My name is Austin Anderson and I’m a data science intern for Upper Hand. This is my first real foray into the industry and it has already taught me so many valuable pieces of information that it’s really hard to condense them all into one coherent blog post.
However, there has been a persistent thread that has permeated into almost everything that I have done so far, and I’d like to take a little time to talk about that. Now, one would never mistake me for a tenured industry professional. But, don’t confuse my lack of experience for a lack of perspective. I feel, if nothing else, my lack of experience provides me with one of the most common and frequently under appreciated perspectives: someone who is new to all of this. Whether or not we like to admit it, all of you reading this have been there or will be there soon, and to me, it’s one of the most important periods in anyone’s professional career.
As someone who is new to this, there are a few trends that you start to notice as you move out of the classroom and into the real world. The one that has made itself abundantly clear to me is that you have to be prepared for things to go wrong.
Now, at first blush, that piece of advice seems blatantly obvious. But, if you look a little deeper, it reveals a scary truth: we aren’t really taught what to do when things go wrong.
In comparison with the controlled environment of the classroom, the workplace offers no true structure for how matters are supposed to play out. I have ran into literally hundreds of things that have “gone wrong” in my internship so far, and I’ve only been here for a month. Of course, acknowledging that “things go wrong” isn’t what I’m here to do. What I really want to talk about is what you can do in order to prepare for this unfortunate fact.
1. Expect the unexpected
My first piece of advice to prepare yourself is to use the opportunities you get to build up your problem solving skills. Classes are designed to get as many people through them as possible while still having to teach something valuable. This naturally makes them fairly insulated to unforeseen complications, but when those complications rear their head, make sure you attack the problem before it attacks you. They may be the exception in schools, but in the workplace I’ve found them to be the norm.
2. Focus on how your skills apply to the workplace
Another piece of advice I have is to not to focus too much on your skills in the context of the classroom, but more how they can benefit you in the workplace. If you simply learn something in order to pass a class did you really even learn anything? I’ve found that there is something you can apply to the real-world in pretty much every class I’ve ever taken, but it’s on you to find out what that is.
3. Don’t neglect the importance of soft skills
The last, and most important, thing I’d like to say is that you should not overplay the importance of your hard-skills and neglect your soft-skills. I’ve found this to be especially true for the data-science inclined, but it applies to pretty much any sector of this industry.
Yes, being fluent in multiple coding languages and knowing advanced statistical computing techniques are extremely useful in the data field. But you almost never get to use them in a vacuum like you do when working on a personal project or an assignment. You are almost always doing something for someone in order to fulfill a specific task, and this means that you have to interact with many different people in order to do what you need to do. This is where communication and interpersonal skills take center-stage, and they’ll make or break you in the workplace.
Putting it all together
Now, this isn’t to say that this advice only applies to those just getting into the workplace. This applies to professionals who are just getting into the data science space as well. The biggest mistake that you can make when getting into data for the first time is having preconceptions about how using said data will turn out.
The truth of the matter is that the range of outcomes when venturing into the data science space are vast, and the best thing one can do in order to cut through it all is approach it with an open mind. Things will go wrong and they won’t work out like you think they will, so being flexible and not getting phased when the data isn’t doing what you want it to are instrumental to success. You aren’t the master of the data so much as you are its shepherd, as you can never truly tell the data what to do or what shape to take. Being patient and working with what the data gives you will always yield better results in the long run.