Getting Started in Data Science-Part 2

I'm finally getting around to writing Part 2 of Getting Started in Data Science. The first part can be found here. I made suggestions for university students interested in the field of Data Science. I even made a video about it too. 

Pick Two, Master One

Pick two computer languages and become proficient in one and a master at the other one. Or, pick a platform like H2O-Flow or RapidMiner and a language. Become a master at one but proficient in the other. This way you can set yourself apart from other students or applicants. 

The reality is that you will be flipping back and forth between languages in your day-to-day work life. You could be writing a Python script to connect a database. Then pull in some data and then a D3js wrapper to make a dashboard. It all depends on where you end up.

Social Equity

I spoke about this in my video, you should get involved socially. Join meetups, go to conferences, and then contribute. Did you do a cool project or solve an interesting problem? Ask to speak about it at a meetup. Public speaking does two things for you: it builds your brand, and it helps you get over the fear of speaking. 

I used to 'pooh pooh' people with communication skills. I used to think all they do is talk and produce nothing. Boy was I wrong. Communicating is as important as solving whatever problem you're working on. 

Another way is to join a club or meetup. This is a great low-stress way to get out and listen to some interesting speakers in the field. There are tons of meetups happening all the time and all you need to do is go to meetup.com and do a search in your area.

If you saw someone give an interesting talk at a meetup, go up to him or her and tell them you enjoyed their talk. Then ask for a business card or ask if there are any opportunities at their company.  Don't be an annoying nudge and email them every day asking about opportunities. Check-in with them every quarter by sending a nice email with an interesting article you read. 

Create something

The next way is to create something. In my last article, I wrote about about how the Makers have a drive to create. As we say at H2O.ai, Makers Gonna Make. So Make something!

Write a new library for Python or R. Create new RapidMiner processes. Then share them with the world. Share them on Github, share them on a blog, or share them on Medium. Doesn't matter but design/build/code something and release it into the wild.  Then cultivate its growth. 

Become that guy or gal whose software is being used at Google (but can't get a job there. sheesh!)

Google: 90% of our engineers use the software you wrote (Homebrew), but you can’t invert a binary tree on a whiteboard so fuck off.

— Max Howell (@mxcl)

June 10, 2015

Make and then Share!

Start a Business

This idea is the hardest but the most rewarding. Become an entrepreneur by starting a business. It doesn't have to be big, look at what Ugly from Uglychart is doing. He's domain flipping and making $125,000 per month. The best part? He's the only employee and doesn't want to get big.

Or, you could be like the founders of RapidMiner. Build a Data Science platform back in 2007, then build build a Startup around it! The founders of Instagram designed an app and photo platform for the iPhone and sold it to Facebook. Of course, they left Facebook but I'm sure they're going to be sought after by Venture Capitalists. 

The hard part with this suggestion is figuring out what kind of business to start. Are you going to be a consultant or are you going to build a product? Then how are you going to sell it (beware the Fremium Devil). 

In the end, it doesn't matter which route you choose. The most important aspect is to remain involved with a Data Science community. Read up on the latest advances, write code, build things, talk to people, and build your personal brand.