Satish Garla
Satish Garla is a former Senior Analytical Consultant in Risk Practice at SAS. He has extensive experience in risk modeling for healthcare, predictive modeling, text analytics, and SAS programming. He has a distinguished academic background in analytics, databases, and business administration. Satish holds a master's degree in Management Information Systems at Oklahoma State University and has completed the SAS and OSU Data Mining Certificate program. He has three years of professional experience as an Oracle CRM Consultant, and he is a SAS Certified Advanced Programmer for SAS 9 and a Certified Predictive Modeler using SAS Enterprise Miner 6.1.
Satish's research in health risk analytics, text analytics, market segmentation, and social media analytics has been presented at SAS Global Forum, JMP Discovery Summit, and other SAS regional conferences. He has developed a SAS macro to collect and analyze customized tweets from Twitter for which he was awarded SAS Student Ambassador for 2011 by SAS. He is also part of a team that won second place in the Data Mining shootout competition 2011 organized by SAS and Central Michigan University. Satish's paper on "Analyzing Sentiments Expressed about Wal-Mart and Sam's Club in Tweets" won first prize in the Walmart analytics competition and SAS Global Forum 2012 social media section. He also contributed a case study on sentiment analysis for the book, Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications.
By This Author
Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS®
By Goutam Chakraborty, Murali Pagolu, and Satish Garla
This hands-on guide to text analytics using SAS provides detailed, step-by-step instructions and explanations on how to mine your text data for valuable insight. Through its comprehensive approach, you'll learn not just how to analyze your data, but how to collect, cleanse, organize, categorize, explore, and interpret it as well. Text Mining and Analysis also features an extensive set of case studies, so you can see examples of how the applications work with real-world data from a variety of industries.
Featured Authors
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Ron Klimberg, PhD, is a professor at the Haub School of Business at Saint Joseph's University in Philadelphia, PA.
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Terisa Roberts is a director and Global Solution lead for Risk Modeling and Decisioning at SAS. She has extensive experience in quantitative risk management, regulatory compliance, and model governance and validation. She has worked in financial services, telecommunications, government, energy, and retail sectors.
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Robert N. Rodriguez retired from SAS in 2018 after serving as a senior director in research and development. Bob received a PhD in statistics from the University of North Carolina at Chapel Hill and was a research statistician at General Motors Research Laboratories before joining SAS.
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Todd Case has been working in the pharmaceutical industry as a statistical programmer for over 20 years. He completed his MS in Epidemiology from Columbia University and has worked at Forest Laboratories (now Allergan), Genentech, Bristol-Myers Squibb, and Biogen. He currently works at Vertex Pharmaceuticals.