Data Science and R: how do I start?

Jesse Maegan
Jan 4, 2018 · 6 min read

It always starts with a DM on Twitter, where someone shares with me their personal data science ambitions, where they currently are in their plans, and then they follow up with a request for me to help them figure out where to go next.

I love these messages — they’re an affirmation that the R community continues to grow and attract new members in part by creating a welcoming and supportive space for beginners, and that our community members are deemed approachable (enough) for someone brand new to R to reach out!

These messages used to be infrequent enough that I would spend time writing a tailored response to each individual, but over time the frequency has increased to a point where I can’t respond with as much thought and attention to detail as I would like. Rather than offer up a generic response, I’ve created this post for you, the data science beginner that wants to learn R!

we’re so glad you’re here!

But what about [resource]?!

You’ll also notice that this list leaves out things like deep dives into Machine Learning and Artificial Intelligence, and that’s intentional. This list is aimed at someone literally starting out with data science and R — complex topics in ML/AI will be there for them later on in their learning path.

there is no shame when it comes to awful puns

If your math skills need some love

Linear Algebra and Calculus — videos

Statistics — videos

Statistics — books

me, trying to figure out how to split the check 6.2 ways

If you’re new to R

R — books

R — other awesome stuff

I aspire to this kitten’s level of greatness

If you’d like to explore Computer Science offerings

what I imagine an MIT capstone project involves

On learning to learn

From open house to home ownership

The first time you encounter a piece of information is like going to an open house. You don’t know if you’re going to rent that house, buy that house, let alone if you’re even going to like that house.

The next few times you encounter that same information is like renting a house — you’re committed for a relatively short amount of time, but you’re not necessarily in it for the long haul. You don’t own it, and you can’t really make any changes.

Once you’ve really learned something, you’ve bought the house. It’s yours. You can knock down walls and landscape the yard and you don’t have to ask permission to do so, because you own the house.

When you first encounter something in data science and/or R, it’s 100% OK to forget it 20 minutes later. At first blush, you don’t know if you’ll ever need this information again! So why go through an online course or a textbook once, expecting to extract all of the information?

Instead, try getting comfortable with the fact that you’re going to forget a lot of things when you first start out — but the more that you read and re-read and learn and practice and apply what you’ve been learning, the more you’re going to remember.

Let go of perfection

So what if you don’t document every moment of your learning process on a personal blog that you created using blogdown? So what if your notes are scattered in a couple of different notebooks and half-used GitHub repos? Who cares if you even take notes?!

Commit to yourself every day and show up and do the work — if a course or book or video isn’t working for you, try something else! This is your journey — you get to decide how you get there.

My daily learning habits:

  • Read through the tidyverse section of the RStudio community site once a day (I’m personally working on getting better at the tidyverse, but feel free to substitute in any tag that is of interest to you!)
  • Spend two hours a day working on content knowledge such as statistics, linear algebra, calculus, or computer science
  • Code for at least 30 minutes a day in addition to what I do for work
  • Engage with the R for Data Science Online Learning Community once a day
you’ve got to practice! work on cultivating discipline instead of waiting for motivation.

Questions that I can’t answer for you

  • What degree should I get?
  • Should I do a data science bootcamp?
  • Should I drop out of school?
  • Is an advanced degree worth it?
  • Should I change my major?
this will be my new response GIF when you ask me any of the above questions

What now?

Share your success story!

Blog about your experience

you’ve got this!

Jesse Maegan

Written by

molecular biologist turned public school teacher before falling in ❤️ with non-profit data science. perpetual #rstats noob.