Questions we get often at Lantern Institute from prospective students are about the requirements for becoming a data scientist. More particularly, students from less technical and non-STEM (Science, Technology, Engineering, Mathematics) backgrounds are concerned how it will affect their pursuit of a career in the field of data science.

At first glance, a prospective student’s concern is quite reasonable. As expansive and continuously growing as the data science field is, it’s also a competitive one. Companies are constantly on the look-out for candidates with lots of potential that can bring value to their teams. That being said, someone with a background in computer science or engineering or statistics may not always be the best person for the job.

In this article we’ll address the apparent entry barrier for jobs in data science. And explore how anyone willing can pursue a successful career in fields of analytics and development.


Before we get into it, I want to briefly review what prospective students are likely to encounter when exploring the possibilities of a new career.

If you’re like most people, your exploration of a career path will likely include looking up the requirements for some job postings. How you go about it will vary from person to person but the result will be similar. You’ll find yourself on the careers page of a big tech firm like Amazon or Google. The positions listed there will state requirements such as MS/Ph.D in STEM, several years of related work experience and proficiency in various tools and languages.

For those without the listed degrees, this will be off-putting to say the least. Even those with the relevant degrees but lacking in experience will be demotivated by the lack of entry-level positions.

There is a reason for the high entry barrier for these positions. The biggest tech firms get applications from the most competitive candidates. Thus they have the option to be more demanding and selective. By setting your sights on those companies you’re already aiming too high and getting a skewed view of the industry.

As will become clear through the next sections, there is a better way of exploring your options. Ones that play to your strengths and use your background to your advantage.


Here’s a short interlude to bring you some numbers to back up what we’re discussing. This should also give a new quantifier for how the field stacks up.

We find out statistics in an article written by Chris Lindner for Indeed Engineering. The article discusses the levels of education, fields of study, and prior job titles of candidates applying to data science roles. The analysis is fueled by data collected by Indeed, the employment website where companies can list jobs.

As expected, the first graph shows that the majority of data scientists have advanced degrees. However, that is not the rule. Almost 5% only have a diploma and 20% have a Bachelors.

Graphs showing a breakdown of data scientists’ backgrounds. [Source: Indeed Engineering]

Secondly, and more interestingly, is the breakdown of the fields of study for data scientists. The results are quite diverse. The non-STEM fields make up over 30% (or 15% depending on how you categorize business majors).

Lastly we look at the prior job titles of data science candidates. We see from this that many entering the data science field transition from other fields or academia.

Reviewing and combining these data two things become quite clear. The majority of data scientists come from higher education and STEM backgrounds (unsurprisingly). And, a considerable portion don’t.

This drives the point that data science is not an exclusive field and is open to anyone interested. That said, the further removed you are from the norm the more you need to learn, and the smarter you need to be about your transition.


There’s no clear cut definition or regulation of what data science is. As a result companies are using the words as branding.

If you recall the dot-com bubble, you’ll know through the ‘90s adding “.com” to your company’s name was enough to cause stock prices to rise. For some, the words “data science” have taken on a similar role. This leaves the term being broad and the positions being vast.

My advice to those making the move to data scientist would be to view data science as a methodology. Data science is a process of working with data, and numerous tools to do so.

This means your goals should be set accordingly. Don’t aim for very specific or high-profile positions. Rather, aim to learn the process and become more knowledgeable. Then you can apply that knowledge to analytical jobs and work your way towards a data science position.


Just as the term data science is broad, so are the types of jobs and tasks that data scientists do. Some might focus more on software development while others focus on the business side of things.

As a prospective data scientist you need to leverage what you already know to your advantage. Look for overlaps between your field of study and the practice of data science. You’re bound to find a spectrum of positions that balance different levels of tasks. You need to specialize your skills.

Focusing on areas of overlap will also boost your hireability. Your past experience and field of study won’t be seen as a missing credential. Instead it will show that you are already intimately familiar with the industry and are now just switching to a more analytical role.

For example, suppose I had a background in business and have spent the past several years working as a salesman. Now I want to change careers and choose to pursue something more analytical and technical. My best path would be one that leverages my experience. Having spent the past with clients, I understand them and the market. That knowledge would make me a perfect candidate in a business analyst role. Especially for projects that are interested in analyzing customer data and increasing sales.


Becoming a data scientist without any relevant experience on your resume is going to be next to impossible. At the very least you need to have something to show that you are learning the principles of data science and know how to use the tools.

This can come from two forms: training, and projects. Both of these can be done through self-learning or through programs. At Lantern we find the best results come when the two are combined. Courses can be used to gain fundamental knowledge of the concepts and common practices. And then applying them on a unique project will give you the experience and functional knowledge you need.

To summarize, becoming a data scientist does not require an advanced STEM degree. You need to get some fundamental training and projects under your belt. Then you can leverage your experience and seek analytical roles in your specialty.

If you’re at the stage of getting training, Lantern Institute has you covered. We offer programs in data science that include courses in crucial topics and mentorships guided by industry professionals.

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