Here Is How A College Dropout Can Pursue A Data Science Career

Every year, a huge number of students drop out of college. It may be due to a certain mindset, a millennial trend or the education system that seems to have a notable amount of cons. Quite a few big names in the industry such as Steve Jobs, Bill Gates, Mark Zuckerberg left college before they could get their degrees, and today they are celebrated for the exceptional work they have done for the technology field. 

There might be many college dropouts today who might want to become a data scientist but are sceptical about it. If you are one willing to take that road, then the answer is absolutely yes! However, the path might be a little bumpy compared to those who have graduated with a degree. Here we list a few pointers that might help you in the long run. 

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Understand The Industry

The first and foremost thing to do is to learn and understand the industry. It can be done so by connecting with the senior, experienced folks from the data science industry. It can help you get a picture of how companies are operating, and what are the various verticals where data science is being used and how. The connections with senior and relevant folks can be made on LinkedIn, various data science forums, attending relevant events, conferences and more. 

A data science aspirant should also stay updated with all the latest and upcoming trends in the domain — whether it’s just a tool or a new algorithm or new software that has been launched. One of the best ways to do it is by joining social media groups, subscribing to data science focused newsletters, and reading a lot of resources on the internet.

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Also, if you are a dropout, you have to find out which companies are hiring data scientists without a degree but with relevant skills. And let’s not sugar coat the reality here, as we have already mentioned, the ride is going to be bumpy, so keep expectations low about getting hired by a big firm or with a large paycheque. If you look at the job postings for data science roles, many firms don’t even mention a bachelor’s degree; they jump right to a master’s or PhD in computer science, engineering, mathematics, or statistics.

However, that doesn’t mean you wouldn’t be hired. Hiring a data science is expensive and you have to take advantage of that scenario. There are many startups that cannot afford to hire a data scientist, and in that case, you can step in to support them deal with their data and be paid equally well. 

The Learning Path

Let’s be brutally honest here — many might suggest you take up courses on the internet; however, this might not work when you start your data science journey right from scratch.

No doubt, there are many online courses that are worthy of your time and money, but if you want to be a successful data scientist with real-time experience, it is advised to enrol yourself to a worthy data science training where you not only get hand hold but also get to try out projects in real-time.

While a lot of training institutes offer beginner friendly courses, many candidates find it difficult to grasp all the knowledge. So, a better way to learn is to break things down.

For example, utilise your free time to learn and polish these following skills:


Learn at least one programming language to kick start your journey and start playing with data at scale. When it comes to data science, R and Python are two strongly suggested programming languages that budding data scientists should embrace — they will never go off the fashion.


Mathematics is nothing less than the backbone of data science. Mathematical concepts aid in identifying patterns and assist in creating algorithms. Statistics and Probability Theory are two of the major elements of mathematics that play a significant role in the implementation of some of the algorithms in data science.

Presentation Skills

No doubt, data science is majorly about extracting meaningful insights out of the cluttered data. Presentation skills are extremely critical to show and make people understand your findings. You need to be a good storyteller as well. You can have a look at one of our articles on data storytelling for deeper understanding.

To know more about the skills required you can have a look at the following articles:

The Job Hunting Phase

Job hunting is the phase when you have to be really patient and calm. It gets difficult for people with a college degree to land a job, and if you are a college drop-out and newbie in the domain, you have to have patience.

As mentioned before, many companies don’t even ask for a Bachelor’s degree, they look for someone with a master’s or PhD. So, being a college dropout, if you get some real-time experience, you can definitely land a job.

Here are a few tips and tricks that you can use during a data science job hunting:

  1. Look for data science roles that don’t require more experience or where a degree isn’t mentioned
  2. Make the most out of LinkedIn. This platform could be your best friend when you look for a job. Every time you apply for a job, try to connect with the job poster and have a conversation regarding your skills and experience. It would help the recruiter to look beyond your college degree and increase your chances of getting hired
  3.  Look for startups that are hiring. Again, just like the previous point, talk to the startups and tell them how you can help them deal with their data. Working in startups not only help you gain experience with your work but also help you adapt to a fast-paced work environment
  4. You CV plays a vital role as well. When you don’t have a college degree, emphasis more on your work experience. Curate your CV in such a way that shows you have a proven track record — a significant amount of work experience even though you don’t have a college degree

Word To The Wise

No doubt, the journey to becoming a data science professional without a college degree is not a cake walk. However, by choosing the correct path, by putting in your effort on learning and gaining experience, keeping patience and by not getting frustrated during the ‘unemployment’ phase, you would definitely land a good job.

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by Vijayalakshmi Anandan

The Deep Learning Curve is a technology-based podcast hosted by Vijayalakshmi Anandan - Video Presenter and Podcaster at Analytics India Magazine. This podcast is the narrator's journey of curiosity and discovery in the world of technology.

Harshajit Sarmah
Harshajit is a writer / blogger / vlogger. A passionate music lover whose talents range from dance to video making to cooking. Football runs in his blood. Like literally! He is also a self-proclaimed technician and likes repairing and fixing stuff. When he is not writing or making videos, you can find him reading books/blogs or watching videos that motivate him or teaches him new things.

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