If you’re looking to get into data science, you might be debating on whether or not to get a data science degree.
Good news - there are a ton of available options for you whether you’re an undergraduate, graduate, or are simply looking to change careers.
And no, you don’t necessarily need a data science degree.
Before we get too far ahead of ourselves, let’s explore what a data scientist is.
Data science is an all-encompassing term for a variety of work and analytical processes that incorporate techniques from mathematics, computer science, statistics, business analysis, data analytics, and machine learning to help solve problems quantitatively.
At the core, a data scientist is someone who collects, cleans, and analyzes data to help make business decisions. They are constantly looking for meaning in the data provided to them by various business units. They look at large data sets and provide visualizations and reports to help leaders in decision making.
A data scientist might help marketing managers predict churn or help sales teams forecast revenue by analyzing historical data to predict future trends.
One example of data science in action is Netflix’s recommendation algorithm, which uses historical data of what its customers have watched in the past to predict which shows you will enjoy.
Right now, data science sounds a whole lot like a business analyst or data analyst role. You’re not wrong - these roles, up until recently, were thought to be the same.
Business analysts also help companies make business decisions by analyzing data. The difference is usually that business analysts tend to use descriptive techniques to report on what’s already happened, while data scientists use a variety of descriptive, predictive, prescriptive, and simulation techniques to help provide forward-looking guidance to decision makers.
The term “data science” dates back to 1974, when Peter Naur proposed it as a substitute term for computer science. The definition went through some cycling and in 1997, C.F. Jeff Wu suggested that statistics should be renamed to data science. It wasn’t until 2008 that the phrase data scientist came to exist as a title.
Most data scientists rely on a variety of skills and techniques to do their job, including:
Until recently, there were no data science degrees. But as universities started seeing how quickly the field was beginning to grow, many of them started offering masters of science in data science.
We’re going to dive deeper into how to become a data scientist today, what the best degrees are for undergraduates and graduates interested in data science, and the various online courses available to learn data science.
There are three groups of potential data scientists:
How you become a data scientist highly depends on your previous background and work experience. If you’re a student, you can tailor your undergraduate degree to learn the skills needed for data science.
If you don’t want to or cannot earn a data science degree, your dreams of being a data scientist are not over. There are plenty of online learning opportunities and even Google’s new certificates.
But, in reality, only around 12% of data scientists worldwide have only completed an undergraduate degree prior to entering the data science field. That means many have masters of science or doctoral degrees in the field.
While data science as a bachelor's degree is rare, there are still degrees that set you up for a data science career. A data scientist benefits from a degree in:
Each of these degrees should give you the foundational knowledge needed for a data science career. They will also give you a unique perspective of data visualization, data analysis, and artificial intelligence based on which one you choose.
However, if you really want to stand out in the job market, you’ll want to level up by expanding your knowledge of SQL, Excel, and with different data analysis tools. Tools often require on-the-job knowledge and training so if you check them out early and build a foundational knowledge, you’ll set yourself up for success.
And while the industry is currently highly technical, there is a move towards no-code data science. If you can use a tool to run analyses and predict insights, why wouldn’t you do that versus manually doing it? Work smarter, not harder!
As we said earlier, most data scientists have advanced degrees from a masters program. But, what’s great about getting a data science degree is that you can take courses from anywhere in the world. Many degrees are online masters programs - and many of these are from pretty prestigious universities.
A master of science degree in data science often doesn’t require may not even require a GRE. You’ll learn skills like data mining, machine learning, Python, R, other programming languages, and technical analyses.
A degree program or certificate might be the best option if you're confident you want to be a data scientist for life.
Before you enroll in an undergraduate or graduate program, you should check out the various online data science courses. These data science programs can range from a few months to over a year.
From General Assembly to Coursera and Harvard, they all offer some level of data science courses. If you already have a foundational knowledge, these are a great place to start on your data science journey. If you don’t have any experience in data science, machine learning, or big data, you might find yourself having to put in some extra work to be successful.
Here at Apteo, we believe in democratizing access to data science. We believe everyone on every team should have the tools and skills necessary to run analyses and understand their data.
That’s why we’ve built our no code analytics platform featuring predictive analytics that requires no coding, SQL, or Excel knowledge. We highly recommend learning no code platforms even if you plan to get a data science degree as they are the tools of the future! Again, why work harder when you could work smarter?
Shanif Dhanani is the co-founder & CEO of Apteo. Prior to Apteo, Shanif was a data scientist and software engineer at Twitter, and prior to that he was the lead engineer and head of analytics at TapCommerce, a NYC-based ad tech startup acquired by Twitter. He has a passion for all things data and analytics, loves adventure traveling, and generally loves living in New York City.