On this short episode we follow up on a sage piece of advice from Sam Charrington. Namely, there is no one right way to learn all of the requisite skills to be successful in data science or the data profession.
Whether you love data engineering, visualization, applied statistics, story telling, or data science, there are so many roads available to you to learn the necessary abilities. You can read books, take online courses, enroll in University programs (or some combination of the three).
All that matters is that you have the excitement, drive, and passion to complete your training and enter the profession!
To keep up with the podcast be sure to follow us on twitter @datacouturepod and on instagram @datacouturepodcast. And, if you’d like to help support future episodes, then consider becoming a patron at patreon.com/datacouture!
Music for the show: Foolish Game / God Don’t Work On Commission by spinmeister (c) copyright 2014 Licensed under a Creative Commons Attribution (3.0) license. http://dig.ccmixter.org/files/spinmeister/46822 Ft: Snowflake
Welcome to data couture, the podcast about data culture at work at home, and on the go. I’m your host, Jordan Bohall. If you like what you hear, be sure to subscribe lows, get the latest episodes wherever you get podcasts. And if you’d like to stay up to date on everything data couture, be sure to follow us on Twitter at data couture pod. Finally, if you’d like to help support this in future episodes, consider becoming a patron of the podcast through our
Patreon firstname.lastname@example.org. forward slash data couture. Now on to the show.
Welcome to data couture. I’m your host Jordan. And on today’s episode, we’re going to be following up on the interview with Sam Charrington. Namely, the notion about whether or not you need to go to grad school in order to become a data scientist. Now, if you forget, Sam says, and we’ll hear this in a second, you should know yourself and do what’s best for your own style of learning. Let’s turn to that now.
There are all these data science programs, all these graduate programs that are cropping up at every university across the United States. is unnecessary? should people be going into these programs? Does it off? Does it actually offer anything valuable? When you can go too fast? Or you can do some of these other online learning courses? What do you think?
It’s a good question?
I think that it’s really important to know yourself and what you need. And to way, you know, way once you get through kind of a formal program versus what you can get on your own.
And also what you know, where you’re coming from,
has a lot to do with that. I think
it if you’re, you know, a lot of people need the structure of, of a formal program. Oh, sure. Right. And they need the, the camaraderie and like the kind of the shared accountability of like showing up in front, you know, in front of a professor and like sitting with people around them.
And having said, due dates, exactly what a code now.
You know, but at the same time, if you’re, I think the the opportunity is that if you’re highly motivated, and you’ve got kind of the base level, you know, sort of base level skills, you know, if you’re a programmer, for example, right, you know, you could pick up a course like this. And, again, the highly motivated comes into play, because, you know, you can’t, you know, one way of taking the course, is the course uses Jupyter notebooks. Sure. And so one way of taking the course is kind of control entering through the nose. But that’s not the same as you know, the standard course, which is, you know, understand the code and each of the cells until you could go to a blank notebook, and sure, manage yourself.
So, if we take Sam seriously, then really, in order to be a data scientist, it’s not about what a piece of paper says about you, for example, my degrees are in mathematics and philosophy. And yet I do data science. I do statistics, I do coding, I do all of those, right. And it goes back to the episode, I believe it’s Episode Six, where we talked about being an autodidact and data science. And so to take seriously, Sams notion that you have to know yourself and understand what you need. Well, you know, I couldn’t have gotten through my degrees without being in a formal program. It’s heavy theory. And, you know, I’m sure some people do very well on their own. But it was very beneficial to me to have that set of standard times that I met with my professors or the classes or making sure that more or less somebody was managing the work that I was producing. However, in this age, we have all this age, cheese, and the age of the internet, with all these wonderful online resources, it’s not necessarily clear that you really need somebody holding your hand, because the online courses are built, well, most of them anyways, in such a way that they lead you step by step from not knowing anything about coding, say, all the way up to writing your first Hello World program to completing complete algorithm that you can then deploy on whichever outlet you choose to deploy. And whether that be GitHub or through some sort of, say, Power BI connection, or through a website, or however else you choose to do that, and the classes that you take online. I, well, I suspect some people are just great at reading a book and building, being able to code immediately from it. But you know, maybe there’s this, how to put it, this kind of hybrid approach where you take the online course you read a book, maybe you show up to college class, maybe you just sit in on it.
It’s really all that matters, as if you know yourself and you understand how you need to learn the material. And then if you can master the material even better. But there’s that one hiccup, you say, Jordan, I did all these things. And I can, you know, perform all the basic functions or basic skill sets required to call myself a data scientist, but I can’t get a job because I don’t have that piece of paper certifying that I went to this university, and I completed these courses, and I got these grades. And therefore, I’m not really qualified, in the eyes of the hiring beholder to get a job in data science. Well, I say to you fear not. Because the industry is changing, especially with the tight labor market as it is and hundreds of thousands of unfilled data science jobs, data, professional jobs, or data engineering or visualization specialist jobs, I can tell you that most companies are in a lurch to find true talent. And so I think the rules for having a certain degree are significantly lightning. And so far as people need folks with the skill set of data science, stats, programming, visualization, storytelling, all of these pieces. And so if you find a way that you can learn these particular sets of skills, and it works for you, and you can complete them and you’re excited about them. That’s key, be excited, or if you’re not excited, maybe move on to something else. But if you can retain this excitement for this particular profession,
I think that you will not have any issue whatsoever, leveraging your skill sets to get the data science job of your dreams. I’d love to hear your thoughts below in the comments. Please let me know and be happy, happy to interact with all of you and get kind of a pulse on what everyone’s thinking. Talk to you next time. That’s it for the show. Thank you for listening. And if you liked what you’ve heard, then consider leaving a comment or like down below. Stay up to date on everything data couture, be sure to follow us on Twitter at data couture pod. consider becoming a email@example.com forward slash data couture music for the podcast. It’s called foolish game. God don’t work on commission by the artist spin Meister used under a Creative Commons Attribution 3.0 license, writing, editing and production of the podcast is by your host Jordan