Imminent Murphy

In which I discover DataCamp, start learning Python, hit the Wall, and remind myself that learning new things can be hard

“Sucking at something is the first step to being sorta good at something.” ― Jake the Dog (Adventure Time)

I.

Once upon a slow weekend, the word “DataCamp” abruptly leapt back into the front of my brain.

I had first heard it muttered by a group of data analysts who were keen on employing it to hone their SQL expertise.

Me? I wanted to learn more about how machine learning really works. Curiosity abruptly piqued and suddenly flush with noradrenaline, I decided to check it out for myself.

Navigating through the newly downloaded DataCamp app on my phone, I was greeted with the choice of a learning pathway. I opted for the ‘Data Scientist with Python' track which promised (paraphrasing here)

“We can definitely teach you all about machine learning. There’s just one thing. We need to teach you… 20-ish other things first."

And so I embarked on the first ‘Introduction to Python' course and - after completing the very first lesson right there on my phone - the app said (and again paraphrasing here):

“Nicely done! But - sadly - that’s all you get right now. Come back tomorrow and we’ll give you a little more.”

I came back tomorrow.

In fact, I came back each day for an uninterrupted week in order to get that tiny dose of dopamine-laced learning. And if I could complete 7 days worth of lessons in a row - why, what was stopping me from doing it for 358 more days?

I purchased an annual subscription which granted unlimited access to the entire library of coursework and - most importantly - enabled me to complete more than one lesson per day.

If I hadn’t need to print an invoice for reimbursement, I’m not sure how long it might have taken me to figure out that there was a DataCamp.com website too. Discovering that an invoice wasn’t possible through the app, I browsed out to the website and was delighted - and almost immediately overwhelmed - by the myriad amount of additional educational content available to me:

• standalone learning projects (both guided and unguided)
• weekly and monthly coding challenges for students
• a blog
• a certification process
• and a data science career/job-hunting section.

It’s all impressive stuff and I can’t help but think some of it should probably have been at least hinted at somewhere within the app.

I quickly finished the Intermediate Python class and tried my first guided project which concerned analyzing Netflix data. This proved to be quite the struggle. I found myself doing lots of googling and experimenting while trying to figure out how to do all things that I had ostensibly learned previously.

This first project was a real application of the content from the first two modules. I came out of it feeling both very accomplished and quite humbled.

II.

I started working on the “Data Manipulation with Pandas” course - and that’s when I slammed face first into the Wall.

The lesson videos started showing increasingly complex Python commands and data analysis concepts, in nearly linear relationship to how it was increasingly difficult to keep it all straight in my head.

And this is when the ever-present Voice of Resistance started its seductive whispers: “You are discouraged because perhaps this is all just too complex for you to handle - at the moment. You’ll be able to focus more later when you have more time. Just drop it for now…”

I took a brief break and I realized that I hadn’t bothered to jot down even a single note during a lesson to help organize my thinking or jog my memory about the concepts. I was still essentially winging it every time I sat down.

And so I discovered what Google Collab is and immediately started taking notes in it. (NOTE: DataCamp also introduced a built-in notebook feature, in-line with the rest of the learning experience. Like I mentioned earlier: you really don’t need to install or configure anything else to use this.)

I adjusted my thinking:

“I am discouraged because I’ve been presented an entirely new concept for the first time in a five minute long video which I have watched only once… without taking notes… and I haven’t instantly mastered it. Is this realistic? It is not.”

I would remind myself of this, re-adjusting my thinking over and over again, while completing the rest of the learning track.

It took approximately 90 hours, 25 courses, and 1308 exercises from start to finish.

TL;DR

Your time is precious and DataCamp is an effective, highly affordable educational platform for learning data science. From start to finish, I went from filthy Python casual to actually tuning machine learning model hyperparameters.

It’s really remarkable. Highly recommended.

DataCamp summary

• All learning activities are delivered via a web browser or app. No additional software needs to be purchased, installed, or configured so there’s little friction to start learning.

• Each topic is divided into a series of lessons and exercises.

• Individual lessons are delivered via instructor-led videos, all of which were clear and concise (not lasting more than 6 minutes).

• Several interactive exercises are paired with each lesson and serve to reinforce the new concepts. All of these were either writing entirely new lines of original code or filling in the blanks in different python samples. And if you should stumble on an exercise, several hints are available - as well as the final answer. This approach gently moves the student from a passive learner to an active practitioner.

• Learning tracks are a series of progressively more complicated topics, interspersed with projects. These standalone projects provide opportunities to directly apply lessons using examples that mirror real-world scenarios. If parts of the project are difficult for you, there are links directly back into previous lessons for more practice.

• When you successfully complete all the work within a Learning Track, you have the option of taking additional tests to achieve a certification. This is a useful credential for showcasing your expertise to both peers and employers (which I ultimately decided not to pursue).