12 things I wish I’d known before starting as a Data Scientist

The answers to these questions can change over time, so it’s important to check in regularly.

I’ve seen a lot of data scientists march down a path for too long simply because of inertia.

What to do as a student to become a Data ScientistTake relevant classes — not just technical classesOf course, statistics and computer science classes will be helpful on the job.

However, lots of classes can be helpful.

Anything that gets you practice thinking critically and making written arguments, such as philosophy, history, or English, can be useful, since that’s a lot of what you do in data science.

Social science subjects such as economics or quantitative psychology can be great for gaining experience making causal inferences.

A class I think back to often is the persuasive speaking class I took, which I invoke regularly at my job.

Take your fair share of technical classes, but learn broadly and follow your interests.

My strategy was always to go with great professors over great syllabi.

I’d still recommend that to any college student, data science or not.

Practice communication — written, visual, and verbalCommunication skills are wildly important and chronically undervalued in data science.

Your impact can only be as good as your communication skills since you need to persuade others to make decisions or help build products based on your analyses.

Thus, a lot of very technical data scientists’ careers are implicitly limited because they can’t write or speak clearly.

Practice — in all three forms, written, visual and verbal — makes a real difference.

Take classes with lots of writing, especially if you feel you’re a weak writer or English isn’t your first language.

A lot of campuses have writing centers to help you get feedback.

That’s a resource to take advantage of while you have it.

Work on real data problemsKaggle is great for learning about modeling.

However, with Kaggle, the hardest part has already been done for you: collecting, cleaning, and defining the problem to be solved with that data.

The best way to prepare for a job as a data scientist is to use real data to answer real questions.

The reason is simple: it’s the closest you can get to an actual job without actually having one.

Find something you’re interested in and get your own data.

Scraping data off the Internet is much easier than most beginners realize with packages like BeautifulSoup, Scrapy, and rvest.

Wikipedia and Reddit are good targets if you need inspiration, but the best choice is something that you’re genuinely excited about exploring.

Then, ask some questions that interest you and see how well you can answer them.

Clean the data, make some graphs and models, and then write up your conclusions somewhere public.

It’ll be slow going in the beginning, but that’s because you’re learning.

If you can, try to solve actual real-world problems for people in your community, such as doing statistics work for a school sports team or doing polling analysis for the school newspaper, in order to get practice with stakeholder management as well.

Publish your work and get feedback however you canThe only way to get better at anything is to get feedback.

Data work is no exception.

These days, it’s so easy to post notebooks to Github or personal websites.

If you write about a topic your friends are interested in, you can learn a lot from how they respond.

What was compelling about your presentation?.What was unclear?.Were you able to persuade them of your main argument?.Did they get bored reading and not make it to the end?.Crucially, make your code available, and try to get code reviews from other students so you can make one another better.

If you use a technique from a class you’re taking, you could even show a professor what you’ve done and get some expert feedback while showing some initiative.

And, who knows, if one of your analyses goes viral on the Internet, you may even get a job out of it!Go to events — hackathons, conferences, meetupsTo the extent that your geography and budget allow it, try to interact with the outside data science world while you’re a student.

Doing so will give you a better understanding of the realities of the field and give you a head start for networking.

There are data science meetups and hackathons in most major cities, and in my experience, most people are very friendly to students at them.

Conferences usually have dramatically discounted tickets for students.

Going with friends can make for a fun field trip together, too!Be flexible with how you enter the fieldData science is a competitive field.

There are a limited number of tech companies with great data science brands, and the battle for their summer internships and entry-level roles is fierce.

However, once you have even a small amount of real data science work experience, it’s much easier to get a second job in the field.

Data scientists with a few years under their belts, even from little-known companies, often have little trouble getting hired at top companies.

Thus, if you want to be a data scientist, and you don’t get an offer right off the bat from one of the famous companies, consider broadening your job search.

There are lots of companies with interesting problems to solve.

Thanks for reading!.I’d love to hear your thoughts — per the collecting feedback bullet above! — so feel free to leave a comment below.


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