Smart Data Discovery Using SAS® Viya®: Powerful Techniques for Deeper Insights
This book is targeted towards a curious analytics professional – from a business manager, citizen data scientist, analyst, or statistician to an expert data scientist who is interested in learning to achieve smart data discovery via a graphical interface approach using SAS Viya software. Felix describes the smart discovery process with SAS Visual Analytics, using experimentation and exploration to creating a data story through model building and visualizations.
The general analytics concepts, specifically SAS Visual Analytics advice and code-less examples shared throughout the book will help all analytics professionals in their data discovery journey using SAS Visual Analytics. A handy resource to use in experiencing the 'wonder and power of data'.
Michelle Homes
Metacoda
Australia
With the increasing need for utilizing data analytics for business growth, comes the need for better data tools, talents, and resources. This book by Felix Liao is surely going to play its intended part in satisfying that ever-increasing need. With its practical approach and example-based teaching methodology, the book can safely be marked as one of the essentials for anyone looking to expand their knowledge on SAS Viya as well as the world of augmented analytics as a whole. It will be beneficial for business leaders, analytics professionals, and machine learning enthusiasts alike.
What’s most important is the easy-to-understand way Felix explains everything. This book can be read and utilised even by a layman with no prior programming experience. Surely a must-have for your data library!
Jason Tan
Podcast Host at The Analytics Show
In this book, you will learn what it means to be a data scientist and participant in the data-driven decision making process, regardless of your technical background. Using SAS® Viya® and real-world examples, the author takes you through a step-by-step journey of data discovery that will leave you with a greater understanding of your data and a solid foundation for advanced analytics. No matter your field of study, this is a fantastic way to learn how to make the most of your data!
Deanna (DeDe) Naomi Schreiber-Gregory
Co-owner, Operations and Finance
Independent Consultant - Statistics & Data Management
Juxdapoze, LLC
Felix Liao knows what he wants to say and says it clearly. This is an exceptionally well-crafted book and is a pleasure to read. Everything in the text is understandable. It’s obvious that the author has gone over it with a fine tooth comb and has removed any obscurity from it. He explains things patiently and carefully to the reader.
The structure of the book is logical, starting with an explanation of its purpose and intended audience. Next, it proceeds to detailed explanations of how to use SAS® Visual Analytics. There is a heavy emphasis on statistics and statistical graphics. This is where the book’s clarity is particularly beneficial. The reader is not thrown into statistical jargon and formulas. The author takes the reader by the hand and is a helpful guide.
To his credit, the author provides the reader with a realistic appraisal of data preparation needed for analysis. He affirms the widely-held belief that 80% of data analysis is data management. He shows how SAS Visual Analytics addresses that need and contains capabilities to assist in data preparation.
A central theme that Felix Liao presents is the concept of a “citizen data scientist.” It merits a second look. The book supports the belief that one person can develop hybrid expertise. In this case, such a person is someone who combines business domain knowledge, product know-how, and analytical techniques. Although such people can and do exist (we don’t need to joke about unicorns), this is a tall order, and many data science departments rely on teams of specialists working together.
For one thing, data preparation often requires in-house familiarity with the details of data access and extraction from a variety of sources, from Excel, to relational data bases, to Hadoop. Often, a lot of time and expertise is needed for these tasks.
As the author points out, technology is evolving. SAS Visual Analytics, like other SAS software, receives regular version updates. Some of the updates can have a positive impact on what an end-user can see and do. Is a “citizen data scientist” also expected to keep up-to-date on the details and benefits of software updates?
In general, what kind of SAS background is needed to make full use of the book? Sometimes the author assumes a lot and, for example, mentions “data views” rather casually. This is technical material that a “citizen data scientist” might not know about but is intuitive to a SAS expert.
Nevertheless, the focus on “citizen data scientists” does not at all diminish the value of Felix Liao’s book. There are plenty of ways the book can be educational and beneficial. Perhaps it’s just better not to put all the eggs into the “citizen data scientist” basket. The material covered can be used to supplement training and education (although the author doesn’t think it can replace them). Cross-fertilization is often helpful. Similarly, the expertise obtained from assimilating the material in the book can be of use to SAS consultants. They too may need to get up some curves to support people who have business domain knowledge, in order to build customized analyses and reports for them.
The book explains the capability of SAS Visual Analytics to assist in data preparation for an end-user. This is a somewhat understated but important message in the book because it illustrates what “self-service” is becoming. A little historical background is worth remembering. Self-service didn’t originate with this product or SAS Viya. SAS has recognized its value for many years, and developed several products that have attempted to support that goal. SAS/ASSIST®, SAS/EIS®, SAS/AF®, and SAS Enterprise Guide® come to mind.
What’s new and different is that Visual Analytics and SAS Viya offer intelligent self-service. This is what the word “Smart” in the title refers to. Felix Liao explains that Visual Analytics includes auto-charting functionalities that automatically select the relevant visualization type based on the type and combination of variables the analyst drags onto the canvas. Auto-charting speeds up the exploration process and encourages experimentation and testing of multiple variables.
Similarly, SAS Viya assists in model building and offers point and click ways to develop and refine modeling. The book provides clear illustrations of this in the sections on decision tree and regression modeling techniques. The author includes these sections as a way for readers to “get their hands dirty” and dig into practical examples. This is another potential overlap with SAS training and education and it would be a good idea to evaluate the potential for cross-fertilization.
In conclusion, it is here, toward the end of the book, that the author reaffirms the idea stated at the beginning of the book that “smart” also refers to the software, not just to the user. The evolution from fixed self-service to intelligent self-service incorporating AI is a significant characteristic of new SAS technology. It raises many exciting questions about where this kind of AI may lead and what kinds of business knowledge may become possible. Where could AI suggest appropriate statistical analysis techniques? Clinical trials might be a candidate.
Jim Sattler
Satmari Software Systems
Manila, Philippines