For folks unable to attend in-person, register to attend the event and two (2) hours before the event we will email you a link to watch the event via live webcast.
University of Colorado Denver - Tuesday March 19, 2013 @ 6:00pm MST
Large auditorium (170 person capacity) with 20' screen.
Location: CU Denver - North Classroom #1539 - 1200 Larimer Street
Denver, CO[masked] - Map: http://bit.ly/Tyznzg
6:00 - 6:15 Schmooze - Old Chicago Pizza will be served.
6:15 - 7:15 Bayesian Modeling by Mark Labovitz
7:15 - 8:30 Python Data Tools by Cary Miller
8:30 - 9:30 Network at Old Chicago at 14th and Market.
Bayesian Modeling, Inference, Prediction, and Decision-Making - Abstract
Uncertainty -- a state of incomplete information -- is pervasive yet we often must make key decisions based on imperfect information. The Bayesian statistical approach to uncertainty quantification, which involves combining information, both internal and external to your available data sources, into an overall information summary, is both logically internally consistent and simple to describe: there's one equation for inference (drawing valid conclusions about the underlying data-generating process), one for prediction of observables, and one for optimal decision-making.
However, specifying the ingredients that, when combined, formulate a good model for your uncertainty is a process -- combining elements of both art and science, intuition and rigor -- that can take a lifetime to master.
Mark Labovitz, an independent data scientist, will provide a brief overview of Bayesian Probability Theory and important technologies. Mr. Labovitz has over thirty (30) years experience as a Statistical / Quantitative Analyst and Team Lead specializing in the quantitative analysis of financial and marketing data. He has an MBA from University of Pennsylvania, The Wharton School and two (2) PhD's: Geomathematics – The Pennsylvania State University; and Applied Mathematics [masked]), Concentration in Statistics– University of Colorado Denver.
Python Data Tools - Abstract
Python is a high-level programming language designed for ease-of-use, speed, readability and tailored for data-intensive applications. Python supports multiple programming paradigms, including object-oriented, imperative and functional programming styles. It features a fully dynamic type system and automatic memory management, similar to that of Scheme, Ruby, Perl and Tcl.
Simply, Python is easy to learn, platform neutral and cheap. Python is a tool to build other tools with, including data analysis tools. It was actually conceived in a huge orgy of different programming paradigms, styles and languages.
This presentation covers the nuts and bolts of manipulating, processing, cleaning and crunching data in Python:
Learn basic and advanced NumPy (Numerical Python) features
Examples of using Python for analyzing large data with emphasis on concurrency
Different techniques in concurrency and when each is appropriate
Analyze streaming Twitter feeds using Python data tools such as pandas and The Natural Language Toolkit
Make these tools go fast for massive data sets using state-of-the-art Python concurrency techniques; multiprocessing, coroutines and greenlets
Use high-performance tools to load, clean, transform, merge, and reshape data Measure data by points in time, whether it’s specific instances, fixed periods, or intervals
Cary Miller, a long-time Python programmer, is a data scientist / open source programmer who has worked with data in many commercial and academic settings. He earned an MS in Applied Mathematics from the University of Colorado Denver. Mr. Miller currently works in litigation support at Catalyst Repository Systems in downtown Denver. He has a wide-ranging data background including work in the health care, energy, financial and bioinformatics sectors.