Author: Charles Givre

Going Back to BlackHat!

Published January 26, 2018

For the last three years, I have had the honor and privilege of teaching a data science class at the BlackHat conference in Las Vegas. Well, I found out yesterday that I’ll be going back for a fourth year! Together with my amazing colleague Austin Taylor (@HuntOperator), we will be teaching Applied Data Science and Machine Learning for Cyber Security. It turns out that this is the only class at BlackHat this year about data science or machine learning!

Teaching a class at BlackHat is really a great experience, and quite terrifying at the same time. You’re presenting a class to the best of the best in security, so you really have to know your stuff. From my experience, the students are really on top of their game so it makes for very interesting and engaging sessions.

What’s New for This Year

This year’s class I have to say, will be the best one yet. We’ve developed a lot of new material including a lesson about improving the performance of models, beaconing detection with Austin’s Flare library, anomaly detection with K-Means clustering and more. I’ll be posting more about the course as we get closer to the event, but if you have any questions or requests, please let me know! If you’re interested, don’t wait, register now!

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Home Automation Update

Published January 21, 2018

One of my most popular posts is a tutorial I wrote two years ago about automating a gas fireplace and I get a lot of questions about home automation, so thought I’d write an update to that tutorial and review some products I’ve bought in the last two years.

My Original Goal

When I originally started seeing all the home automation products that were coming out, my original interest in them was to see what kind of data these devices gathered about their owners and I ended up giving two presentations on this topic at the Strata Conferences in New York and London. With all that said, for my research I wanted to be able to control the functionality of my home with my phone. After buying a bunch of devices, I was really disappointed. At the time, I wasn’t able to automate any lighting because my home was built in the 1920s and none of the light switches had a neutral wire–a requirement for Z-Wave switches. After we moved to a newer home, and I started automating lights and installing other automation devices, the thing that really frustrated me was the difficulty in getting all the devices to work together. I had high hopes for IFTTT, (and still do) but at the time, it seemed like it was a half-assed workaround.

I also was very disappointed with the available security systems. At the time, the choices were pretty much limited to systems which required you to pay rather steep monthly “monitoring fees” to some company and use rather low-tech devices, or half-baked products that did appear promising but seemed to be MVPs at best.

So what changed? I can answer that in one word: Alexa.

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Training all security teams in basic data science

Training all security teams in basic data science

Published October 25, 2017

I had the honor of doing a podcast with O’Reilly Media about the importance of training all levels of staff in Data Science. Here is the complete podcast:

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Thoughts on Teaching Data Science

Thoughts on Teaching Data Science

Published October 19, 2017

A big interest of mine is how to impart what little I know of the tools and techniques of data science to others. When I was at Booz Allen, I taught numerous classes both for internal staff and for various clients. I’ve also taught for Metis, O’Reilly Publishing and for the last three years, at BlackHat so I do have some experience in the matter. I’ve looked at MANY data science programs to see if what they are teaching lines up what I’m teaching and I’d like to share some things which I’ve noticed which will hopefully help you build a better data science program. My goal here is to share my mistakes and experiences over the years and hopefully if you are building a data science training program, you can learn from what I learned the hard way. I make no claims to be the perfect data science instructor, and I’ve made plenty of mistakes along the way.

While I’m at it, I’ll put in a plug for an upcoming data science class which I am teaching with Jay Jacobs of BitSight Security at the O’Reilly Security Conference in NYC, October 29-30th.

Really, data science instruction is an optimization problem: as an instructor, your goal is to minimize confusion whilst maximizing understanding. To do this, you must remove as many obstacles as possible from the students’ path which create dissonance. This may seem silly, but I have observed that if you have small errata in your code, or your code doesn’t work on their machine, even due to something they did, it significantly detracts from their learning experience and their opinion of you as an instructor. Therefore, removing all these obstacles to understanding is vital to your success as an instructor.

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The Difference between Software Development and Data Science

The Difference between Software Development and Data Science

Published September 11, 2017

I am fortunate enough to get regular messages from recruiters on LinkedIn asking to speak with me about software development jobs. Here’s the thing… I’m not a software developer, I do data science and data analytics. For the last seven years, my job title has included the words “data” and “scientist” in the title. I have never held a position with the words “Software” and “Developer” in the title. I have taught and am currently teaching classes with titles such as “Data Science for Security Professionals” and “Applied Data Science for Security”. All of this is on my LinkedIn profile, yet despite this, the messages continue.

On some level, it makes sense. If you look at my resume, you’d see that I have a degree in computer science, experience with various coding languages, and projects on github. Hell, I’m a committer for Apache Drill…

So what’s the difference between a data scientist and software developer?

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Tutorial: Visualizing Machine Learning Models

Tutorial: Visualizing Machine Learning Models

Published July 24, 2017

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….

Decision Boundary

Recently, I learned of a new python library called YellowBrick, developed by Ben Bengfort at District Data Labs, that implements many different visualizations that are useful for building machine learning models and assessing their performance. Many visualization libraries require you to write a lot of “boilerplate” code: IE just generic and repetitive code, however what impressed me about YellowBrick is that it largely follows the scikit-learn API, and therefore if you are a regular user of scikit-learn, you’ll have no problem incorporating YellowBrick into your workflow. YellowBrick appears to be relatively new, so there still are definitely some kinks to be worked out, but overall, this is a really impressive library.

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Tip of the Day: How I reclaimed 10GB of Hard Disk Space on my MacBook Pro

Tip of the Day: How I reclaimed 10GB of Hard Disk Space on my MacBook Pro

Published July 21, 2017

I love my MacBook Pro. Quite honestly, it’s the best laptop I’ve ever owned. However, my one regret is not buying the larger hard drive. Anyway, over the last few months, I’ve noticed that my free disk space kept on shrinking. I did all the usual stuff, deleted unneeded applications, ran various disk cleaning tools, etc until finally, I hit the motherlode… I discovered that brew, everyone’s favorite package manager was archiving old versions every time you ran brew update!!

To fix this problem… simply run: brew cleanup. I did this and voila! 10 GB of hard disk space cleaned up!

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

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

Published July 20, 2017

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 them out. If you are teaching a class, or if you are simply wanting to learn more about a particular tool, then Griffon is perfect for you.

You can download Griffon here: https://github.com/gtkcyber/griffon-vm.

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

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

Published May 16, 2017

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 private, with visibility limited to select contributors. That is sufficient to guarantee the privacy of the data most of the time. However, in some cases, you may be explicitly prohibited from uploading data to the cloud.

Would it be possible to use data.world in a project even when part of the data must not live in the cloud?

It didn’t take me long to answer that question. Fortunately, I also have been doing a meaningful amount of experimentation and development with Apache Drill over the last few years. What impresses me about Drill is its versatility and potential to dramatically increase analytic productivity, open up previously inaccessible data sources, query across data silos, and do so with the common language of ANSI SQL.

As I began experimenting with both, I couldn’t help but wonder if it might be possible to somehow combine the two.

Well, it turns out, it is…

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Academics and Data Science

Academics and Data Science

Published April 23, 2017

I received the following comment on an article: Let’s Stop Using the Term Fake Data Scientist and thought it merited a response. Usually the comments I receive are constructive even if they disagree with what I wrote, but this particular comment, demonstrated an arrogance which I believe is a huge problem in the data science world.

You can of course read the original article here, but the basic point was that data science is interdisciplinary field–consisting of a mixture of computer science, applied mathematics, and subject matter expertise, with a smattering of data visualization and communication skills. I believe that it is inappropriate to label someone as a fake simply because their skillset is proportioned differently than many math-centric data scientists. I’m also a believer in Dr. Carol Dweck’s thesis on having a growth-oriented mindset (as stated in her book Mindset) and that people who might be working in data science but whose skills need development in a certain area, should be given instruction and assistance rather than derogatory labels.

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