The important thing to remember is that you need to love how you get to your destination, the journey, the process, not

You are proficient in Python, R, and SQL. You have developed intuition to analyze almost any data. And you know how to apply ML models. Now is the time to take your skills to the next level. The last most crucial thing you will need to learn is the art of putting together the data pipeline, integrating with cloud services like AWS, Azure, IBM Cloud, Hadoop, Spark, to name a few, and pushing it into production. Again, there are loads of resources online. You just have to look them up.

I rubbed my hands. “Because talking is nothing but thinking out loud, to converse with another is to essentially receive access to the speaker’s mind. Hence the word ‘interview’ breaks down to — enter view.”

In short, to think like Bezos — in this respect — merely calls for, as he puts it: “Surrounding yourself with people who challenge you, teach you, and push you to be your best.'

There are loads of ML algorithms but you don’t “gotta learn them all” (*insert Pokemon theme song in the background*). You should figure out which ML techniques are more useful to your domain knowledge. Again, YouTube can be a good starting point as it has been so far. But this time you will definitely need to rely more on other resources like module documentations, or niche blogs like on Medium. By now you should probably be a God of looking things up online.

Given that Christophe is well familiar with my Pure Mathematics Code, which has struck success among academics and delves into pure logic, he didn’t take my suggestion lightly. “I’m all ears, Genius! Give me some examples.”

Because Experience is the hard teacher that gives tests before study guides, wisdom dictates the older a person becomes, the more resourceful. Diller is old enough to be Bezos’ father. Perhaps Bezos embraces the insight once expressed by Mike Tyson’s legendary trainer, Cus D’Amato:

This is purely based on my own experience and gross generalization. In most cases though, it’s not really as linear and you will bounce between the different chapters, especially chapters 3 and 4. The experience will definitely be different for you in some way or the other. There are other methods to ace each of these stages which are as effective as these, if not more. Some of you may focus on data visualization, some may specialize in Machine Learning. But I guess there is an element of commonality between them; they all fall under the umbrella of Data Science.

Apparently, Bezos relished the opportunity to rub shoulders with the media executive responsible for “The Killer Dillers” — the star-studded list of people, mentored by Diller, who later became hugely successful media executives.

What more is wisdom than, well, hearing a cobra is poisonous without having to lose your life for proof? Bezos, then, makes a concerted effort to surround himself with wiser, more mature minds. Sure, hanging with people you’re smarter than boosts your ego, but hanging with people smarter than you boosts your intellect.

The most crucial part is maintaining momentum and learning or practicing regularly. But the most difficult part is sitting down with your computer to start working. Once you successfully convince yourself to start for the day though, it does get easier every time. At the end of the day, if you don’t get those bursts of your “feel-good hormones” when you convert the little bits of data into tangible knowledge, is it really something you wanna do for the long term?

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One answer is capitalism. But that is just political economy. What about at the level of ideas, beliefs, philosophy, culture? When we look at America that way, a curious and weird truth emerges — which instantly explains why Americans don’t treat each other like human beings. There’s an idea which changed the world — but America never learned.

There are very few new things in this world, very few. That’s why people that are young, if they’re smart, try to profit from the experience of an older guy so they won’t have to go through all the pain and suffering.

The chief principle on which science rests — the law of causality — can best be summed as follows: like causes always produce like effects. No wonder, then, Bill Gates loves playing Bridge with his pal Warren Buffett, as does Bezos delight in attending parties with his pal Barry Diller.

However, this is where your domain expertise will dictate most of what you do. You have mastered the tool that Data Science is but what you want to do with it and how you apply it in your own domain is why you will be hired. You may want to explore Natural Language Processing to analyze big genome data or carry out sentiment analysis for a chatbot to automate customer service for a company. You may want to learn the ins and outs of how Convolutional Neural Networks work to detect objects through computer vision. Or you may just want to analyze marketing and customer behavior data to help create better-informed strategies for brand growth or profit.

The important thing to remember is that you need to love how you get to your destination, the journey, the process, not just the destination itself. Play around with every new concept you learn. Tweak things here and there. Let your curiosity flow.