• Print  |
  • Feedback  |

Training & Bookstore

Bookstore» Satish Garla Author Page

About Satish Garla

Satish Garla is currently working as a Consultant in Risk Consulting practice at SAS. He has a distinguished academic background in Analytics, Databases and Business Administration. He has a Master's degree in Management Information Systems at Oklahoma State University. He has completed SAS and OSU Data Mining Certificate program. He has three years of professional experience as Oracle CRM Consultant. He is SAS Certified Advanced Programmer for SAS 9 and Certified Predictive Modeler using SAS Enterprise Miner 6.1. He has been using SAS tools extensively in research and consulting projects. His research in Text Analytics, Market Segmentation, Social Media Analytics has been published in SAS Global Forum, JMP Discovery Summit and other regional SAS conferences. Satish 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 Institute. 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. His team also won an honorable mention award in 2010 data mining shootout. Satish's poster on Analyzing Sentiments Expressed about Wal-Mart and Sam's Club in Tweets won first prize in the Wal-Mart Analytics student poster competition. Satish has contributed a case study on sentiment analysis for the book "Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications".
Conversations

question Questions or comments?
Coming Soon!

Managing and Analyzing Unstructured Data Using SAS Text Analytics: A Practical Guide book cover Managing and Analyzing Unstructured Data Using SAS Text Analytics: A Practical Guide
By Goutam Chakraborty, Satish Garla, and Murali Pagolu
Anticipated publication date: Fourth quarter 2013

Managing and Analyzing Unstructured Data Using SAS Text Analytics: A Practical Guide is a hands-on guide that provides detailed instructions and explanations of how to do text analytics. Filled with examples, analyses, as well as case studies, the authors take readers step-by-step through applying text analytics using primarily the point-and-click interfaces of SAS Enterprise Miner, SAS Text Miner, SAS Content Categorization Studio, SAS Information Retrieval Studio, and SAS Sentiment Analysis studio.

Intermediate users looking to retrieve, organize, categorize, analyze, interpret and use textual data for generating insights about customer and prospects’ behaviors and sentiments will benefit from this book and will quickly be able to put new skills into solving the text analytics problems of their industry.

Let me know when this book is available