Docere et Facere, To Teach and To Do

“Helping aspiring data scientists forge their own career paths, more universities are offering programs in data science or analytics.” – Wall Street Journal, March 13, 2017

George Bernard Shaw’s play Man and Superman provides the maxim, “He who can, does. He who cannot, teaches.” Most of us know this as “Those who can’t do, teach.” (And Woody Allen added a punch line in Annie Hall: “… and those who can’t teach, teach gym.”)

I’m determined both to do and to teach, because I enjoy each of them. When it comes to data and advanced analytics, something I’ve been using or abusing my entire career, I’m excited about expanding what I’m doing. So below I’m highlighting two cool opportunities I’m engaging in now…

 

Teaching Big Data Architectures and Analytics in the IC

I’ve just been asked by the government to teach again a popular graduate course I’ve been doing for several years, “Analytics: Big Data to Information.” It’s a unique course, taught on-site for professionals in the U.S. intelligence community, and accredited by George Mason University within GMU’s Volgenau Graduate School of Engineering. My course is the intro Big Data course for IC professionals earning a master’s or Ph.D. from GMU’s Department of Information Sciences and Technology, as part of the specialized Directorate for Intelligence Community Programs.

I enjoy teaching enormously, not having done it since grad school at Stanford a million years ago (ok, the ’80s). The students in the program are hard-working data scientists, technologists, analysts, and program managers from a variety of disciplines within the IC, and they bring their A-game to the classroom. I can’t share the full syllabus, but here’s a summary:

This course is taught as a graduate-level discussion/lecture seminar, with a Term Paper and end-of-term Presentation as assignments. Course provides an overview of Big Data and its use in commercial, scientific, governmental and other applications. Topics include technical and non-technical disciplines required to collect, process and use enormous amounts of data available from numerous sources. Lectures cover system acquisition, law and policy, and ethical issues. It includes discussions of technologies involved in collecting, mining, analyzing and using results, with emphasis on US Government environments.

I worry that mentioning this fall’s class now might gin up too much interest (last year I was told the waiting list had 30+ students who wanted to get in but couldn’t, and I don’t want to expand beyond a reasonable number), but when I agreed this week to offer the course again I immediately began thinking about the changes in the syllabus I may make. And I solicit your input in the comments below (or by email).

math-1500720_960_720.jpgFor the 2016 fall semester, I had to make many changes to keep up with technological advance, particularly in AI. I revamped and expanded the “Machine Learning Revolution” section, and beefed up the segments on algorithmic analytics and artificial intelligence, just to keep pace with advances in the commercial and academic research worlds. Several of the insights I used came from my onstage AI discussion with Elon Musk in 2015, and his subsequent support for the OpenAI initiative.

More importantly I provided my students (can’t really call them “kids” as they’re mid-career intelligence officials!) with tools and techniques for them to keep abreast of advances outside the walls of government – or those within the walls of non-U.S. government agencies overseas. So I’m going to have to do some work again this year, to keep the course au courant, and your insight is welcome.

But as noted at the beginning, I don’t want to just teach gym – I want to be athletic. So my second pursuit is news on the work front.

 

Joining an elite Mission Analytics practice

I’m announcing what I like to think of as the successful merger of two leading consultancies: my own solo gig and Deloitte Consulting. And I’m even happy Deloitte won the coin-toss to keep its name in our merger 🙂

For the past couple of years I have been a solo consultant and I’ve enjoyed working with some tremendous clients, including government leaders, established tech firms, and great young companies like SpaceX and LGS Innovations (which traces its lineage to the legendary Bell Labs).

But working solo has its limitations, chiefly in implementation of great ideas. Diagnosing a problem and giving advice to an organization’s leadership is one thing – pulling together a team of experts to execute a solution is entirely different. I missed the camaraderie of colleagues, and the “mass-behind-the-arrowhead” effect to force positive change.

When I left Microsoft, the first phone call I got was from an old intelligence colleague, Scott Large – the former Director of NRO who had recently joined Deloitte, the world’s leading consulting and professional services firm. Scott invited me over to talk. It took a couple of years for that conversation to culminate, but I decided recently to accept Deloitte’s irresistible offer to join as a specialist leader of Mission Analytics, working with a new and really elite team of experts who understand advanced technologies, are developing new ones, and are committed to making a difference for government and the citizens it serves.

Our group is already working on some impressively disruptive solutions using massive-scale data, AI, and immersive VR/AR… it’s wild. And since I know pretty much all the companies working in these spaces, I decided to go with the broadest, deepest, and smartest team, with the opportunity for highest impact.

Who could turn down the chance to teach, and to do?

 

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