How to Become a Data Scientist Without a Degree?

Are you full of energy working with data and eager to learn about the world of data science? Do you need a degree to become a data scientist?

You should be able to become a good data scientist without a degree with the right talents and skills. This article will look at the guides on how to become a data scientist without a college degree.

If you are looking for a job, a college degree will significantly help you. But what’s vital is how you work with data, not whether you have a degree.

The strategies I provide below will quickly help you become a scientist without a data science degree. Keep reading for more details!

1. Gain Prerequisite Knowledge

Experts in this field recommend gaining basic knowledge if you want good career advancement. We all know that data science is a broad field rooted in topics including math, statistics, and computer science. So, starting with these will help you understand the fundamentals.

In addition, you should learn calculus, probability, discrete math, and linear algebra to find out trends and make the best forecast for the project.

Finally, perfect your skills in R and Python, as they are the two most popular languages in this field of data science.

2. Learn the Data Science Foundations

The next strategy to becoming a data scientist is to learn the data science basis. So what exactly is data science? The job uses technical and analytical skills to extract information and analyze data.

Essential factors in this area include data extraction and transformation to make accurate predictions and measurements. So, each one is isolated and requires individual mastery and skills.

Finally, storytelling is an advanced aspect you should pay attention to while teaching yourself data science. To succeed with this skill, I suggest useful support tools like matplotlib, ggplot2, and seaborn.

3. Take Data Science Courses and Get Certified

Online course

Certifications related to data science will be proof of your ability to work. To get a certificate, you must take courses and pass a basic skills test.

You can take offline or online courses at reputable training institutions. For example, Udemy, Coursera, and edX are well-known online course providers to keep you updated with the latest field knowledge.

These platforms offer learners a variety of options. Depending on your major, you can choose the appropriate course accordingly.

Some courses have end-of-course exams to test your ability and give you a certificate. These qualifications will be a plus for you to apply for better jobs easily. It will convince employers to pay more attention to your CV than others.

4. Work on Live Projects

All HRs appreciate candidates who have handled real projects. So, working on live projects will help you learn how to apply theory to solve possible situations.

Many businesses are willing to accept interns, opening up work opportunities for beginners to hone their skills.

Thus, you can apply for internships at companies or NGOs. Training at big businesses like Amazon or Microsoft will help you have a more impressive CV.

5. Build Other Technical and Soft Skills

Build your hard and soft skills

Specialized skills such as computer programming, data processing, and visualization are indispensable. Yet, it will only be enough if you possess these complex skills. Becoming a data scientist will be very difficult if you do not have useful soft skills.

The ability to think analytically and critically is something that you need to practice to become better at work. You must have good communication skills to convey your ideas to your colleagues. Team spirit, teamwork skills, and high responsibility will be factors to help you go further on your career path.

6. Build a Portfolio and Apply for Jobs

After studying, training, getting certifications, and practicing to gain the necessary experience, you can confidently write your CV.

It would help if you referred to how to write a CV by sharing blogs or social networks to complete your resume in the best state. With your knowledge and skills, your CV will be complete and make a good impression on employers.

You can apply to be a data scientist at big companies. If you pass the interview and get hired, you will have the job you want with a high income and benefits.

Skills to Help You Become a Data Scientist

These skills will help strengthen and broaden your understanding of the job. Mastering them helps you upgrade yourself to be a successful data scientist.

1. Statistics

You may need more than the data science basics program to work at large enterprises. Tasks that a data scientist requires you to know more than that.

First, you should read more documents on logic, data, and theorems to graph the interaction between data. Mastering computational skills helps you to analyze and model the metrics you have to deal with.

2. Mathematics

Besides, math is the field that underlies all IT-related disciplines. So, data scientists need deep learning in math, probability theory, and statistics to analyze the information.

3. R/Python

Proficiency in programming languages will be a plus for your workload. Among programming languages, R and Python are the best tools for data processing. Once you master either of these, it will be easier to become a data scientist.

4. SQL

In addition, you can also learn more about SQL database query language. SQL is a computer language allowing you to build, repair, process, and extract data from existing sets.

You can find learning tips at specialized data governance websites. For example, the expert R, Python, and SQL documentation will also provide valuable guidance for using the tool well when dealing with real-world problems.


Becoming a data scientist does not require you to have a formal degree. Yet, this job requires you to hone a lot of relevant knowledge and master both technical and soft skills.

I hope you got the answer after reading this article. This career path will have many troubles, but as long as you drive forward and work hard, you’ll accomplish a well-deserved victory.

Thank you for reading!