Thinking Fast
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Thinking Fast

How to be a Successful Data Scientist

My Process Isn’t Easy But It Works

Photo by Fab Lentz on Unsplash

What separates the successful entrepreneurs from the unsuccessful ones? The same thing that separates the successful data scientist from the unsuccessful one, and it’s not how smart you are.

It’s people.

Friends, audiences, networks.

Don’t believe me, check out this article from Inc.com that covers a number of studies supporting the power of a social network.

Building an audience with your data science ideas, helps both you and your audience to learn how your way of thinking brings value. It also helps you to shape how you communicate the value you can bring.

Do you have to know everything about data science to show value? Absolutely not!!! No one does.

In fact, much of where I focus my time is on determining what skills I need to learn to continue to bring value to my audience, to my clients, my stakeholders, whomever.

This leads to two things. First, I am constantly learning. Second, I am constantly aligning myself with my audience.

But knowing what to learn and when is really hard, especially when searching for the thousands of resources available online. So if your learning journey in data science is being bootstrapped by free and low cost online tools, here are a few recommendations I have for helping to successfully manage your time and expectations when it comes to further advancing your skillset and setting on the path to being a more successful data scientist.

1. Know the fundamental data science process (shameless self-promotional plug, see my book link below)

2. Once you know how the process works, prioritize your time accordingly

a. For example, maybe you need to beef up on the data engineering phase of the process. Start by tackling one blog, tutorial, or example per week to see if you can get the code to work.

3. Not every resource, free and paid, has perfectly working code. Don’t get down when things aren’t working on your end

a. These are great opportunities to go deeper in the code to learn how to debug

b. You will also learn more about how your computer works since many bugs can be explained by differences in operating systems

c. If you can’t debug to get things working, move on. There are probably a hundred other resources on the same topic you can learn from

4. Set a New Year’s resolution and let if direct your learning for the year

a. Every year I set a resolution to focus that year on one core area. For example, when I first got into data science and started doing this, my first resolution was simply Learn Python.

b. I spent each week of the year tacking a different Python problem.

c. By the end of the year, I was getting pretty good with Python. Year 2, Data Engineering. Year 3, Traditional Modeling. Year 4, Deep Learning. You get the point.

5. Never stop never stopping.

But it is okay to take a break, because even when we aren’t working on a problem our brain is still working on that problem. In fact, much of what our brains figure out happens when we aren’t explicitly trying to figure things out. Thus, downtime is not only good but it is essential to allow what you have been explicitly learning to crystalize.

Like engaging to learn about data science, career growth, life, or poor business decisions? Sign up for my newsletter here and get a link to my free ebook.

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