Charles Givre

Charles Givre

Senior Lead Data Scientist at Booz Allen Hamilton

Bio: Mr. Charles Givre has worked as a Senior Lead Data Scientist for Booz Allen Hamilton for the last six years where he works in the intersection of cyber security and data science. For the last few years, Mr. Givre worked on one of Booz Allen's largest analytic programs where he led data science efforts and worked to expand the role of data science in the program. Mr. Givre is passionate about teaching others data science and analytic skills and has taught data science classes all over the world at conferences, universities and for clients. Most recently, Mr. Givre taught a data science class at the BlackHat conference in Las Vegas and the Center for Research in Applied Cryptography and Cyber Security at Bar Ilan University. He is a sought-after speaker and has delivered presentations at major industry conferences such as Strata-Hadoop World, BlackHat, Open Data Science Conference and others. One of Mr. Givre's research interests is increasing the productivity of data science and analytic teams, and towards that end, he has been working extensively to promote the use of Apache Drill in security applications and has contributed to the code base. Mr. Givre teaches online classes for O'Reilly about Drill and Security Data Science and is a coauthor for the forthcoming O'Reilly book about Apache Drill. Prior to joining Booz Allen, Mr. Givre, worked as a counterterrorism analyst at the Central Intelligence Agency for five years. Mr. Givre holds a Masters Degree in Middle Eastern Studies from Brandeis University, as well as a Bachelors of Science in Computer Science and a Bachelor's of Music both from the University of Arizona. Mr. Givre holds various Certifications including CISSP, Security+, Network+, Certified Penetration Tester, and CDIA+. He speaks French reasonably well, plays trombone, lives in Baltimore with his family and in his non-existant spare time, is restoring a classic British sports car. Mr. Givre blogs at thedataist.com and tweets @cgivre.

Tutorial: Visualizing Machine Learning Models

Tutorial: Visualizing Machine Learning Models

One of the big issues I’ve encountered in my teaching is explaining how to evaluate the performance of machine learning models.  Simply put, it is relatively trivial to generate the various performance metrics–accuracy, precision, recall, etc–if you wanted to visualize any of these metrics, there wasn’t really an easy way to do that.  Until now…. […]

Announcing the First Release of Griffon: A Virtual Environment for Data Science

Announcing the First Release of Griffon: A Virtual Environment fo...

My colleagues Austin Taylor and Melissa Kilby are proud to announce the first stable release of Griffon:  A Virtual Machine for Data Science.   Griffon is a virtual machine which contains many data science tools pre-configured, installed and linked up to make it so that you don’t have to be a Linux expert to try […]

The Best Of Both Worlds: Joining Online And Local Datasets With Apache Drill

The Best Of Both Worlds: Joining Online And Local Datasets With A...

data.world is rapidly establishing itself as the premier site for data scientists and analysts to host and collaborate on datasets. I have been impressed with data.world’s growth and interested in starting to use the platform in my professional projects. On data.world, datasets can be open and visible to the general public or they can be […]

Tips for Debugging Code without F-Bombs – Part 1

Tips for Debugging Code without F-Bombs – Part 1

Debugging code is a large part of actually writing code, yet unless you have a computer science background, you probably have never been exposed to a methodology for debugging code.  In this tutorial, I’m going to show you my basic method for debugging your code so that you don’t want to tear your hair out. […]

Why Musicians Make Good Analysts

Why Musicians Make Good Analysts

I recently read Taming the Big Data Tidal Wave by Bill Franks of Teradata and in the book (which is going on my recommended reading list) he has a section about the ideal analyst.  While I am admittedly very biased on this one, Mr. Franks makes a very good point that in many instances the best […]

The Biggest Problem in Data Science and How to Fix It

The Biggest Problem in Data Science and How to Fix It

Imagine you have some process in your organization’s workflow that consumes 50%-90% of your staff’s time and contributes no value to the end result. If you work in the data science or data analytics fields you don’t have to imagine that because I’ve just described what is, in my view, the biggest problem in advanced […]

The Morality of Data Collection

The Morality of Data Collection

I just returned from Strata + Hadoop World in San Jose, where I gave a talk entitled “Kosher Collection: Best Practices in Data Handling“.  I really had an amazing time at Strata this year and major kudos to the organizers for putting on a great show. The central premise of my talk is that in […]

Start Informing your Business with Data Science, How?

Start Informing your Business with Data Science, How?

I’m writing this blog post in the departure lounge at Heathrow, on my way back from Strata + Hadoop World, London.  Whilst at Strata, speakers kept coming back to the idea that an ever growing number of large businesses are not really happy with the investments they have made in analytics and data science.  One […]

Two Skills Every Data Scientist Needs

Two Skills Every Data Scientist Needs

I saw an article a little while ago on LinkedIn (which at the time of writing I cannot find) but the basic premise of the article was that problem solving was the most important skill for data scientists to be effective at their job.  (If anyone can find the article, please send me a PM as […]