New SAS book helps forge data science superheroes
Applying Data Science: Business Case Studies Using SAS® helps readers apply analytical and data science methods to answer pressing business questions
It’s a bird! It’s a plane! No, it’s applied data science coming to save the day. A new book from analytics leader SAS, Applying Data Science: Business Case Studies Using SAS ®, reveals the power of advanced analytics – and the professionals who wield it – to defeat even the toughest business problems.
Through eight real-world case studies told in 28 chapters, author Gerhard Svolba explores many data science methods, like Monte Carlo simulations, Poisson regressions, Kaplan-Meier estimates and more. Melding SAS code, illustrations and business context, Svolba demonstrates how various approaches can tackle true-life challenges, like rearranging customer data to be analyzed with unsupervised machine learning methods like association analysis. Any secrets about how to defeat supervillains, however, will be saved for the sequel.
“Over the last few years I answered many business questions from our customers using analytical methods,” said Svolba, a Principal Solutions Architect and analytics expert at SAS, about his inspiration for the book. “For most of these questions, the application of analytical methods made a large difference. It allowed me to cover the business questions in a much more comprehensive, precise and detailed way compared to the application of only graphical or descriptive methods.”
Written for business analysts, statisticians, data miners, data scientists, and SAS programmers, Applying Data Science covers a diverse range of topics, from forecasting new product demand to performing headcount survival analysis for employee retention. Don’t think delving into your childhood Monopoly game could help transform you into your company’s capeless superhero? The last case study may make you reconsider.
“This book [ . . . ] focuses on the application of data science methods, and the preparation, enhancement, and presentation of the results,” Svolba writes in the foreword. “It illustrates the perfect fit of using the SAS analytics platform for the analysis of various business questions with data science methods.”
The power of the SAS Platform is its ability to break down the silos to analytics insights and fully support all aspects of the analytics life cycle, by being inclusive of a diverse number of methods, data types, practitioner skill sets, deployment environments and use cases. Learning how to unlock the potential of SAS in these pages, data enthusiasts will want to keep this book on the shelf for future reference.
Applying Data Science: Business Case Studies Using SAS can be ordered from the SAS Publishing bookstore. Svolba’s other books include Data Preparation for Analytics Using SAS and Data Quality for Analytics Using SAS.
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