Workshop
Wednesday, March 19, 2014 in San Francisco
Full-day: 9:00am - 4:30pm
Making Text Mining Work: Practical Methods and Solutions
A free copy of Dr. Fast's book on Practical Text Mining is included.
Intended Audience: Practitioners seeking tools to analyze unstructured text data.
Knowledge Level: No previous experience required though some technical background in statistics or predictive analytics will be useful.
Attendees will receive an electronic copy of the course notes via USB drive.
Workshop Description
In their 2011 Hype Cycle report, Gartner has Text Analytics sliding into the "Trough of Disillusionment", highlighting the difficulty of achieving its great promise. Despite this verdict, text mining and text analytics can be valuable tools, if you know where to look for the solution. This workshop will address:
- The text mining solutions available now and the problems for which they are best suited
- Best practices in the key text mining areas
- How to set positive but realizable expectations for the return on investment of a text mining project
This one-day session surveys standard and advanced methods for text mining. Dr. Fast will describe the key inner workings of leading algorithms, demonstrate their performance with business case studies, compare their merits, and show how to pick the approach best suited for your project. Methods covered include search indexes, text classification, information extraction, document similarity and more.
The key to successfully leveraging these methods is to find the right "hammer" for your text "nails" and understand the limits of those techniques.
Dr. Fast will share his experience mining text on real-world applications in several fields, highlighting the range of available solutions and how to combine technologies to maximize the value of the vast store of (untapped) unstructured data.
If you'd like to become a text mining practitioner – or if you already are, and would like to hone your knowledge across methods and best practices, this workshop is for you!
What you will learn:
- The tremendous value of learning from unstructured textual data
- How to choose the proper text mining solution
- Text mining best practices
Schedule
- Workshop starts at 9:00am
- First AM Break from 10:00 - 10:15am
- Second AM Break from 11:15 - 11:30am
- Lunch from 12:30 - 1:15pm
- First PM Break: 2:00 - 2:15pm
- Second PM Break: 3:15 - 3:30pm
- Workshop ends at 4:30pm
Attendees receive a free copy of Andrew Fast's book on Practical Text Mining, an electronic copy of the course notes via USB drive, and an official certificate of completion at the conclusion of the workshop.
About the Presenter
Dr. Andrew Fast leads research in Text Mining and Social Network Analysis at Elder Research, the nation's leading data mining consultancy. ERI was founded in 1995 and has offices in Charlottesville VA and Washington DC, (www.datamininglab.com). ERI focuses on Federal, commercial, investment, and security applications of advanced analytics, including stock selection, image recognition, biometrics, process optimization, cross-selling, drug efficacy, credit scoring, risk management, and fraud detection.
Dr. Fast graduated Magna Cum Laude from Bethel University and earned Master's and Ph.D. degrees in Computer Science from the University of Massachusetts Amherst. There, his research focused on causal data mining and mining complex relational data such as social networks. At ERI, Andrew leads the development of new tools and algorithms for data and text mining for applications of capabilities assessment, fraud detection, and national security.
Dr. Fast has published on an array of applications including detecting securities fraud using the social network among brokers, and understanding the structure of criminal and violent groups. Other publications cover modeling peer-to-peer music file sharing networks, understanding how collective classification works, and predicting playoff success of NFL head coaches (work featured on ESPN.com). With John Elder and other co-authors, Andrew has written a book on Practical Text Mining, to be published in January, 2012.
