But I'm just another worthless PhD.
Those are the words that were echoing in my head whenever I left my academic position
as a research scientist and started looking for a job in industry.
I did not hit success right away.
I actually submitted over 200 applications my first time trying to break into industry
and failed.
I did not get a single reasonable offer that was worth considering and I had to go back
and dust myself off, do more prep and get much more specific in the job I was looking
for in order to make that successful transition to industry.
And part of the issue was my mindset.
I just didn't believe that I was worthy of an industry job or that my experiences in
academia had aligned me for anything in industry that I could be hired for.
This is the grad school sucks podcast and my name is Matt Carlson.
I am a social science PhD who worked in academia and then left academia to start a career in
data analytics in industry.
And on this show, I want to talk to you a little bit about how to start your own data
related career outside of academia.
And today I'm going to specifically talk about five reasons why I think many PhDs make great
data analysts.
So let's get started.
Number one, PhDs are curious people.
And if you're working with data in industry, there are a lot of answers that you are going
to need to find.
This could be answers on where certain data is located, how to work through different
errors that you run into, how to understand some of the data that's coming in, or simply
how to use that data to answer different questions that your organization needs insight
on.
So if you have done scientific hypothesis testing as a PhD or grad student, I think
you are perfectly well equipped to be able to answer questions that are brought up in
the industry space.
And I think an obvious example that comes to mind for me whenever I think about how PhDs
are great at seeking out solutions to problems are coding errors.
When I was a grad student, my mentor did know a fair amount about different stats packages,
but he was not able to solve every problem that I ran into.
And so I spent a lot of time researching on my own how to solve different errors, specifically
in M plus that I was running into when I was doing different research projects.
And while it sucked at the time because I was spending a lot of time and energy to solve
what felt like really small persistent issues, it taught me how to seek out the solutions
to my own problems.
And that is something that comes into play in my work today as a data analyst, because
you still run into problems when you're an industry, not just in academia, you run into
problems all the time when you're working with data and you're trying to get answers
out of it so that leadership can get insight into different issues and use that for their
organization moving forward.
All right, the second reason that I think many PhDs make great data analysts is that
we've already spent a lot of our career working with data.
There's a phrase that I've heard before that goes something like data is data.
And essentially, if you've worked with one kind of data before, whether that's survey
data or data on different health markers, you can translate a lot of that understanding
and skill to working with other kinds of data, whether that's forecasting trends in real estate
or different hospital metrics that the organization you're working in is interested in.
Of course, there are nuances to different sectors and types of data that are valuable.
But if you spent time working with quantitative data of one type, I think it is very easy
to spend time working with quantitative data of a different type.
And of course, a great example is my own career.
I worked in studying the effects of trauma on young kids when I was in academia.
And then in my academic position, I was specifically looking at the effects of different programs.
Those topics are completely unrelated to the work I do today.
I look at things like insurance claims information and health care utilization.
But the fact of the matter is that data is data.
And those experiences I had in academia prepared me for working with nearly any kind of data
that I found myself in front of in the future.
All right.
Reason number three, I think the PhDs make great data analysts is that we have used different
programs and stats packages to work with data before.
If you're a quantitative PhD, you'll have worked with different programs like SPSS,
SAS, MATLAB, STATA, maybe even R. And while many of those programs are not used in industry,
the programs that are used in industry do function quite similarly to those programs.
And you'll very easily be able to pick up a new coding program or language in order to
work with industry data.
They say that once you learn a second language, learning the third language is actually much
easier.
And I find that that is definitely the case when it comes to different coding languages.
All those years I spent understanding M+ and knowing how to create a new script for M+ to
run the statistical methods that I was interested in is not completely down the drain.
Although I don't use M+ in my day to day job, I do use other tools like SQL and actually
SAS.
And all the time I spent learning the M+ code language, even though I don't use M+ contributes
to my ability to understand any language.
And while it does take time to pick up and learn a new coding language, it is much, much
faster if you already know one first.
And of course, the big three languages or programs that are used in industry are SQL,
R, and Python with SQL or SQL being the most common.
All right, reason number four that I think many PhDs make great data analysts, we are
self taught.
Of course, we're taught in classrooms, of course, we have mentors who guide us in different
ways.
But if you are a PhD student or you've gotten a PhD, you've done a dissertation, you've
published papers, you have taught yourself something in some way along the way.
And this is incredibly valuable because although there is oftentimes some kind of onboarding
or early training whenever you get your first job in data analytics, you're really expected
to learn a lot of it on your own.
Of course, you can ask questions, of course, you can get insight from others like your
colleagues and coworkers.
But there are always going to be things that you're going to have to figure out on your
own.
And if you have flexed the muscle of being self taught as an academic, that is going to
definitely benefit you when you transition to industry and start teaching yourself the
ways of industry.
There are always new tools that are emerging, there are new trends you're going to have
to understand in data analytics.
And the faster you can learn, the faster you can become more and more competent.
All right, before I share the fifth reason that I think PhDs make great data analysts,
I want to invite you to my upcoming live workshop in about 10 days, I'm going to be going live
for about an hour to discuss my transition from academic research to data analytics in
industry.
I'm going to be walking through my path specifically, and then the path that I think nearly any
quantitatively inclined PhD can take to become a data analyst.
If you've been considering a non academic career working with data, I think this free
workshop is exactly what you need in order to build your understanding on what is out
there.
I will leave a link in the description of this episode if you'd like to register and
I hope to see you there.
All right, number five, the fifth and final reason why I think many PhDs make great data
analysts is we are persistent.
Many times when you're working with data, things are not going to be easy.
Maybe the data is not going to be clean.
Maybe the data is not going to behave well in whatever fashion you're using it.
We're just always going to be problems that arise that require you to try again and again
and again to get things right to make sure that report runs correctly to make sure that
query actually answers the question needing solved.
And like I said before, if you have done or in a PhD, you have been persistent, persistent
for a long period of time, because PhDs take forever to finish, especially if you're actually
going to do the dissertation at the end and finish that PhD.
Persistency matters both when you're a data analyst, but also when you're trying to get
a data analyst job, because I think many people are going to have to submit at least 100,
maybe even 200 applications in order to get that first industry job.
And of course, if you want to know more about that, be sure to sign up for the workshop that's
happening in 10 days.
But whether you're on the job market or you've gotten that first job already, persistency
is going to matter because there are going to be a lot of people that are going to fall
out of the race of becoming a data analyst or becoming a senior data analyst or maybe
breaking into data science one day.
And if you're persistent and you just keep going, you're going to be able to stay ahead
of all of those people who don't have the same level of tenacity that you do.
All right, those are my five reasons why I think many PhDs make great data analysts.
If you enjoyed this podcast episode, be sure to subscribe to hear more in the coming months
about what it takes to become a data analyst with your academic skills and experiences
and leave a rating or review to let others know that this is a podcast worth listening
to.
Thank you all for being here and I'll see you next week.