Welcome to this 3-6-5 Data Science special,
where you’ll learn everything you need to know to land an entry-level job in Data Science!
As one of the fastest-growing industries over the last decade, Data Science has become an
extremely appealing career path. That path, however, needs to start somewhere. So, if
you are watching this video – you’ve got questions. Well, we are here to give you the
answers and then point you to some extra resources that will help you prepare for data science
success. We’ll go over education, experience, skills
and finish up with a cohesive plan on the steps you need to take to start your journey
as a Data Scientist. Of course, all the information is based on empirical research, statements
by employers in data science, and a dash of our personal experience. So, let’s begin, shall we?
In previous videos on our channel, we’ve discussed the best degree for an aspiring
data scientist. To recap: any form of post-graduate degree in a quantitative field gives you a
pretty good chance of success, with Computer Science being the most-represented major. Apart from an education, you also need some
sort of experience credentials to your name. To understand the methodology we used, you
can check out the article linked in the description. For reference, the results suggest that roughly
35% of current Data Scientists have already had a job in the same position, which is actually
fantastic. Woah, woah, woah… but how is this good news?
Well, the remaining 65% had a different occupation prior to that. Therefore, roughly 2 out of
every 3 data scientists are on their first data scientist job in the field. Therefore,
it’s safe to say that becoming a data scientist is a very achievable goal.
However, don’t expect to become a data scientist right after school. A mere 2% of all data
scientists started off with no previous position on their resume. This number in itself sounded
suspiciously high to us. Either way, to land even an entry-level position, you still need
some previous experience elsewhere. This is a testament to how demanding the position
of a current day data scientist is nowadays. Demanding and hard to get, but not impossible.
So, what steps should you take? According to employers and recruiters, if
you want to succeed in the field, you also need to know three things: the tools, the
data and the business. Let’s break this down!
Knowing the tools means confidence in working with the most popular software on the market.
Those are undoubtedly R, Python, or better yet – both. With a bit lower priority but
still extremely important are SQL and visualization software, such as PowerBI and Tableau. Finally,
it is important to note that Excel is still a main prerequisite in any job description
in the field. Now, if you feel you need to strengthen your
data science skillset, we’ve got you covered. We’ve created ‘The 365 Data Science Program’
to help people enter the field of data science, regardless of their background. We have trained
more than 350,000 people around the world and are committed to continue doing so. If
you are interested to learn more, you can find a link in the description that will also
give you 20% off all plans if you’re looking to start learning from an all-around data
science training. Okay! Back to the key requirements for entry-level
data scientists! Next up, is knowing the data. This means you
need to understand where your data is coming from, what are the best ways to process and
pre-process it, and most importantly, how to extract actionable insights from it.
Therefore, you need some coding pedigree, regardless of whether it’s in R, Python
or another scripting language. The statistical and analytical skills are there to help you
understand and interpret the results before translating the raw numbers into insights.
Usually, to land an entry-level job you don’t need to excel in all categories and being
okay in 2 of the 3 is fine… as long as you’re great at programming. 😊
Finally, it’s crucial that you know the business. Before you apply for a job in a
given company, you must find out which aspects of data science and what skills are necessary
to land a position there. And, by all means, having market expertise in the specific field,
is always a bonus. So, the more holistic your understanding of the data and the industry,
the more well-suited you are for the position. Overall, employers are looking for somebody
with good coding, statistical, and analytical skills.
Aren’t we missing something? Of course, employers are achievement-oriented,
so they’re always looking for certain transferrable skills in a candidate that add value to the
company. Taking initiative, setting challenging goals,
and making efforts to exceed those goals are some examples of transferrable skills you
should develop. Interpersonal skills also translate easily across various industries
and contexts, so make sure you got that covered. Other highly appreciated skills in this category
include the ability to learn from experience and be the propeller of positive changes,
independence, self-direction, and accountability. Therefore, make sure your resume includes
projects or internships where you worked with others, on top of some evidence of your proficiency
in coding. Your statistical and analytical credentials can always be tested with an on-sight
examination or an academic transcript, so focus on the interpersonal and programming
skills when constructing your resume. For the full list of skills, check our free
Data Science career guide. And, if you want to learn more about what you should and shouldn’t
include in your resume, check out our data science resume guides. Links are in the description.
Alright! So, we discussed what you need to know, and
what skills you need to have, but now it’s time for the what you need to DO part.
In highly competitive fields, such as Data Science, who you know could be just as important
as what you know. This is especially true when you’re trying to break into the field
and find somebody who is willing to give you a chance, even at a Junior position. Getting
a recommendation from your previous boss, or a referral from an employee of the company
you are currently applying at, is a sure-fire way to boosting your chances of getting hired.
And the tried and tested way of getting these is through networking.
One good approach is to use Handshake and similar sites, where alumni from your school
post job ads. This way, you can find interesting potential employers who you want to interact
with. Drop them an e-mail, ask them for an informational interview, give them your details
and ask specific questions about what their company does. By doing so, you’re making
a solid good impression because: A) you know or you want to learn the business, and B)
you’ve done your research. Sometimes, you won’t be able to get direct
contact information through the website, so you can check out your school’s alumni directory.
You should be able to find at least an e-mail, a phone number or a LinkedIn profile, and
all you have to do next is reach out. Alternatively, you can meet people in the
field by going to local conferences or lectures about Data Science. Universities and colleges
frequently organize events of the sort, which are often open to the general public. In addition,
independent Data Science societies also sponsor or organize control-group meetups where they
discuss the applications of D-S in specific fields – like medicine or finance for example.
Just remember, the more invested you look, the higher the chance that these people would
want to keep in touch, so try to stay enthusiastic and curious.
Of course, knowing the right people will get you far, but in most cases – won’t get
you the job. Even with a referral or recommendation, you still have to go through a job interview.
Your potential employers can always test your statistical skills with a written exam and
your programming skills with a remote task. However, you only get the face-to-face interview
to present your coherent communication skills, so make sure you highlight them in the best
possible way. Of course, data science incorporates multi-disciplinary aspects of various fields,
so it can be difficult to prepare for everything. That is why we created a free booklet with
180 of the most common real-world interview questions for D-S and their answers. Think
of this as our Data Science equivalent to “Cracking the Code”, albeit a little bit
smaller. You can find a link to this resource in the description as well.
Right! After explaining everything, let’s quickly
summarize what you need to do, to land an entry-level job as a Data Scientist.
For starters, you should earn at least a graduate degree in a quantitative major like Computer
Science. Then, you need to gain experience in a field tangent to Data Science, so a job
as an analyst or in I.T. is a good way to go about it. An internship is also a viable
option, if you’re still studying. Knowledge about coding, working with data
and the line of work you are interested in is vital too, so ensure your resume showcases
all of that. Also, try to highlight some essential transferrable skills in your resume, like
drive for the business and ability to work in cross-functional teams. Conduct some networking
and try to earn a recommendation or referral for a specific position. On a final note,
make sure to showcase certain immeasurable qualities you possess, like communication
skills and curiosity during the interview. In our opinion, doing all of this will give
you a great shot at securing an entry-level job as a Data Scientist.
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