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. If you enjoyed this video, don’t forget

to hit the “like” or “share” button! And if you’d like to become an expert in

all things data science, subscribe to our channel for more videos like this one.

Thanks for watching!