Data Science For Dummies (For Dummies

by Lillian Pierson

Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book. Data Science For Dummies demonstrates: Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today.

Genres: Nonfiction, Science, Technology, Business, Reference, Programming, Mathematics

432 pages, Paperback

Published September 15, 2021

About the author

Lillian Pierson is a CEO & data leader that supports data professionals to evolve into world-class leaders & entrepreneurs. To date, she’s helped educate over 1.3 million data professionals on AI and data science.

Lillian has authored 6 data books with Wiley & Sons Publishers as well as 8 data courses with LinkedIn Learning. She’s supported a wide variety of organizations across the globe, from the United Nations and National Geographic, to Ericsson and Saudi Aramco, and everything in between.

She is a licensed Professional Engineer, in good standing. She’s been a technical consultant since 2007 and a data business mentor since 2018. She occasionally volunteers her expertise in global summits and forums on data privacy and ethics.

Reviews

[Daniella Araujo · Rating 1 out of 5]

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.

[Ashok Krishna · Rating 3 out of 5]

To be candid, this ended up being one of those rare books that I stopped reading after a while and started skimming instead. Too much theory and overflowing with words. Occasionally repetitive and too tiresome. You can use it as a generic reference material though.

[Anthony.Uw.Ligmail.Com · Rating 3 out of 5]

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

[Amid عميد]

I don't think I've ever enjoyed a "For Dummies" book. While I sometimes want to read about complex topics in simple language because English is my second language, "For Dummies" books feel too simplistic and not aimed at serious learners.

[Johan · Rating 1 out of 5]

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.

[Ossian Hempel · Rating 3 out of 5]

Does a decent job of what it sets out to do. Introduces the concepts within data science and points out what skills are required. Provides examples of applications in different fields. However, I don't know how useful such information really is. It can be good as guidance of where to continue learning, but the information itself is too diluted to be practical, in my opinion. In some parts there are code, in some, there are theoretical considerations, etc.

[danielle; ▵ · Rating 3 out of 5]

I listened to this book to review data science concepts prior to technical interviews, but the focus was more on defining data science and its position within business than on data science methods. Oh well. Back to the textbooks.

[Angie Boyter · Rating 1 out of 5]

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

[John Dolan · Rating 3 out of 5]

Missing a lot of fundamentals, some useful information scattered amongst lots of repetition.

[Daniel · Rating 4 out of 5]

I had read 2 editions of machine learning for dummies but, I had never read data science for dummies. So I was excited to read it. This book sure has a lot of information in it. It talks about Algorithms to information about business models for data science businesses.

My favorite chapters from the book are chapter 19 Ten Phenomenal resources for open data and chapter 20 Ten free or low-cost data science tools and applications. I got excited reading about all the places I could get data from.

I wish I said I could understand ever word of data science for dummies. But, I didn't. I started out understanding what I was reading then started having trouble understanding what I was reading. Then when I got to chapter 19 and 20 I started understanding things again. Data science is so complicated I don't know if I will ever be able to learn it.

Maybe if I reread the book and took it at a slower pace. Anyway to me data science seems complicated. But, it seems like it would be fun if I could get good at it. Anyway I think I have a better idea about what data science is and what it can do after reading data science for dummies.

Also this book has no coding examples. That was OK, with me though.

[Devon Ellison · Rating 3 out of 5]

I think for other people who want to get into computer science and how it works this is a good book for them, it just wasn't for me though, at the beginning of the book it was interesting and easy to understand but I guess at some point it got a bit to complex for my understanding. I liked the book and think that the author did a really good job of breaking down large and complex ideas into smaller and easier-to-understand ideas. the book also covers a plethora of topics using real-world examples, but I just didn't find many of them to be very interesting.

[Scott West · Rating 2 out of 5]

Some good general advice on Data Science applications and getting started with practical websites. However many resources seem ephemeral and unsubstantiated. There could be more detailed information on machine learning and practical Data Science applications in fields like Healthcare and Finance rather than the fleeting examples given. Limited information on practically doing data wrangling and applying analysis and Machine Learning to uncover insights. A nice idea, lacking in information.

[Greg · Rating 4 out of 5]

Great overview of data science. I thought going in there would be more examples of the programming and implementation side of things, but she does point to her website which has some examples in Python. Also has useful information on how to start and develop a career in data science, and lists of career tracks and job titles depending on what path you want your data science career to take.

[Courtney Hays · Rating 3 out of 5]

Read this to gauge whether I want to pursue a data analysis certification. Turns out, I do not.

While this book includes some good resources for individuals interested in data science, I found the information to be, at times, vague and repetitive. Chapters 15-17 were painfully repetitive and could have been condensed. I’m glad to be done with this read through.

[Kelsey]

wasn't what I was expecting

[Lee · Rating 1 out of 5]

Read 23%.

[Franklin Tan · Rating 3 out of 5]

This is a good source for basic introductory knowledge, but it felt more like a glossary of concepts.

[Craig Beyers · Rating 4 out of 5]

This is a thorough, introductory book on data science, with concise descriptions of how to use Python and related libraries in data science.

[Oak · Rating 5 out of 5]

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*

[Song · Rating 4 out of 5]

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.

[Thomas · Rating 3 out of 5]

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.

[Jiannina · Rating 5 out of 5]

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.

[Ty Roper]

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

[Eric · Rating 4 out of 5]

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

[Darren Chen]

Although it is published in 2015, it still has a good bird's eye view of the field of data science and the tools used.

[Cathy Craig · Rating 2 out of 5]

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.

[Sam Isse · Rating 4 out of 5]

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

[Khalid Alawar · Rating 5 out of 5]

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

[Ahmed Mohammed · Rating 3 out of 5]

Good overall but very lacking in the data visualization part, it puch you to d3js which i don't recommend as a tool for visualization