Secret Fun Tech People

If you are trying to pick a career, it would help to know what the daily experience is like in various professions.

A friend of mine recently quit her old job and did a coding bootcamp. She worked hard, went through interviews and is now working in tech. She was correct in expecting that coding is more interesting and provides more opportunity than her old job.

She is not at a FAANG or grinding at a startup. She got hired in a remote position that requires an understanding of code. She’s starting at the bottom of the hierarchy in her 30’s, as someone with no experience.

Now that she has started work in the industry, she reported to me that, “I don’t think I could have predicted that the people would be this much fun.”

She is genuinely enjoying tech culture. She texts me obscure tech jokes now as if it’s an SNL skit that I would enjoy. (e.g. https://www.youtube.com/watch?v=kHW58D-_O64 somewhat obscure YouTube channel) Her previous job was boring, and she never told me a positive thing about it. She is happy, not just with her financial return on investment but with her community.

If you read much about tech policy, you have heard about harassment in the workplace, especially for women. This is indeed an important issue. I’m not presenting my anecdote to imply that everything is fine everywhere. If people are trying to make important life decisions, then this is worth discussing.

One factor that might make people not want to learn to code is that they are afraid the work would be isolating and boring. It can be, but there is also a community aspect that can be positive.

I polled my Twitter friends and got this result (small, biased sample, albeit, and I suspect it’s mostly men who answered):

No one disputed that tech folk can be fun, although some people wanted to qualify the statement by saying that different companies have different cultures.

John Vandevier (@JohnVandivier) sent me a blog he wrote about a study on tech culture. “Analyzing ‘Resetting Tech Culture’ by Accenture and Girls Who Code” The study shows that the world is complex. Lots of women are happy in tech. At the same time there are people who face harassment. There is good news and bad news. Offenders should stop offending. There are also good opportunities out there for people who train for tech.

When I shared the story about my friend’s good news, it was mostly ignored on Twitter. Good news does not drive engagement. Happy people are not interesting and so no one hears about them. Tech is not the right choice for everyone, and some people have been mistreated at tech companies, but on the margin a few more people should probably go for it.

Here’s something to balance out my rosy report about all the laughing and LOLing among coders. Last year I had a miserable long day of coding. I wrote up a diary entry about how much I hated that day. I’m not trying to get sympathy for myself. I wanted to capture a modern experience that is shared by many.

Coding can be hard and frustrating and lonely. The jokes are funny because the pain is real.

Book Review: Big Data Demystified

Last year, our economics department launched a data analytics minor program. The first class is a simple 2 credit course called Foundations of Data Analytics. Originally, the idea was that liberal arts majors would take it and that this class would be a soft, non-technical intro of terminology and history.

However, it turned out that liberal arts majors didn’t take the class and that the most popular feedback was that the class lacked technical challenge. I’m prepping to teach the class and it will have two components. A Python training component where students simply learn Python. We won’t do super complicated things, but they will use Python extensively in future classes. The 2nd component is still in the vein of the old version of the course.

I’ll have the students read and discuss “Big Data Demystified” by David Stephenson. He spends 12 brief chapters introducing the reader to the importance of modern big data management, analytics, and how it fits into an organization’s key performance indicators. It reads like it’s for business majors, but any type of medium-to-large organization would find it useful.

Davidson starts with some flashy stories that illustrate the potential of data-driven business strategies. For example, Target corporation used predictive analytics to advertise baby and pregnancy products to mothers who didn’t even know that they were pregnant yet. He wets the appetite of the reader by noting that the supercomputers that could play Chess or Go relied on fundamentally different technologies.

The first several chapters of the book excite the reader with thoughts of unexploited potentialities. This is what I want to impress upon the students. I want them to know the difference between artificial intelligence (AI) and machine learning (ML). I want them to recognize which tool is better for the challenges that they might face and to see clear applications (and limitations).

AI uses brute force, iterating through possible next steps. There are multiple online tic-tac-toe AI that keep track records. If a student can play the optimal set of strategies 8 games in a row, then they can get the general idea behind testing a large variety of statistical models and explanatory variables, then choosing the best.

But ML is responsive to new data, according to what worked best on previous training data. There are multiple YouTubers out there who have used ML to beat Super Mario Brothers. Programmers identify an objective function and the ML program is off to the races. It tries a few things on a level, and then uses the training rounds to perform quite well on new levels that it has never encountered before.

There are a couple of chapters in the middle of the book that didn’t appeal to me. They discuss the question of how big data should inform a firm’s strategy and how data projects should be implemented. These chapters read like they are written for MBAs or for management. They were boring for me. But that’s ok, given that Stephenson is trying to appeal to a broad audience.

The final chapters are great. They describe the limitations of big data endeavors. Big data is not a panacea and projects can fail for a variety of what are very human reasons.

Stephenson emphasizes the importance of transaction costs (though he doesn’t say it that way). Medium sized companies should outsource to experts who can achieve (or fail) quickly such that big capital investments or labor costs can be avoided. Or, if internals will be hired instead, he discusses the trade-offs between using open source software, getting locked in, and reinventing the wheel. These are a great few chapters that remind the reader that data scientists and analysts are not magicians. They are people who specialize and can waste their time just as well as anyone else.

Overall, I strongly recommend this book. I kinda sorta knew what machine learning and artificial intelligence were prior to reading, but this book provides a very accessible introduction to big data environments, their possible uses, and organizational features that matter for success. Mid and upper level managers should read this book so that they can interact with these ideas prudentially. Those with a passing interest in programming should read it for greater clarity and to get a better handle on the various sub-fields. Hopefully, my students will read it and feel inspired to be on one side or the other of the manager- data analyst divide with greater confidence, understanding, and a little less hubris.