Programming for Data Science: How to Learn Just Enough in Weeks
I wished for more but a few weeks is all I got. Here’s how I did it.
This was the mail I’d been waiting for. It was a familiar name in my inbox, and I was over the moon. This was what I’d been working hard for. I opened the mail... and I stared.
Earlier that day, I was handed in the project for my first data science internship.
“Arunn, here’s the project on hotel room pricing you’d be working on. Please have a look at the problem statement document and I’ve mailed some starter codes. Take the rest of the day off, and let me know if you’ve got any questions.”
“Sure, I’ll get started right away.”
I opened the mail. The extension for the starter code ended with an unfamiliar single letter. What?
I opened the code script to be sure anyway.
It slowly sank in. But all these YouTubers told me Python is the most popular programming language. Why R now?
I wish I could rip apart those YouTubers. I’ve already spent a year with Python, and all I have now is weeks?
In this article, I will give you just enough resources, answer your pressing questions, and show how you can learn just enough programming for data science in a matter of weeks.
The Best Language for Data Science?
Maybe if I had options like every other beginner, I’d have argued about Python vs. R too. I didn’t have that luxury. You’re bound to realize this truth anyway, and I’ll save you some time.
“It doesn’t matter which language you pick, you’ll end up learning both.”
Okay, I get it. You still want to know which one to learn first.
If you want to know your first language for data science, I want to know about you first.
Are you coming from an academic/statistical/mathematical/economics background?
If you answered yes, the R programming language is best for you. If not, you pick Python. That’s my rule of thumb. There’s nothing more to compare between languages.
I understand your concern: You want to make the right decision upfront and not regret it later in the journey. I was like you too until I had no option. Prepare yourself to embrace all the languages — data science is beyond the tools.
Picking a language is easy. You need much more help in learning to code. What have we got?
Python — Your Everyday Language
Python is a general-purpose programming language, meaning it can be used for multiple domains beyond data science.
Most coders start with Python since it’s comparatively easier to pick. I’d recommend starting with a course to learn the basics such as variables, loops, functions, logical operators, and basic data structures.
Let’s look at some useful resources?
- Python for Everybody Specialization by the University of Michigan: I started with this, and it’s the most comprehensive course I’ve seen on Python. You’ll know what I’m talking about when you look at the reviews. Start with this.
- Python for Data Science by freeCodeCamp: This free course outlines everything you need to know for data science, including NumPy, Pandas, and more.
- Python for Data Scientist by DataCamp: I used this to solidify my python skills with hands-on exercises. This can be your final touch.
Don’t fall into the trap of continuously learning from more and more courses; you can’t afford to do that. Remember you’ve got a few weeks only!
R — It’s Easier Than You Think
R’s got a steep learning curve, but when you’ve got the hang of it, it’s quite simple. The developers have kept a non-programming audience in mind when building this language.
Let’s keep in mind that we have only a few weeks and quickly look at some resources to get started?
- Data Science Foundations with R by Johns Hopkins University: Hands down, this is the best course you’ll ever find. This was one of the first few courses that were released on Coursera and are still as good. I took up this course and finished it within a week or so. I’d recommend going through the practice exercises they provide.
- Data Scientist with R by DataCamp: They have listed down courses in an organized way, which you can use to slowly skill-up in R. I used this after the previous one. Still, the best thing about these courses is the hands-on exercises — it’s the best way to learn (more on this later).
That’s all; this is more than enough to get the hang of the language. Let’s move on to the “gem”?
SQL — The Forgotten Gem
People battle between Python and R only to forget the most important language for data — SQL.
I learned SQL during my computer science degree and didn’t have to spend much time on this. But if you’re coming from a different background, it’s vital to understand the data's language.
Some data science positions only test you on your SQL knowledge. If I had to learn it from scratch, I would have used the SQL for Data Analysis by Udacity, which is free of cost, comprehensive, and covers everything you’ll need.
There’s one thing more important than learning from courses. What’s that?
You Will Fail In Programming Until You Do This
If you thought by watching the above-recommended courses, you’d become a programmer; you’re awaiting some disappointment.
You need to practice coding.
I can’t stress about it enough. This is the number one mistake most beginners make when learning to program. In the words of Abhishek Thakur, the world first 4x Kaggle Grandmaster:
“If you don’t code, you don’t learn.”
This is How You Learn Programming in Weeks
You pick a language. You learn through online courses. You go on coding sites and attempt to solve problems. You fail. You feel defeated.
You badly want to learn anyway.
You look at the solutions. You attempt the next. You take up 30-day coding challenges. You become consistent. You feel comfortable with the language.
You get an internship. You’re excited and feel victorious. You receive a mail. You get a project on a different programming language. You stare. You buckle up and do it all over again.
You’ve done it before. You’ll now do it over and over again.
You’re a champ now.
As a note of disclosure, some affiliate links have been used in this article to share the best resources I’ve used and at no extra cost to you.
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