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 data science jobs through my projects, how to make learning data science enjoyable, a top-down approach to learning, and other advice from my journey into the data science field. You can listen to the whole episode (and all other episodes of the TDS podcast) for free on Spotify. …
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 from reading 136 books in 2019, I didn’t want to highlight hundreds of paths to high achievement. Instead, I’ve framed the lessons as updates to 12 pre-existing beliefs I, or wider society, held. These updates are not corrections because knowledge continually changes, and many of our old worldviews were logical, given the information at the time. …
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.
That being said, for me, the best books offer an ideal blend between entertainment, learning new things, and having your opinions challenged. To get my thoughts in order on the books I read in 2019, and perhaps to help you find a book, below are the 136 books (127 non-fiction and 9 fiction) I read in 2019. (The numerical ratings are here: Google…
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.
Although the move may be trying, it also has rewards: you (usually) don’t have to take work home, there are no stressful exams, and, perhaps most importantly, someone is paying you to show up and do work that’s (in most cases) easier than what you spent tens (or hundreds) of thousands of dollars to do in college. So, what’s the secret to excelling in a job immediately after college? Like most other areas of life, there’s no single answer everyone is hiding from you; instead, there are basic principles and actions that, when repeated thousands of times, can increase the chances of success. …
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.
This fixed-mindset view is tempting — if we aren’t born with talent, then we might as well not even try to be high performers — but it’s also wrong. The beliefs of the first group of people — those with a growth mindset (the idea abilities can be developed) —are backed many studies of top achievement in many professions. As described in the paper “The Mundanity of Excellence” by Daniel Chambliss, when we objectively study excellence we…
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 value and adding them together to get the overall percentage change. …
How to Lie With Statistics is a 65-year-old book that can be read in an hour and will teach you more practical information you can use every day than any book on “big data” or “deep learning.” For all promised by machine learning and petabyte-scale data, the most effective techniques in data science are still small tables, graphs, or even a single number that summarize a situation and help us — or our bosses — make a decision informed by data.
Time and again, I’ve seen thousands of work hours on complex algorithms summarized in a single number. Ultimately, that’s how the biggest decisions are made: with a few pieces of data a human can process. This is why lessons from “How to Lie with Statistics” (by Darell Huff) are relevant even though each of us probably generates more data in a single day than existed in the entire world at the writing of the book. As producers of tables and graphs, we need to effectively present valid summaries. As consumers of information, we need to spot misleading/exaggerated statistics which manipulate us to take action that benefits someone else at our expense. …