It was a valiant attempt to define and educate the audience on the book on what “data science” is. I think it accomplished that, but otherwise the book got lost in the firehose of presenting applications, tools, and methodologies. It was too shallow to be useful and too deep to be absorbed for me. It’s a good place to start if you want to further explore what to actually read about or experiment with in data science.

  8. 4 out of 5

    Jiannina

    If you are interested to know what is data science about, this is a good book to read. I really like how the author uses very simple words and real life examples to explain complex concepts. She also gives you some resources that will help you acquire the skills you need if you want to become a data scientist.

  9. 4 out of 5

    Khalid Alawar

    Amazing book that provides a holistic view of Data Science, not too technical, provides exactly what it promises!

  10. 5 out of 5

    Cathy Craig

    This is super technical for my sticks and stones brain. I took away a general overview of how big data is generated and what people do with it and that was basically what I looking for.

  11. 4 out of 5

    Sam Isse

    A great Book for introduction to data Science, it explains jargon of this field briefly

  12. 5 out of 5

    Eric

    Very good primer of what data science is and the tools available at a data analytic professional's disposal.

  13. 5 out of 5

    Ty Roper

    Clear, simple explanation of Data Science. Many references to public data sources and publicly available tools.

  14. 5 out of 5

    John

  15. 5 out of 5

    Roy P Douglass

  16. 4 out of 5

    Karlissa Kawecki

  17. 4 out of 5

    Abigail Burton

  18. 5 out of 5

    Anov Mailoa

  19. 5 out of 5

    John Leven

  20. 5 out of 5

    Martin

  21. 4 out of 5

    Eduardas Shishmanian

  22. 4 out of 5

    Eric Xu

  23. 5 out of 5

    Syafiq Firdaus

  24. 4 out of 5

    Nadine Morrisroe

  25. 4 out of 5

    Asish T Karunakaran

  26. 5 out of 5

    Megan

  27. 4 out of 5

    Don Kasper

  28. 5 out of 5

    Daniel Noventa

  29. 5 out of 5

    Shawkath Khan

  30. 5 out of 5

    Brandon Wilde

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