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How To Be A Data Scientist
Interested in a career in data science? Because data science is so new, we asked a top data scientist for insight into the field. Jake Porway is a data scientist at The New York Times and the founder of DataKind (originally known as Data Without Borders), which matches nonprofits in need of data science with freelance and pro-bono data scientists. Norway has a computer science background and a Ph.D. in statistics from UCLA. Here's what he had to say about how to get into data science, how to perform well, and how to avoid key mistakes in the field.
1. Get the Right Skills
According to Porway, getting into the field boils down to three key things:
• Practical computing skills
• Statistical skills
• A desire to learn
"You need to be able to write scripts to scrape data, as well as code up the algorithms you, come up with in your head," Porway says. "You should know your basic stats (and more, ideally) if you're going to really be able to assess whether the models you're building or algorithms you're writing are doing what you want."
2. Make Connections
Before joining The New York Times R&D lab, Porway worked in machine learning and computer vision and spent a lot of time getting robots to identify landmines and fly planes (how cool is that?). It wasn't until he landed his job at The New York Times that he got to expand into broader data science tasks, namely Project Cascade, which tracks links from the publication across social media.
The most important thing to get in the field, Porway says, is to get learning.
"Get on a data science project!" Porway says. "Download some data, pick up some R[a language and environment for statistical computing and graphics], and start playing ... I'd say to focus on using something like R alongside a basic stats book to guide you through exploring some data. The machine learning and computing skills will come with that (of course this depends on your past experience – if you're already a statistician, pick up some Python!)"
Then it's time to make some connections. Porway recommends a local meetup group – because being part of the data science community is "the fastest way to know what you don't know." And in a field that's constantly evolving, that matters.
3. Get In the Game
Porway has a Ph.D. in statistics from UCLA, but he stresses that you don't need one to do good work.
"It might help, but don't think you have to go off and do another five years of school to be able to call yourself a 'data scientist,'" Porway said.
Data science is a relatively new field. This means that those who want to get into the field need to approach it with an open mind.
"A data scientist at Foursquare is going to look a lot different from a data scientist at Goldman Sachs," Porway says.
4. Rock Your New Role
Data science is all about clarifying goals, examining assumptions, evaluating evidence and assessing conclusions. But there's one little piece of the puzzle many people overlook. Can you guess what it is? According to Porway, the secret ingredient is critical thinking.
"It really sets apart the hackers from the true scientists, for me," Porway says. "You'd be amazed at how many times I've seen someone build a model and report the results without realizing that they hadn't thought critically about where the data was coming from or if their experiment was designed correctly. You must must MUST be able to question every step of your process and every number that you come up with."
Data Scientists: Tech's Rock Stars?
So why are data scientists being referred to as the new rock stars of the technology world? This analogy actually goes deeper than data nerds' desire to sound ultra cool. Just like a rock star, a data scientist's career includes diversity, artistic freedom, and adaptability. And like the rock stars of the entertainment world, the best data scientists tend to gain quite a following of people from all walks of the data and technology industry.
What a data scientist does is very diverse; just as musicians use different instruments, tools and techniques to play musical styles that are as disparate as jazz and death metal, a data scientist also masters a particular tool and field. There's style involved, too. And there is no right or wrong way of doing the job either – it’s about the impact the work has on other people.
Data Scientists Do What?
So what do data scientists do exactly? Let's go through this with an example that we all might be able to relate to.
Learning data science is not easy.
It will take a lot of work, a lot of energy and a lot of time from you.
I have seen an ad recently in my Instagram feed that said:
“Take this course and master data science in 1 month!”
And I was like: what the fudge!?
I’ve been practicing data science for 6+ years now. I’ve held senior DS positions (in addition to teaching). But I wouldn’t say that I mastered data science or analytics. I know for a fact that no one can master data science in 1 month. In fact, my personal estimation (based on students I worked with) is that from zero to the junior level the learning process will take ~6-9 months.
Learning Data Science”, it’s “improving your Data Science skills”
The world changes really fast and it won’t get any slower.
And I seriously believe that if one wants to keep up with the pace, the only way to do it is by focusing on improving skills.
You might already have heard that according to researchers’ predictions, ~65% of today’s grade schoolers will hold jobs that don’t exist yet.
You might also have heard that the current estimated “half-life” of engineering related information is ~4 years. So 50% of the things you learn today regarding IT will be outdated in ~4 years.
What does it mean for you?
That the skills you acquire and improve are way more important than the actual information you learn.
It also means that “learning data science” is not about learning data science.
improving your coding skills.
improving your business skills.
improving your mathematical/statistical skills.
improving your data visualization, presentation, communication, and other soft skills.
Learning data science is not about:
Learning a certain package of Python.
Learning the different industry benchmarks for this or that KPI.
Learning certain statistical models.
Learning how to use Google Data Studio or Tableau.
What seems important today, might be irrelevant in 5 years
Because mastering, for instance, the Scikit-learn library or Google Data Studio might seem important today… but I bet that there will be a better machine learning package and a better data visualization software in 5 years.
Don’t get me wrong, I still think that today, you should learn these things because they are part of the current data science and analytics ecosystem and also part of the learning curve itself.
I’m saying that you should keep in mind that when you learn these (or any other) tools, the important thing is not to cram in every little syntax detail or which button is wherein the specific software – but to understand the big picture. Why does this tool work the way it works? What’s the underlying logic? How does this function work in other similar tools? Once you get these, changing between tools (even between programming languages) will be easy as pie.
And you will be much more prepared for the ever-changing future.
So to future-proof your data science career: focus on your skills and not on the information you learn!
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