(Time to Read: 4 mins)
Data science is on the rise and there is no stopping it. If you've been having thoughts lately about hopping on the data science bandwagon, then you should definitely consider using data science podcasts to guide your first steps and introduce you to this massively thrilling field.
Cracking your way into a new field is an endeavor that is usually fogged with dark clouds of intimidation. Clouds that often end up scaring people away from learning things that they're interested in and might have even had a shot at mastering.
There's a study that you might have heard and that a lot of people keep reiterating whenever learning a new skill is discussed that says that a person needs 10,000 hours ON AVERAGE to just learn a new skill.
The first thing you need to do if you have heard that figure is to throw it in the nearest trashcan with all the discouragement that it might have caused you.
This figure was concluded from a study conducted by a professor of psychology at Florida State University named K. Anders Ericsson.
What Dr. Ericsson meant with that figure was how long it took a person on average to reach an ultra-competitive level at a certain field.
Laying your first steps into a field is a whole other thing, and it needs no more than what you would call your “dead time”, thanks to podcasts. Here we're particularly concerned with data science podcasts and how they can change the way you go into this often intimidating field.
Keeping Up With the Express Train
With so many subjects to learn in a relatively-new field that is open to massive exploration like data science, visual and textual learning methods that demand exclusive attention cannot viably be your sole resort.
Even if you are not an auditory learner by nature, data science podcasts can still bridge a certain gap between you and the data science field, which you can then supplement with research, reading, watching videos, and practicing.
Podcasts can give you a glimpse (or more like a whisper) at the big picture.
You would get to learn the terminology, know a little bit about every subfield, and stay updated on the latest industry trends.
This information will inevitably affect your efficiency while learning (positively, of course) by allowing you to save your free time for in-depth lessons and “set the mood” for the first topics you’ll learn into the field you are interested in.
If you get to know briefly about the concept while to work or as you're shopping for groceries, your free time in front of a computer will be directed in total towards digging deep into that concept and expanding upon what you already dipped your feet into.
Getting Familiar with the Industry
Let's be straight about one thing; no one can deny that in the end, in order to become an actual data scientist there is actual practice that has to be done.
The traditional saying “practice makes perfect” still applies.
But, keeping up with such a vibrant and untraditional field using exclusively traditional means is simply unworkable. The data science umbrella comprises multiple subfields that differ in scope depending on who you ask, but in the broadest sense, you have at least data analysis, data engineering, and machine learning engineering, each with its own branches and specialties.
Something that is worth noting here is that each of these specialties has its own career path with its own unique sequence – and subsequent paths to follow.
That's a point that is given special attention by data science podcasts.
You will find some, such as The Super Data Science Podcast as well as many other similar podcasts constantly presenting you with success stories and career experiences that can give you valuable anecdotal insights – that are often tough to get.
You can learn how it might be like to get into each of these data science subfields based on the real-life experiences of others who have already walked that road – so you don’t have to start from scratch.
People who are looking to making a career shift into data science are not the only ones who stand to benefit from such stories. You will find that many people who are already knee-deep in their data science careers still use data science podcasts for the sake of maintaining their enthusiasm and redrawing their guidelines by listening to other people who once lived the same situations or shared the same thoughts.
The Friendly Factor
Since we got into the conversational and casual aspect of data science podcasts, it must be said that this tone –which is common among podcasters in general – adds a great deal to the learning experience.
Imagine the difference between listening to a lecture recorded from inside a university auditoriumvs. one in the comfort of your ears.
In the universities, you can hear the lecturer speaking out to hundreds of students (whose presence you can also hear) and kind of follow along. But then comes a big difference when listening to a podcast where only one person along with, say, one or two guests at the most, is having a close, partially-personal conversation about a certain topic.
The difference is indisputably huge, and here we're only comparing it to an audio lecture.
Take that up another level and compare a podcast to a text. That's probably why books and videos struggle to maintain our attention for a few minutes while most data science podcasts can go on for over an hour or two and still keep a large portion of their audience.
In terms of the availability of material, it's hard to compete with podcasts.
You can go on Youtube and search for lectures and crash courses, but the number of channels or playlists that present any coherent data science-related material that can walk you from one concept in a serial manner is relatively limited when compared to the world of data science podcasts.
You could learn almost anything depending on which day of the week you are at, for instance:
Take the O'Reilly Data Show coming at you on Wednesdays, with their consistently illuminating discussions of practical data science applications and their exploration into new possibilities in the field.
Besides their educative value, you will find these discussions pretty entertaining to listen to and visualize along with as you're taking a walk with the podcast playing in your headphones.
The very next day, (every Thursday) tune into The Super Data Science Podcast where you can stop and think more strategically about your data science career – and where it’s headed. You’ll listen to others who are already standing where you want to be and see for yourself what the next steps might be for your data science career.
The Data Skeptic podcast, for example, which you'll find countless other similar podcasts online. On Data Skeptic you are presented on a weekly basis (every Friday) with a new concept in data science that is discussed with a fair degree of depth that is more than enough to get you started on the subject.
So, there you have a data science podcast that you can tune in on every Friday on your way to work/college or during the weekend while you're out waiting on a group of friends, and then you can spend the rest of the week looking more into that concept that you were introduced to. That's only one type of data science podcasts.
On Sundays, you have the Linear Digressions podcast examining practical data science applications like Google Flu Trends or Autoencoders, and giving you a peek at situations you might encounter as a data scientist by discussing things like “How we pick projects for a professional data science team.”
Boom. That’s your week.
There you go. In a couple of paragraphs, we set up a schedule to fill your dead time for half of the week. With some research for the data science podcast that fit your particular interests in the field, you can set up an entire program in that fashion and get yourself diving into the vast blue oceans of data science, almost all free of cost. It's all within your reach, so it's yours to seize.