Breaking Into Data Science in 2019

In that case, Python should probably be your first choice.

However, in my experience, picking one and just starting to code is what matters most.

Find out what language you prefer and do not only rely on third-party recommendations.

If you want to be extremely versatile, I would recommend finding a favorite but also being able to use the other.

Luckily for me, the NYC Data Science Academy teaches its entire curriculum in both, R and Python.

I personally prefer using Python for machine learning but, at the same time, appreciate data analysis in R using the tidyverse, which is a collection of R packages designed for data science.

Part 2: Brush Up On StatisticsAs a business major, I had taken an elementary statistics course in college as well as some economics and finance courses.

Thus, diving deeper into statistics did not mean being confronted with something I had never seen before but it still proved to be quite a challenge.

In my opinion, especially in 2019, only knowing how to use machine learning packages, such as scikit-learn, is neither enough to effectively practice data science nor will it be enough to land you a job in data science.

Document Your ProgressIn order to organize everything I would need to know in a centralized manner, I started creating Word documents with summaries for each respective topic.

There are so-called “Cheat Sheets” readily available online, however, I usually find them to be lacking in depth.

Moreover, as I emphasized at the beginning of this article, there is no one-fits-all solution to anything regarding data science.

Therefore, it is a good habit to build your customized data science look-up library.

I took notes during the lectures at the bootcamp and refined and reviewed them at night.

While this took a lot of effort, it greatly facilitated understanding more and more complex algorithms as the lectures progressed.

Master the FundamentalsAs a final note on this topic: do not, under any circumstances, skip the basics.

While trying to jump to fancy algorithms might seem tempting at first, spending the majority of your time with the fundamentals is the better choice in my opinion.

In addition to the lectures, I read several books about statistics and statistical learning.

The best book on statistical learning, in my opinion, is “An Introduction to Statistical Learning: With Applications in R” by Daniela Witten, Robert Tibshirani, and Trevor Hastie.

Different books take different approaches.

Thus, combining books that focus on a verbal explanation of algorithms with books that dive into the technical details proved to be a good investment of my time.

I, too, was intrigued by all the buzzwords circulating on the Internet but ultimately found that you first have to build very solid foundations before you can think about adding new capabilities to your data science skill set.

Ask Many QuestionsIf you should happen to attend a boot camp as I did, make use of your instructors.

Ask as many questions as you can.

Do not wait until you run into serious problems before starting to asking questions.

Even showing more experienced people your code and asking for ways to improve its efficiency can prove to be extremely useful.

In case you should not be able to get professional help, do not despair.

There are plenty of online communities and resources that will help you answer your questions.

Chances are, you are not the first person running into this problem.

On top, figuring out the solution yourself will help you remember it more easily.

Other Things to Brush Up OnDepending on your background, reviewing basic linear algebra and calculus might also be a good idea.

I would recommend either going through your old linear algebra or calculus notes or taking an online course, such as MIT’s freely accessible linear algebra course.

This is particularly important if you should be interested in reading academic papers and more technical books.

Part 3: Build a Project PortfolioComplete At Least Four ProjectsThis third step is of utmost importance if you want to land a job in data science.

In order to convince your potential employers that they should hire you and to apply what you have learned thus far, try to complete at least four major projects.

If you, like myself, attend the NYC Data Science Academy bootcamp, then you will have to complete three projects covering all aspects of the data science lifecycle.

These projects will cover everything from data acquisition over data visualization to machine learning.

Finally, the capstone project enables you to choose any topic you would like to work on.

You should use this opportunity to position yourself in the job market and target your dream employers.

For instance, if your goal is to apply data science to healthcare data, then try to find a project that tackles an issue in that area, such as the prediction of diabetes onset.

Do Not Stop ThereIf you really want to break into a specific industry, you should not stop at four projects.

Search for data that might be relevant to your dream employer and experiment with it.

Build something interesting and write an article or blog post about your project.

The more you showcase your abilities and interest in a specific field, the more likely it is that people in that industry are going to be impressed by you.

Do Not Try To Be Too FancyWhen choosing projects, it is tempting to go for things that sound fancy.

Do not do that.

At least not right away.

Make sure your projects are solid from start through finish and contain as little errors as possible.

Have someone check your projects and review them for you.

During my time at the bootcamp, I presented all of my projects to my fellow classmates as well as my instructors.

Getting different opinions on your work will help you improve future projects.

Part 4: Trying to Find a JobPreparation Is KeyIf you want to succeed in the data science hiring process, prepare yourself as much as possible.

Check out coding challenges on HackerRank, familiarize yourself with the types of questions being asked, and, perhaps most importantly, document your interviewing process.

As with machine learning theory, you should create a document in which you describe and evaluate your experiences when interviewing.

Then, before each interview, review that document along with your machine learning theory document(s) and make sure that you avoid repeating mistakes.

Warming yourself up before starting a coding challenge by completing some tasks on HackerRank might also be helpful.

Learn How To Pitch YourselfIf you want a job in data science, you are going to have to compete with many other applicants.

Set yourself apart by creating your personalized narrative.

Why are you the perfect fit?.Why did you choose these specific projects?.Why data science in the first place?.Since you are going to have to introduce yourself in almost every interview, make sure that you craft a strong narrative that can be adapted depending on what company you are targeting.

While you are at it, prepare pitches for your projects too.

Not every potential employer will want to hear you describing all of your projects.

Maybe one specific project caught the attention of the people that are going to interview you.

Make sure that you are able to describe each project in depth but also have a backup pitch in case you only need a short description of your projects.

Practice these pitches in front of other people.

Thinking of what you might say at home is not comparable to standing in front of people you do not know while trying to explain your projects.

If you go to school or attend a boot camp, practice pitching yourself with your classmates and give each other constructive feedback.

Doing so, you might be able to avoid several mistakes prior to your first interview.

NetworkAfter completing the NYC Data Science Academy’s bootcamp, all graduates are encouraged to attend a hiring partner event in which you might be able to find your future employer.

Before attending these types of events, it is absolutely crucial to have already learned how to pitch yourself and your projects.

Be aggressive when attending such events.

Research the hiring managers and recruiters that are going to attend the event.

During the event, try to figure out whether there might be a fit between you and the company as quickly as possible.

Hand over your resume and ask for business cards.

Another very important piece of advice: do not talk to only one or two people.

Even if there is potential for a great fit, do not limit yourself in the number of potential job offers.

Move on after a certain period of getting to know each other and exchanging contact information has passed.

Networking is a skill that takes practice.

Luckily for myself, the bootcamp provided its students with extensive advice and tips on how to navigate networking events.

Make sure to know the rules of conduct in networking (e.


writing a good follow-up email to every hiring manager that was in attendance).

However, as with the projects, do not stop there.

Network with the people around you.

Data science is a fascinating field with many fascinating people.

Connect with your classmates if you are in school or a bootcamp.

Find interesting people to follow on LinkedIn.

Attend data science meetups in your city.

There are many opportunities for networking and the more you do it, the better you will get at it.

Do Not Give UpFinding a job can be hard.

You might go to many interviews just to have people tell you they will not be able to hire you.

Unless you are lucky and find a job right away, getting a job might turn out to be a very frustrating process.

Do not despair.

If you keep persevering and improving yourself and your resume, someone will eventually notice.

Keep believing that you will get the job offer you want.

Talk to people that have gone through the data science hiring process before and you will see that many of them had many extremely frustrating interviews.

What separates those who make it from those who do not, ultimately, has a lot to do with the ability to keep on fighting and not giving in.

ConclusionBecoming a data scientist in 2019 is not easy.

There will be many hurdles you will have to jump over and many challenges you will have to overcome.

Nevertheless, the rewards of making it through that process are huge.

Not only will you get to do what you love doing but also engage with very bright people from all kinds of different backgrounds.

Just take the first step and the rest will follow.

Start following your passion.

It is fine if it should take you longer than it takes others, what matters is your willpower.

Making sure that you steadily improve day by day will ultimately get you where you want to go.

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