5 Questions Every Aspiring Data Scientist Wants To Know

Lots of people have taken the first step into getting into the field of data, but at some point everyone faces the next big step: how do I make a career out of this? After weeks of discussions with aspiring data scientists, below are the top five questions I’ve consistently heard when it comes to getting into the data sector.

Also, for anyone who is trying to learn the first steps to becoming a data scientist, you can view this article.

1. I’m trying to find data science jobs but they’re tough to come by. How can I get my foot in the door?

There is some weird rumor floating around that a data scientist’s job is completely different than the job of a data analyst, BI developer, data engineer, database manager, or some other data related job. It’s not! There are tons of overlap between all of those jobs, so taking a job as a BI developer will help give your experience handling data sharpen your data science skills.

Do not overlook these jobs. There are plenty of them out there. There is a good chance that these companies also have data scientists working for them which gives a great opportunity to learn first hand from them. If they don’t have data scientists working there, thats also a great opportunity to create your own data science projects in house to show managers your skillset. Sometimes if you want a data science role, you have to create one yourself.

2. I’ve taken data science courses and bootcamps, but I don’t have a strong background in statistics. Will that be a problem?

I’ve heard the saying “Data science is like driving a car. You don’t need to know how everything under the hood words in order to drive it.”

While that can be true and you could run some algorithms to make predictions or insights without truly understanding how the statistical fundamentals are working, you will run into issues at some point where an algorithm is inappropriate for certain reasons due to statistical assumptions which can lead to bad predictions. Even worse, when you do have to explain your reasoning of why you chose a model and you don’t know what the true benefits of the model are, people might not trust your work.

Aspiring data scientists also believe that all data science projects are writing machine learning problems. There are a lot of problems that can be solved using simple probabilities and distributions. Understanding how these distributions work can give you the ability to solve a wider array of problems when machine learning models are not needed.

Data science is more like picking stocks. Anyone can pick a stock and have it be successful in the short term, but the people who are the most successful in the stock market tend to be the ones who do research about the companies they are investing in.

3. I didn’t get a major in data science, does that mean I won’t be qualified for a data science job?

Absolutely not! Data science is a relatively new term so universities who are teaching data science curriculums are new to the game. Most likely data scientists that you see or talk to did not major in data science but actually in a field like computer science or mathematics. However, with the advancements of online data science courses and bootcamps you can learn data science skills that can help you build an online portfolio to show employers that you can handle a dataset.

4. Are online courses enough for learning or should I get something like a masters degree?

Data science bootcamps are great for people who are trying to become familiar with the landscape of the job, but it will not be enough for an employer to hire just based off the bootcamp. I’ve taken courses through Coursera, Udemy, and Udacity all for different stages of my career path. I enjoy the cheaper options when I’m trying to learn a specific skill, like learning Docker, coding in Python, visualizations in R, etc. There’s no need for a whole bootcamp when you’re just trying to focus in on one subject matter.

A masters degree is a big step. I feel like too many people immediately jump into getting a higher degree without really understanding if they like the career they’re getting into. A masters degree is great for future career potential, but it is a big risk if you are still on the fence on whether you really enjoy being a data scientist.

When you’re fully ready to take the next step for long term growth in data science, a masters degree is a great choice. If you’re still trying to learn the fundamentals, something as simple as YouTube can get the job done.

5. Whats the best step to getting employers to take me seriously as a data scientist with no experience?

SIDE PROJECTS! Data science, just like almost every other industry, is very much a “prove it to me” field. If you were joining a competitive video game league and a friend said they were the best video game player in the country, you would ask to see if they had any videos of their games or watch them play a game or two so you could judge for yourself, right?

Same goes with side projects in the data / computer science world. You say you can handle and manipulate data to make informative predictions? Awesome! Can you show me an example?

One great thing about side projects is that they never become obsolete. Once you complete one, you’re able to reference them forever. Doing an analysis does not expire after a certain date. I’ve completed side projects years ago that I am still referencing to this day. They’re great to put on cover letters when applying for a job to show “I would be perfect for this job and here are some projects that I have to prove it.”

The toughest part about side projects is just getting started on them. It’s like you hear all the time when someone tells you “dude, I have this great idea for an app…”. How often do you actually hear about that person going through the process to get their app created? Yeah, never. The longer you put off doing the project, the less likely it will ever get done.

However, make sure you do a project that is your own thought! Do not do an analysis on the Kaggle Titanic data set, EVERYONE has already done that one. You’re not impressing anyone with a basic analysis that every data person has gone through already. Kaggle and other data set locations are a great place to start looking for data, but always try to think of a project that will be unique to you and make you stand out from the crowd.

Data is more valuable than ever which is a main reason why it’s tough to get into the data sector. If it was easy then it wouldn’t be such a desirable job to have. There are lots of different ways into a job working with data and once you’re in…you’re in. Take the time to work on side projects that show off your skillset and give people the confidence if they gave data with sensitive information to you, you would know exactly what to do.