How to Start a Career in Data Science Without a Degree

It is safe to say that data currently rules the world. Today, companies continually need data scientists to help them analyze, separate and extract the necessary information from big data. The information data scientists extract helps with problem solving and innovation.

While a career in data science may interest you, your lack of a relevant degree to support your professional research may mitigate your interest. Here are seven proven ways to start a career in data science without a degree.


What is data science?

Data science involves the use of analytical skills and scientific techniques to extract information and insights from raw data. Data scientists can bring information and knowledge from all forms of data, both quantitative and qualitative. Next, they apply this extracted information to problem-solving, decision-making, and development strategies.

Today, data is a huge engine of the global economy, so data scientists are and will be in high demand for the foreseeable future around the world. Starting a career in data science without a degree isn’t that difficult. Here are the steps you need to take to kickstart your data science career:


1. Learn the fundamentals of data science

To start a career in data science without a degree, you must have adequate knowledge of several relevant core disciplines. Data science derives from fundamental disciplines such as mathematics, statistics and computer science.

You can read books, watch YouTube videos, and take physical and virtual classes to give you a basic understanding of these disciplines and increase your knowledge. Additionally, your knowledge of math would help you create machine learning algorithms and discover patterns.

Furthermore, statistical experience is useful when reviewing and analyzing data, allowing reasonable conclusions to be drawn. Understanding computer science will also prepare you for machine learning, an integral aspect of data science.

You can check out this YouTube video for an introduction to machine learning, for example:

2. Take data science courses

The next step after learning the basics is to take specialized data science courses. These allow you to better understand how it all fits together in the field of data science. Thankfully, there are many great courses on the subject, which give you the information you need to help you understand data science and its associated careers.

You can access huge open online courses (MOOCs) from providers like Coursera, Udemy, and edX. The knowledge gained from these courses will prepare you for a thriving career. Some courses to get started include:

3. Obtain a Data Science certificate / certification from credible certification bodies

You can take data science courses without obtaining the required certificate. However, free courses like this aren’t enough if you want potential employers to take you seriously. When you get certified from your completed courses, they provide proof of your knowledge and skills to potential employers. Some of these are:

4. Practice the data science skills you are learning

Careers in data science are not theoretical. They involve practices. So, as you learn in theory, you need to put these skills into practice through hands-on experiences to help you obtain and improve them.

Additionally, while many courses will provide you with practical assignments, you should still put the extra effort into undertaking internships and personal work projects. You can also look for data science assignments that are suitable for beginners in the field.

5. Develop other technical and interpersonal skills

Every career requires a combination of skills, and careers in data science are no exception. Ideally, you need to possess analytical, critical thinking, problem solving, intellectual curiosity, team spirit, and communication skills.

Additionally, you need to develop solid computer programming, data management, and visualization skills. You should note that learning these skills takes time, and you may learn some faster than others, although you can only improve them through constant practice.

6. Build a solid portfolio of projects

It would be helpful to tailor your portfolio to fit the exact data science career you’re interested in. For example, if you are interested in machine learning, your portfolio should reflect projects involving machine learning. You can build your portfolio by working on personal projects, including case studies that apply to specific issues that interest companies.

You can also apply for internships at companies willing to take data science interns for short-term projects. Also, you can decide to work for free for friends who own business and non-governmental organizations. Some organizations you can apply to for internships and jobs are:

7. Apply for jobs in your data science niche

It would be pointless to go through all the processes to become a data scientist if you can’t find a job. You deserve to get a well-paying job after the rigors of learning, building skills, and earning certificates. It will be easier to find work based on your niche if you are specific about the data science career that interests you.

You can use some design tips to enhance your portfolio, prepare your resume, and post them on LinkedIn and other social media sites where they might be potential job recruiters. You may need to search for potential recruiters and hiring managers, apply for unlisted jobs, and send cold emails.


8. Prepare for data science interviews

Before and after applying for a data science job, you need to properly prepare for data science interviews. While data science interviews are unique, with questions spanning the behavioral and technical spheres, some common job interview questions that apply across the board are also included.

Some tips to help you prepare for an interview include finding the company you applied to and preparing to test your technical skills. Also, if your interview is virtual, you may want to check out these tips on how to stay focused during an online interview. You should also learn to confidently discuss salary ranges with your interviewers.

If you’re not sure how to answer questions on the spot, you can research possible questions and practice how to answer them so they don’t throw you off balance. You can also look for possible questions to ask your interviewer. Asking questions during the interview indicates your interest in the role and indicates your commitment to learning and improvement.

Start your data science career now in data science

Careers in data science are both well paid and highly sought after. Since you don’t need a degree to enter the field, the steps listed above will help you start a career in data science, excel at it, and earn handsomely.

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