Hi, I'm Jen. Welcome! Today we're talking about the data analytic skills that you
won't learn in college. There a few of them, but there's one key one that every
hiring manager wishes that you had and - in many cases - if you're just getting out
of college, you may not have. I've spent over fifteen years working in analytics
and hiring analysts so I know exactly where the challenges are when you're
just getting out of school and getting into an analytics or data science field.
And I also know the frustration and challenges that hiring managers are
facing which you probably feel when you're a candidate and aren't getting
calls back or aren't getting that second interview. The number one skill that
analysts right out of school tend to be lacking is how to apply all of those
technical skills that you just learned to real-world problems.
You're probably amazing at analyzing research questions. Life is ambiguous
though. There is so much that's not defined and not known when we're doing
analysis in business settings and this is where a lot of people get hung up.
They don't have all of the information they need to perform the analysis and
they tend to freeze up or they go searching for all of the answers so that
they can do this textbook analysis and that's just not reality. It's good to
look for answers and question things, but there are often going to be times where
you're going to be analyzing information and you don't have all of the facts.
That's part of your job is to explain things that we don't understand. The
second closely related skill that you won't learn in college is how to make
information meaningful to people. You know, as an analyst, that the data is
meaningful. I know as a manager that used to be an analyst that the data is
meaningful. But most people honestly don't give a crap what your data says if
you don't give it context and meaning. You have to tell people why they should care
about the data that you analyzed or the results that you got.
All the statistical knowledge or programming knowledge or algorithms that
you learn to create are completely useless if you can't explain them and
give meaning to them to people that aren't data centered people. They're also
completely meaningless if you can't use them to solve real-world problems. You
can have a high education level - you could have a PhD - and still not actually
know how to apply those skills. Sure, you may be extremely book smart, but
businesses aren't just looking for book smart. They're looking for people that
can take what they've learned and apply it to real world situations and business
problems and challenges to be able to make the business run better. This is
where the third skill comes in and that's understanding business from a
business lens. You can perform analysis even without knowing much about the
subject and for a lot of people we do this naturally in our day to day lives.
If you're an analyst, you're probably analytically thinking
through most of the things in your life. You may have Excel spreadsheets for
stuff nobody else would. The better you understand a topic and in this case it's
business and what business is trying to accomplish and even so far as the types
of questions that the business needs to answer even if they're not asking the
right questions. A lot of times what people ask for when they ask for
analysis is what they think the solution or the path to their end answer is. It's
not really asking what they want to know at the end so getting used to
challenging people, sorting out good questions, and good analysis processes is
so key. Use your intellectual capabilities to analyze the data and
then focus immensely on the communication. 99%+ of people
out there are not that familiar with data analysis. Even people that really
use some of the same processes like doctors oftentimes aren't that familiar
with what data analysis is. So break it down, put context to it, put it in plain
language. That's the entire value to me of a data analyst. This is where people
will have an involvement for many years to come in
analytics. A computer can run an algorithm or do machine learning or AI
data analysis work, but a computer can't often give context and put things into
plain language. The reason that you're hired as a data analyst or data
scientist is because you bring this value that computers can't - at least not
yet. That's really giving context and
meaning to all of this data that's analyzed. You're really hired to help
solve business problems and put a puzzle together of the different issues that
come up and be able to explain it and give context and meaning to everyone
else. Just because these aren't skills that you'll learn in college (classrooms) doesn't
mean that you can't learn them while you're in college. You will be a really
powerful, strong candidate if you learn how to do real-world application of
problems and then can reflect that on your resume. You could do this by taking
problems that have already been solved, setting aside the answer and doing the
entire process start to finish. Or take something in your personal life that you
find interesting and research it. It doesn't have to be a hardcore centered
around financial data or production data or things that we typically think of
when we think of data analysis in business. It could be something like weather phenomenon or
sports statistics or really anything as long as you can show how you got from
the start to the end and address a practical problem where you didn't have
all of the data and all of the knowledge available to you. Those are my top three
skills that you won't learn as a data analyst in college but will make you a
super powerful candidate if you do learn them. What questions do you have about
them? Let me know in the comments. I'd be happy to talk with you about it. For more
insights on data analyst and data science careers, subscribe to the channel
and turn on notifications. I'm here every Thursday talking about all things
related to analytics. I hope to see you back!