How to get a data science internship?
Nowadays reports and publications consistently
name ‘data scientist’ as one of the preferable
jobs.
While there are many articles about the set
of skill you need to get the data scientist
position, we wanted to focus on the students
who crave working in this prosperous field.
The benefits of a data science internship
are countless, beginning with the opportunity
to work with professionals in the field, up
to building your own portfolio.
These internships offer fantastic mentorship
and networking opportunities.
You can learn from professional data scientists
and demonstrate you are already one step ahead
of your peers.
Watching this video means that you are already
aware of all that, so let’s focus on how
to get the desired data science internship.
What does a data science internship look like?
Data science internships are a unique opportunity
for people who want to gain hands-on experience
working with data at a fast-growing company.
In fact, many recent graduates often have
difficulty when they enter their first official
job as a data scientist.
Suddenly they realize that the data they will
be working with is much messier and more complex
than what they’ve experienced while studying.
There is, however, a simple explanation why
this happens.
As a student, many of the data sets you encounter
are carefully preprocessed by the course instructor,
so they are much “cleaner” compared to
actual “real world” data sets.
And this is one of the massive benefits of
taking a data science internship, working
with the actual real-life messy data.
People, who have just entered the field, have
very high expectations about the job, but
the truth is that it is highly unlikely that
you will be tasked with creating a machine
learning algorithm right away.
Why?
Because 90% of machine learning is preprocessing
and 10% is modeling.
To sum up – expect messy, raw data and all
that comes with this beautiful chaos.
What we are referring to, of course, is the
hands-on-experience and unparalleled exposure
to skilled data scientists that will help
you along the way!
What are the main activities undertaken during
a data science internship?
We said that a data science internship will
introduce you to real-life data.
As a data science intern, you’ll be on a
team of professionals who are solving business
problems for companies (including the one
you’re working at).
We mentioned machine learning, but a more
probable workload scenario would involve:
conducting analyses, producing reports, building
creative data visualizations, molding the
data into a narrative or the better-known
– telling a data-driven story
All of these might sound overwhelming for
the novice data scientist, but you won’t
be in it on your own.
You will work closely with engineers, product
designers, and product managers.
You will be asked to devise metrics, design
randomized controlled experiments, and tackle
hard open-ended problems.
While on this topic, it is a good idea to
commit yourself to learn and mastering one,
two or more programming languages, to have
some SQL skills, and to know how to use some
big data tools.
Just give it a try and you will find that
these concepts are truly not that complex
as it sounds.
In fact, the more you learn during your internship,
the more your manager will notice you and
after all, isn’t the end goal of an internship
to get hired by the company you have worked
for (or to have leverage when negotiating
with an even bigger one)?
what do you need to do to get the internship?
Having said all that, it won’t come as a
surprise if we tell you that the key to success
in data science is to start early.
These tips are all over the internet, but
let’s look the basics you want to have covered:
1.
Experience?
You don’t have to worry about it.
That’s the reason you are doing an internship
– to gain experience.
Interns are a great way to bring new and innovative
ideas onto a team because interns come with
a fresh set of eyes.
“Companies can use this perspective to their
advantage by working closely with interns
to develop and test new hypotheses”, says
Eric Frenkiel, co-founder and CEO of database
start-up, MemSQL.
2.
Resume.
The sooner you start with building your data
science resume the better.
You need to make sure it is up-to-date and
includes previous projects.
You won’t believe how many people underestimate
the power of the CV!
3.
Cover letter.
You might want to consider making your cover
letter fully customized.
It will make you stand out from the other
applicants.
A generic cover letter is sure to make an
impression that this is just one more application
out of a huge pile.
No employer likes to think they are just one
of a long list of possibilities, and it makes
the candidate look indiscriminate.
4.
Interview etiquette.
In addition, being aware of relevant interview
etiquette is a great benefit.
Good manners make difference.
5.
Soft skills.
Finally, some companies look for soft skills
when hiring data science interns.
Feel free to practice possible interview questions
with your friends.
This will certainly make you feel more confident
and well-prepared.
Still, we are not here to tell you the things
you can easily find online.
We have prepared for you a cheat sheet with
success strategies for finding the data science
internship you truly want.
1.
Best foot forward… start participating in
career events and job fairs
The benefit of visiting these events is getting
in touch with a lot of companies.
It is a time-efficient process and you have
the chance to make a good impression by showing
off strong motivation.
2.
Reading glasses on… and dive into your University
Job Board
Look at it often, because sometimes firms
announce certain job openings exclusively
through the University Job Board.
Many students don’t pay attention to this
source of opportunity, so doing so immediately
increases your chances.
3.
Warm up your typing fingers… and contact
start-up companies
Be proactive and contact interesting start-up
firms.
Working within a start-up team would be great
for your personal development.
Offer your help and gain valuable experience
in a dynamic environment.
Finally, building your professional network
– truly the heart of our journey.
Here are some things you can to do to widen
your professional network.
For example, build your data science portfolio.
Your data science portfolio will be the public
evidence of your data science skills.
The importance of the portfolio is three-fold.
A data science portfolio can help get you
employment.
It shows your strengths.
And finally, you can learn from it while building
it – that’s super important.
How to approach building a portfolio for your
data science internship?
Good, so here we have the question of how
to build your data science intern portfolio:
Kaggle and GitHub are some of the best platforms
you can use.
It is very likely that Kaggle will be an important
part of your portfolio creation journey.
It has a large, active community of data scientists
and a great platform for sharing your work.
Furthermore, Kaggle competitions are a great
way to gain hands-on experience in real-life
datasets.
Also, you will learn and/or practice your
data cleaning skills.
This gives you a chance to practice analyzing
data and a way to come up with a model.
On the other hand, GitHub is a platform where
you can interact with data scientists and
machine learning engineers.
Having an active GitHub account is a powerful
signal that you really want to enter the field
and can help you build some credibility.
In fact, at some companies, hiring managers
look at the applicant’s GitHub to get a
better idea about what they have built and
how they’ve built it.
It’s all part of the selection process.
Do we have other suggestions?
Yes.
Another great idea is to pick up side projects.
With platforms such as Toptal and Upwork,
you can sign up as a freelancer and work with
a variety of companies and start-ups to gain
experience.
It may be difficult to land a freelance project,
but if you do, you’ll be compelled to do
your best and learn a lot along the way.
Some other useful places where you can find
data science resources are Hunch, Data Mining
Blog, SmartDataCollective, and KDNuggets.
Why do we think these are useful names to
have to bounce around in your brain?
We’ll tell you!
Consider the following situation: your future
employer asks you about the last data science
article you read.
You don’t really spend that much time browsing
the internet for data science news.
What you do, however, is open your email once
or twice a week and read the newsletters from
these websites.
The titles stick, and so do the names of these
well-recognized platforms.
This already creates a fantastic first impression.
And of course, the more pieces of writings
you read, the more up-to-date you will be,
and the more bonus points you will score with
your future employer.
How about online courses?
That said, only reading articles isn’t always
enough.
To be honest, employers prefer students to
come from mathematics, statistics, or programming
background, because they don’t really know
how otherwise to test the data science capabilities
of an applicant.
The programming languages needed usually include
Python and R, with the former leading the
way.
One of the best ways to tackle this issue
is to take online courses.
This method of learning will save you time
and you don’t have to worry about your budget.
These courses teach you in detail all the
necessary skills to start your desired job.
At the beginning of this video, we mentioned
that you may need to build a personal brand.
Think of “personal branding” your online
appearance and what you want your future employer
to see.
What we suggest is to make yourself a professional
LinkedIn profile.
Unlike the resume, we spoke earlier, a LinkedIn
profile allows you to describe all your projects
and work experience in more depth because
you can emphasize the previous projects or
companies, you have worked on.
An important part of LinkedIn is the search
tool because employers search for people on
LinkedIn quite often and your goal as a future
data science intern is to show up in the search.
You might want to consider having relevant
keywords in your profile.
LinkedIn allows you to see which companies
have searched for you and who has viewed your
profile.
In addition, the site helps you to gain insights
on industry trends or even how you compare
with other aspiring data scientists.
LinkedIn can and should be used as a strategic
tool to cultivate your network and build your
brand.
We know that this is a lot of information
to process but these days, there is more than
one way to show off your skills and get the
data science internship you really desire.
One last piece of advice from us would be
to never stop learning!
This is how you grow as a person and as a
professional.
Don’t forget to have fun along the way and
keep checking our site www.365datascience.com
for more information and new
data science courses!
