The data scientist unicorn problem

I'm looking for a Data Scientist position in San Francisco Bay Area and have been on many interviews. I have found organizations are looking for too much. They want high-level capabilities in statistics, machine learning, big data, computer science, AND specific domain knowledge. Since they hiring their first (and possible only) data scientist, they are looking for those capabilities in the same person. Add to the mix non-overlapping technologies. The human resources person is looking for someone with SQL and Hadoop chops because those are the most common buzz-words. The team member wants someone with R and Unix experience because they are useful right now "in the trenches." Everyone wants years of experience in an toddler stage industry (i.e., likes to make loud noises about important it is).

Instead of trying to find an unicorn, maybe they should look for a horse with reasonable looking prosthetic horn. As Dan Savage says,  “There's no settling down without some settling for.” Find a .77 Data Scientist that you can round up to The One.

Feynman on distributed computing

@ 9:00 

That time period was an incredibly productive time in the development of computer science. Many of the ideas we take for granted about computer architecture and algorithms were first put forward and developed. The mash-up of different fields of study created fertile ground for breakthroughs. The teams were able to deliver immediate value AND develop theoretical predictions for the future.

Now could be a similar time. Computer science should look to the fields of topology, bioinformatics, and cognitive neuroscience for the inspiration of new ideas.

(Having geniuses doesn't hurt.)

The 3 Ds of skill learning

Increasingly, the interesting problems in the world require computational approaches. I don’t have a computer science background but I have in the trenches experience with computational approaches, primarily in MATLAB. However, there are hard edges to MATLAB for the problems that I think are worth solving. Python has the horsepower and the wheels to solve those problems. I applied the 3 Ds of skill practice to my quest to learn Python.

One side note about study materials. I started with free high quality resources (e.g., Google’s Python Class, Learn Python The Hardway, and Think Python) but I wanted to be more than an Advanced Beginner. I was pleased with the in-depth coverage of Learning Python and Programming Python by the fine people at O’Reilly.

Deliberate

I always had clear, concrete goals to improve my ability. At first, it was working through specific chapters or sections. This worked well for the initial introduction to the language. Later in order to find objective benchmarks, I had to rely on testing.

Testing is critical, but often times lacking in self study. It highlights which material has been mastered and which material need more attention. This learning recalibration is needed to stay in the zone of proximal development.

The best method for testing programming skills are coding interviews, and the best coding interviews can be found at the req.

Deep

My success was correlated with the depth of my studying. At a coarse level that meant no TV, IM, or Twitter. At a more subtle level, I made sure nothing was on mind other than the current learning. A trusted system was helpful.

Distributed

This classic behavioral psychology trope works every time. The same amount of practice spread out will increase retention. I would review previous material before engaging with new material. Anki is my preferred method for stacking and tracking my learning corpus.

The art of misdirection from ted.com

The above video is an engaging and intuitive introduction to attention. People are often seduced by consciousness, their own "Frank." Because the mind is the most dominant (and most frequent companion), people think that it is the most important mental process. Our brain has many mental processes, those other processes are quieter or completely silent. I am interesting in brain research, especially neural decoding, because it provides a window into those processes.

The direction and magnitude of connectomics

The connectome meme is currently the best paradigm for understanding the brain. Identifying the connections in the brain is a prelude to a far more powerful story - the direction and strength of those connections. Similar to betas in an regression analysis, identification is an important first step but the direction and magnitude of those weights is where the greatest promise lies.

The environments of innovation and productivity

It is difficult to require innovation or productivity, but they can be encouraged with the right environment. However the right environment, both physical and mental, for each one is distinctly different.

Innovation is finding a new way to "crank the widgets." It happens with happy accidents. Asking a random (seemly innocent question). Interacting with people. Realizing the link between two distant concepts. This is "heads up" work.

Productivity is "head's down" work. Humming along, mostly quiet. Clear crisp boundaries. "Cranking the widgets" is best done without interruption.

It is up to you decide what your job (at the current moment) requires. Is it being innovative or being productive? Then craft the environment that maximizes that potential. Environments, both physical and mental, are all too frequently accidents of time and space reflecting the past rather than the present.

The brave new world of molecular gastronomy

Molecular gastronomy, the science of cooking, is making exciting breakthroughs. One future direction could be combining cooking with genotyping. It is becoming increasingly cheap and easy to find people's genetic structure. Given the strong role genes play in taste and smell, I wouldn't be surprised if high-end restaurants start genotyping individual people to customize meals.

This idea was inspired by Cory Doctrow's Makers.