I hosted a livestream on my YouTube channel to celebrate 1500 subscribers and answer some questions I commonly receive. If you didn't catch it live, don't worry the video lives on YouTube and you can listen to the audio above or on Anchor.
I've put a summary of some of the questions I answered below.
Data Science Courses vs Real Life?
The main difference is cleanliness. What do I mean by cleanliness? The projects I've done through online courses have dealt with relatively clean datasets. This means not much has to be done to manipulate them in a way to be used with a machine learning or deep learning algorithm. However, this isn't the case with the brief experience I've had in the field.
The data we've played with has had to be crafted into a structure we can use. This often takes the majority of the effort and time dedicated to a project. It comes back to the adage, bad data in means bad data out, much of my time has been spent getting data into a format suitable for machines to gather insights out of.
This involves plenty of steps from dataframe manipulation in Pandas to applying filters in Excel to view the data in a different light. The methodology behind getting the data ready is different for each problem and is very difficult to directly learn in any way other than practice.
Udacity AI Nanodegree vs AI Masters Degree?
To start, the Nanodegree is called a Nanodegree for a reason. It's not to fully replace a Masters Degree but rather offer those not interested in a full-fledged Masters (like myself) to enter the field.
I highlighted some major differences in a previous post but the main one comes down to you as a learner.
How do you best learn? Can you self-study online? Or do you prefer a classroom environment? Are you interested in doing further study in a PhD program? Or are you interested in learning the technologies to apply them to your daily life?
I spent my time at university learning how to learn. Now I've got a pretty good understanding of how I best learn. I can learn on my own and I'm not particularly interested in going for a PhD program (yet) so I started with an AI Nanodegree to get my feet wet.
If you prefer learning in person and are after further studies an AI Masters Degree may be what you're after.
Both of these things being said, there are many different directions you can take with either of them. Take out a piece of paper, write down your strengths, weaknesses and where you want to be in 3-5 years, then consider whether a Nanodegree or Masters Degree is more suited to your path.
What would I do if I was back in high school?
These questions are always fun to unpack. Hindsight is a beautiful thing. It's a paradox. "If I'd known what I know now back then, things would be so much different."
It's a tough time. Coming to the end of school, there are so many options. Some have it all figured out but many don't. I didn't. I'm still figuring it out.
If I did go back, I'd probably put less pressure on myself to have it all figured out. I'd remind myself no one is born with a passion.
I'd also spend some time exploring a bunch of things. I'm high on openness so there isn't much that doesn't interest me. I'd remind myself to keep following my curiosity and find something which sticks. And I probably wouldn't advise myself to jump straight into university without exploring the world a little.
All of this, of course, is based on what I know now so it's biased. Oh, to be 17 again. I don't know whether I prefer knowing more or knowing less.
I'll host more of these livestreams in the future. If you'd like your question answered live, let me know.