Simulation and Predictive Analytics

This is a guest post by Lawrence Leemis, a professor in the Department of Mathematics at The College of William & Mary. 

A front-page article over the weekend in the Wall Street Journal indicated that the number one profession of interest to tech firms is a data scientist, someone whose analytic skills, computing skills, and domain skills are able to detect signals from data and use them to advantage. Although the terms are squishy, the push today is for “big data” skills and “predictive analytics” skills which allow firms to leverage the deluge of data that is now accessible.

I attended the Joint Statistical Meetings last week in Boston and I was impressed by the number of talks that referred to big data sets and also the number that used the R language. Over half of the technical talks that I attended included a simulation study of one type or another.

The two traditional aspects of the scientific method, namely theory and experimentation, have been enhanced with computation being added as a third leg. Sitting at the center of computation is simulation, which is the topic of this post. Simulation is a useful tool when analytic methods fail because of mathematical intractability. Continue reading

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Natural Language Processing DC Discussion List

Data Community DC is pleased to announce a new service to the area data community: topic-specific discussion lists! In this way we hope to extend the successes of our Meetups and workshops by providing a way for groups of local people with similar interests to maintain contact and have ongoing discussions.

In a previous post, we announced the formation of the Deep Learning Discussion List for those interested in Deep Learning topics. The second topic-specific discussion group has just been created, a collaboration between Charlie Greenbacker (@greenbacker) and the DC-NLP Meetup Group and Ben Bengfort (@bbengfort) and DIDC - both specialists in Natural Language Processing and Computational Linguistics.

If you’re interested in Natural Language Processing and want to be part of the discussion, sign up here:

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Thoughts on the INFORMS Business Analytics Conference

This post, from DC2 President Harlan Harris, was originally published on his blog. Harlan was on the board of WINFORMS, the local chapter of the Operations Research professional society, from 2012 until this summer.

Earlier this year, I attended the INFORMS Conference on Business Analytics & Operations Research, in Boston. I was asked beforehand if I wanted to be a conference blogger, and for some reason I said I would. This meant I was able to publish posts on the conference’s WordPress web site, and was also obliged to do so!

Here are the five posts that I wrote, along with an excerpt from each. Please click through to read the full pieces: Continue reading

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DC NLP August 2014 Meetup Announcement: Automatic Segmentation

Curious about techniques and methods for applying data science to unstructured text? Join us at the DC NLP August Meetup!


This August, we’re joined by Tony Davis, technical manager in the NLP and machine learning group at 3M Health Information Systems and adjunct professor in the Georgetown University Linguistics Department, where he’s taught courses including information retrieval and extraction, and lexical semantics.

Tony will be introducing us to automatic segmentation. Automatic segmentation deals with breaking up unstructured documents into units – words, sentences, topics, etc. Search and retrieval, document categorization, and analysis of dialog and discourse all benefit from segmentation. Continue reading

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My Brother’s Keeper Data Jam: August 2

On February 27, President Obama announced the My Brother’s Keeper initiative, a program that combines the efforts of the government, philanthropic organizations, and the private sector to work with boys and young men of color to close the lingering achievement gap.

If you’re as passionate about social justice as you are about data, MBK is worth paying attention to. The Department of Education has made effective use of data one of the core tenets of MBK.

The first MBK Data Jam will be held on the Georgetown campus on August 2. Register as a participant and spend the whole day jamming on teams comprised of designers, data viz experts, developers, educators, and practitioners to create data visualizations of current challenges and build new tools in order to create ladders of opportunity for all youth, including boys and young men of color; thought leaders and subject matter experts in the MBK focus areas can register as a coach/mentor and spend the afternoon providing feedback to the teams formed in the morning.

Let’s show the world what data can do!

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Building Data Apps with Python on August 23rd


Data Community DC and District Data Labs are excited to be hosting another Building Data Apps with Python workshop on August 23rd.  For more info and to sign up, go to  There’s even an early bird discount if you register before the end of this month!


Data products are usually software applications that derive their value from data by leveraging the data science pipeline and generate data through their operation. They aren’t apps with data, nor are they one time analyses that produce insights – they are operational and interactive. The rise of these types of applications has directly contributed to the rise of the data scientist and the idea that data scientists are professionals “who are better at statistics than any software engineer and better at software engineering than any statistician.”

These applications have been largely built with Python. Python is flexible enough to develop extremely quickly on many different types of servers and has a rich tradition in web applications. Python contributes to every stage of the data science pipeline including real time ingestion and the production of APIs, and it is powerful enough to perform machine learning computations. In this class we’ll produce a data product with Python, leveraging every stage of the data science pipeline to produce a book recommender.

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Confire: A new Python library

Announcing the release of a new open source library: Confire is a simple but powerful configuration scheme that builds on the configuration parsers of Scapy, elasticsearch, Django and others. The basic scheme is to have a configuration search path that looks for YAML files in standard locations. The search path is hierarchical (meaning that system configurations are overloaded by user configurations, etc). These YAML files are then added to a default, class-based configuration management scheme that allows for easy development.

Full documentation can be found here:

Confire on PyPI

In a fit of procrastination, I put my first project on PyPI (the Python Package Index): Confire, a simple app configuration scheme using YAML and class based defaults. It was an incredible learning experience into the amount of work that goes into Python developers being simply able to pip install something! I wanted to go the whole nine yards, and set up documentation on Read The Docs and an open source platform on Github and even though it took a while, it was well worth the effort!

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Announcing Discussion Lists! First up: Deep Learning

Data Community DC is pleased to announce a new service to the area data community: topic-specific discussion lists! In this way we hope to extend the successes of our Meetups and workshops by providing a way for groups of local people with similar interests to maintain contact and have ongoing discussions. Our first discussion list will be on the topic of Deep Learning. The below is a guest post from John Kaufhold. Dr. Kaufhold is a data scientist and managing partner of Deep Learning Analytics, a data science company based in Arlington, VA. He presented an introduction to Deep Learning at the March Data Science DC Meetup.

A while back, there was this blog post about Deep Learning. At the end, we asked readers about their interest in hands-on Deep Learning tutorials.


The results are in, and the survey went to 11. And as in all data science, context matters–and this eleven is decidedly less inspiring than Nigel Tufnel’s eleven. That said, ten out of eleven respondents wanted a hands-on Deep Learning tutorial, and eight respondents said they would register for a tutorial even if it required hardware approval or enrollment in a hardware tutorial. But interest in practical hands-on Deep Learning workshops appears to be highly nonuniform. One respondent said they’d drive from hundreds of miles away for these workshops, but of the 3000+ data scientists in DC’s data and analytics community, presumably more local, only eleven total responded with interest.

In short, the survey was a bust. Continue reading

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Natural Language Processing in Python and R

This is a guest post by Charlie Greenbacker and Tommy Jones.

Data comes in many forms. As a data scientist, you might be comfortable working with large amounts of structured data nicely organized in a database or other tabular format, but what do you do if a customer drops 10,000 unstructured text documents in your lap and asks you to analyze them?

Some estimates claim unstructured data accounts for more than 90 percent of the digital universe, much of it in the form of text. Digital publishing, social media, and other forms of electronic communication all contribute to the deluge of text data from which you might seek to derive insights and extract value. Fortunately, many tools and techniques have been developed to facilitate large-scale text analytics. Operating at the intersection of computer science, artificial intelligence, and computational linguistics, Natural Language Processing (NLP) focuses on algorithmically understanding human language.

Interested in getting started with Natural Language Processing but don’t know where to begin? On July 9th, a joint meetup co-hosted by Statistical Programming DC, Data Wranglers DC, and DC NLP will feature two introductory talks on the nuts & bolts of working with NLP in Python and R. Continue reading

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Event Recap: DC Energy and Data Summit

This is a guest post by Majid al-Dosari, a master’s student in Computational Science at George Mason University.

I recently attended the first DC Energy and Data Summit organized by Potential Energy DC and co-hosted by the American Association for the Advancement of Science’s Fellowship Big Data Affinity Group. I was excited to be at a conference where two important issues of modern society meet: energy and (big) data!

There was a keynote and plenary panel. In addition, there were three breakout sessions where participants brainstormed improvements to building energy efficiency, the grid, and transportation. Many of the issues raised at the conference could be either big data or energy issues (separately). However, I’m only going to highlight points raised that deal with both energy and data.
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