Data Science for Dummies is a broad introduction to the many facets of doing data science. One of the common perceptions I hear about today is that, in order to do data science, one has to have experience in a number of fields: statistics, programming, data visualization and domain-specific knowledge. Given that most people getting into data science come from one of these specializations, it is helpful to see what the other specializations require and have to offer. Lillian Pierson's book goes a long way toward fulfilling that mission by surveying the many areas that feed into a data scientist's skill set.The first part of the book does an excellent job of explaining what data science and is a great place to start for anyone interested in getting into this field. The case study and success story sections at the end of chapters 2 and 3, respectively, are excellent wrap-ups of how data science can be applied.Having taken some online statistical courses, I was curious to see how Pierson would explain the complexity and the reasoning required in Part II of the book, entitled 'Using Data Science to Extract Meaning from Your Data'. Probability and Statistics are explained reasonably well, and there is a good chapter on Clustering. Part II overall does explain what meaning you can get from data, but if you are looking for a detailed tutorial on applying statistics, you should consider this section of the book a survey, not a comprehensive tutorial. That said, the author does a good job of covering some of the important techniques such as linear regression and the k-nearest neighbor algorithm.Pierson is a consultant, speaker and writer who has written some interesting data analysis studies that caught my interest. Part III of the book on Data Visualization and communicating clearly is maybe the most inspired section of her book. This section goes into great detail explaining what it means to connect to an audience, showing excellent visualizations in the process. This is followed by a good introduction to one of the hottest visualization technologies today, D3. The D3 chapter is a good introduction for those who have not used it before.For those who are more inclined to use ready-made, web-based tools rather than create their own visualizations in programming, the chapter on Web-Based Applications for Visualization Design does a nice survey of some available tools. There is also a lot of interesting material on creating spatial maps with data, which in the interest of surveying the field of data science, is a great area to start given how much geographically-oriented data is available on the web.Part IV of the book entitled 'Computing for Data Science' gives a survey of three programming languages that are useful for getting started in data science. Python and R are currently both popular, and most data scientists use one or the other although some will use both. SQL, however, is a database programming language which I think anyone doing data science should learn because there will come a time when you have to extract data from a relational database. Pierson covers these languages well so that you can get your feet wet before deciding where to dive deeper.The book finishes with several chapters on applying data science in journalism, environmental science, e-commerce and criminal justice. These are good survey chapters that provide insight into the many applications of data science.Overall, I think this book is a great survey of what is required to do data science. If you are new to the field, even if you have some statistics, programming or data visualization background, Pierson's book will broaden your knowledge and likely open your eyes to the many exciting opportunities you would have as a data scientist.