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Data Science For Dummies, by Lillian Pierson
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Discover how data science can help you gain in-depth insight into your business – the easy way!
Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles in organizations. Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their 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 before moving on to working with relational databases and unstructured data 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.
- Sales Rank: #77063 in Books
- Published on: 2015-03-09
- Original language: English
- Number of items: 1
- Dimensions: 9.30" h x .78" w x 7.40" l, .0 pounds
- Binding: Paperback
- 408 pages
From the Back Cover
Learn to:
- Deduce, discover, and communicate valuable insights from structured, semi-structured, and unstructured data sources
- Use meaningful visualizations to display and interpret data
- Take advantage of data processing tools like Hadoop� and MapReduce
- Turn your organization's data into a competitive advantage
Gain in-depth insight into your business with data science—this book makes it easy!
Big data is a big deal. This book helps you harness its power and give your business that all-important competitive edge. You'll learn to manage large amounts of data within hardware and software limitations, merge data sources, ensure consistent reporting, and interpret the data to tell your business story in a way that's easily understood.
- Get a grip on data science — understand what it is, who uses it, and what it can do
- How big is it — see how big data is defined and how to handle it with MapReduce, Hadoop, and alternative solutions
- It's probable — explore probability and statistics in interpreting your data
- Model ideas — learn about mathematical modeling, fuzzy multi-criteria programming, and modeling spatial data with statistics
- Make it visual — examine different types of data visualization techniques and learn to choose the style that's right for your purpose and your audience
- Ideal technology — learn where Python�, Open Source R, SQL, or even Excel� may be the tool you need
- The sky's the limit — see how data science can help solve environmental issues, drive e-commerce, and even predict criminal activity
Open the book and find:
- The basics of data science
- Ways to define big data
- How business benefits from data science
- Information about regression and clustering techniques
- Various visualization options
- Tips for designing great dashboards
- How data science is used in journalism
- Ten free data science tools and applications
About the Author
Lillian Pierson, P.E. is an entrepreneurial data scientist and professional environmental engineer. She's the founder of Data-Mania, a start-up that focuses mainly on web analytics, data-driven growth services, data journalism, and data science training services. She also covers the topics of data science, analytics, and statistics for prominent organizations like IBM and UBM.
Most helpful customer reviews
19 of 21 people found the following review helpful.
5 for definition 3 for tutorial
By Abacus
Lillian Pierson has written a remarkable book on one count (explaining Data Science) and a not so great one on another count (actually teaching the methods of Data Science). Based on the latter the usual audience of the “For Dummies” series may be somewhat disappointed.
After studying this book, you will not have developed the adequate proficiency to undertake any of the quantitative, programming, or spatial visualization methods the author presents. This is not surprising. You won’t readily pick up the entire fields of probability, regressions, and Monte Carlo simulation in the barely 20 pages that the author assigns to this matter. Similarly, you won’t be able to program much if anything by reading the 20 pages chapter on either Python or R. As you know, there are entire For Dummies books written on many of those subjects separately. And, many of those actually struggle to cover one single field adequately. For instance, in my view the “R for Dummies” and “Probability for Dummies” are mediocre (after studying them I rated both of them a 3). So, if others could not well cover R over 350 pages, what chance would Pierson have to cover it adequately in 20 pages.
However, this is not to say that this book does not have much value. It really does. And, that is in defining what Data Science is, in exploring all its aspects, in differentiating between Data Engineers and Data Scientists, in surveying practical solutions of how many fields are currently using Data Science and how, and in informing you what exists out there.
Additionally, some of the chapters are surprisingly strong (relative to some of the quick overview of specific subjects as demonstrated by other chapters). This includes many chapters near the end. Chapter 19 on Environmental Data Science is excellent. You can tell this is one of the author’s subspecialties. Chapter 20 on Data Science for driving E-Commerce is also excellent. The author has a surprisingly broad and deep culture on this specific topic. The Part of Tens is very good. Sometimes within the Dummies Series, the Part of Tens are wasted fillers mandated by the For Dummies format. This is not the case here, as this section is very informative with many excellent free data sources and data tools.
In summary, the book is excellent in defining what Data Science is. It is not always that good at teaching how to practice Data Science. As indicated above, this is no fault of the author. You can’t possibly teach a dozen complex mathematical disciplines, computer programs, and data visualization techniques in 350 lightly written pages. As mentioned, it is already remarkable that in that small pace she could survey and define so well all those disciplines (and give you a rudimentary idea of how to practice those).
The book can be used in many different ways.
For many readers it may serve as a pretty good benchmark of where they stand. If one is reading this book, it is not unlikely that you are already half way there to being a Data Scientist. This book will clearly spell out what are the skills you already have that qualify you as a Data Scientist, and what skill you are still missing to be a full fledged one.
This book can serve as an excellent stepping stone to further study many aspects of this fascinating field. Pierson has a very deep and broad culture in Data Science. She refers to tons of stuff, including very powerful open source web based data visualization and data analysis software that most people (even quants themselves) may have never heard off. She also defines well how to use certain computer programs and what they can do for you. Thus, if you are hesitating between learning R or Python, she will provide much valid information on the subject to facilitate your decision. In other words, Pierson provides an excellent survey of what exists out there, and what fields, methods, or programs are out there that you could study next. That alone is a huge value and is the main value of the book.
9 of 9 people found the following review helpful.
The How's and What's of Data Science. Along with Basic Instruction in Statisical Methods, Data Engineering, Machine Learning...
By Ira Laefsky
Excellent overview and how-to guide to data science (and some data engineering topics). A number of analytical techniques are discussed ranging from basic statistics and techniques of information visualization, to more specific and advanced methodologies for analyzing structured, semi-structured and unstructured data sets of various sizes and data velocities. Specific analytical techniques such as clustering, classification and K nearest neighbors are discussed. Programming languages and techniques are documented sufficiently to begin coding and select techniques which require further documentation. Data manipulation with Python and R, as well as Excel are described and some detailed examples are given. Sql data manipulation is also documented as are tools for Information Visualization. Finally applications ranging from environmental modeling to criminal activity prediction.
In all cases as little jargon as possible is used and these complex topics are described in a way that an intelligent layman can at least understand there application, and those with some background in college statistics or information technology could actually implement solutions using these tools (if supplemented by their help files and documentation). A clear and respectable introduction to data science and some aspects of data engineering.
--Ira Laefsky, MS Engineering/MBA formerly on the Senior Consulting Staff of Arthur D. Little, Inc. and Digital Equipment Corporation
8 of 8 people found the following review helpful.
Great Primer for Anyone Interested in Data Science or Analytics!
By Dennis Still
The field of data science has exploded in the last 10 years. Yes, data analysis has existed well before that, but the reality is that being a "data scientist" is pretty great right now. So many tools, opportunities, and growth potential for businesses around the world. I absolutely loved this book, and I am a seasoned data analytics professional. It covers topics generally and much more specifically depending on what your intent is. I use this book as a reference to paint a data picture of how business intelligence can help individual businesses succeed. When I am looking for a way to explain a concept to someone who might not live, eat, breath data analysis...this book is great.
The author was not out to write the definitive, historical view of data science. She was trying to paint a broad stroked picture of the landscape right now that is big data, data science, and all things analytics. It is an enjoyable read and I would recommend this book highly to my friends and colleagues within the field of data analytics. In addition, I would also highly recommend those that want to start in the field review this book and come to interviews ready to talk about these resources. It will make you a much better candidate. Thanks again Lillian Pierson for writing such a great primer on data science for the masses.
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