Section 1.3 Skills for Data Scientists
All in all, our cereal box and grocery store example helps to highlight where data scientists get involved and the skills they need. Here are some of the skills that the example suggested:
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Learning the application domain—The data scientist must quickly learn how the data will be used in a particular context.
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Communicating with data users—A data scientist must possess strong skills for learning the needs and preferences of users. Translating back and forth between the technical terms of computing and statistics and the vocabulary of the application domain is a critical skill.
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Seeing the big picture of a complex system—After developing an understanding of the application domain, the data scientist must imagine how data will move around among all of the relevant systems and people.
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Knowing how data can be represented—Data scientists must have a clear understanding about how data can be stored and linked, as well as about “metadata” (data that describes how other data are arranged).
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Data transformation and analysis—When data become available for the use of decision makers, data scientists must know how to transform, summarize, and make inferences from the data. As noted above, being able to communicate the results of analyses to users is also a critical skill here.
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Visualization and presentation—Although numbers often have the edge in precision and detail, a good data display (e.g., a bar chart) can often be a more effective means of communicating results to data users.
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Attention to quality—No matter how good a set of data may be, there is no such thing as perfect data. Data scientists must know the limitations of the data they work with, know how to quantify its accuracy, and be able to make suggestions for improving the quality of the data in the future.
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Ethical reasoning—If data are important enough to collect, they are often important enough to affect people’s lives. Data scientists must understand important ethical issues such as privacy, and must be able to communicate the limitations of data to try to prevent misuse of data or analytical results.
The skills and capabilities noted above are just the tip of the iceberg, of course, but notice what a wide range is represented here. While a keen understanding of numbers and mathematics is important, particularly for data analysis, the data scientist also needs to have excellent communication skills, be a great systems thinker, have a good eye for visual displays, and be highly capable of thinking critically about how data will be used to make decisions and affect people’s lives. Of course there are very few people who are good at all of these things, so some of the people interested in data will specialize in one area, while others will become experts in another area. This highlights the importance of teamwork, as well.
In this Introduction to Data Science eBook, a series of data problems of increasing complexity is used to illustrate the skills and capabilities needed by data scientists. The open source data analysis program known as “R” and its graphical user interface companion “R-Studio” are used to work with real data examples to illustrate both the challenges of data science and some of the techniques used to address those challenges. To the greatest extent possible, real datasets reflecting important contemporary issues are used as the basis of the discussions.
No one book can cover the wide range of activities and capabilities involved in a field as diverse and broad as data science. Throughout the book references to other guides and resources provide the interested reader with access to additional information. In the open source spirit of “R” and “R Studio” these are, wherever possible, web-based and free. In fact, one of the guides that appears most frequently in these pages is “Wikipedia,” the free, online, user-sourced encyclopedia. Although some teachers and librarians have legitimate complaints and concerns about Wikipedia, and it is admittedly not perfect, it is a very useful learning resource. Because it is free, because it covers about 50 times more topics than a printed encyclopedia, and because it keeps up with fast moving topics (like data science) better than printed encyclopedias, Wikipedia is very useful for getting a quick introduction to a topic. You can’t become an expert on a topic by only consulting Wikipedia, but you can certainly become smarter by starting there.
Another very useful resource is Khan Academy. Most people think of Khan Academy as a set of videos that explain math concepts to middle and high school students, but thousands of adults around the world use Khan Academy as a refresher course for a range of topics or as a quick introduction to a topic that they never studied before. All of the lessons at Khan Academy are free, and if you log in with a Google or Facebook account you can do exercises and keep track of your progress.
At the end of each chapter of this book, a list of Wikipedia sources and Khan Academy lessons (and other resources too!) shows the key topics relevant to the chapter. These sources provide a great place to start if you want to learn more about any of the topics that chapter does not explain in detail.
Obviously if you are reading this book you probably have access to an eBook reader app. You can also access this book as a PDF on the book’s website. It is valuable to have access to the Internet while you are reading, so that you can follow some of the many links this book provides. Also, as you move into the sections in the book where open source software such as the R data analysis system is used, you will sometimes need to have access to a desktop or laptop computer where you can run these programs.
One last thing: The book presents topics in an order that should work well for people with little or no experience in computer science or statistics. If you already have knowledge, training, or experience in one or both of these areas, you should feel free to skip over some of the introductory material and move right into the topics and chapters that interest you most. There’s something here for everyone and, after all, you can’t beat the price!
