In October 2020, I was interviewed by DrivenData, an organization that hosts data science competitions for good, one of which I placed second in while teaching myself data science. My interview appeared condensed and edited on DrivenData’s blog and here are my full, unedited answers.
Even if you don’t make it through this article — there’s a reason my responses were condensed— I’d recommend checking out DrivenData’s challenges to get experience with realistic data science problems that try to benefit society. …
Talking is a lot like writing in that it forces you to formulate vague ideas into understandable, concrete concepts. Often, we think we know an idea, but we don’t truly comprehend the theory unless we can communicate it clearly in words to someone else. That forced understanding through communication is partly what drove me to write about data science in the first place, and what drove me to speak recently on the Towards Data Science podcast.
In the episode, we (me and the host YK from CS Dojo) talk about how I self-taught myself data science, how I earned two…
Reading 136 books in a year does not get you to enlightenment. It may lead in the other direction, towards greater confusion. How? In those 55,000 pages, you are bound to find inconsistencies, such as pieces of advice that directly contradict each other. You are also guaranteed to read about people who were successful using one strategy and people who were successful using the opposite approach. What you find over 136 books is not one path to success, but many, as life is so varied, there is no single master strategy.
Therefore, when I thought about what I had learned…
Before we get started: reading books does not make you a better person, and it doesn’t necessarily make you smarter. Reading is just a form of entertainment, and a reading habit isn’t superior to a movie-watching or exercising habit. I choose to spend my free time reading, but I don’t judge people who choose otherwise. So, this article in no way implies “you should read more books”; I think you should do whatever you want in your extra hours as long as it doesn’t hurt others and you enjoy the experience.
Rule number one for achieving goals: don’t take advice from athletic apparel company slogans. “I am what I am,” “impossible is nothing,” and of course, “just do it” may be effective at selling sporting goods, but they contradict proven methods for reaching your objectives. By suggesting that an individual, through sheer willpower, can achieve unfathomable success, these slogans promote ideas opposite to effective goal attainment strategies:
After 16–20 years of schooling designed to produce efficient, docile factory workers, students are released into the “real” world, where none of the rules they’ve learned apply. Working with others is now called collaboration instead of cheating, there are no simple right/wrong answers, and, instead of being told to sit down and shut up, you’re expected to make contributions. It’s no wonder the transition from college to the working world is challenging.
Over the past year, I’ve gone from the simple world of writing Jupyter Notebooks to developing machine learning pipelines that deliver real-time recommendations to building engineers around the clock. While I have room for improvement (I still make plenty of coding and data science mistakes), I’ve managed to learn a few things about data science that we’ll go through in this article. Hopefully, with the lessons below, you’ll avoid many of the errors I made learning to operate on the day-to-day data science frontlines.
There are two groups of people in the world, those who see the following as an opportunity and those who find it absolutely terrifying:
Innate talent is not the cause of success in any field.
The first group of people read this and say “great, that means there’s nothing stopping me from being successful.” The second group of people say “Uh-oh, that means I can’t use talent as my excuse for not being successful.” People want to believe inherent ability determines success because it absolves them of responsibility for their own low level of performance.
Incredibly, after 16 years of schooling, the majority of American college students get this question wrong:
What is the total percentage change in the following situation?
Decrease of 40% followed by an increase of 60%.
A. Increase of 10%
B. Increase of 20%
C. Decrease of 4%
D. None of the above.
The answer, of course, is C, an overall decrease of 4%. Not only did the majority of college students get this question wrong, they did not even get the correct direction, with over half guessing this was an increase. The common error is taking the percentages at face…
Data Scientist at Cortex Intel, Data Science Communicator