How To Become a Data Scientist Without a Degree | 8 Steps

Are you interested in data science but don't want to spend money on a degree? Fortunately, you can still become a data scientist without one. You can learn about essential decision-making and technical skills through various online resources, such as online data science certification programs.

Study programming languages, data engineering fundamentals, and artificial intelligence by enrolling in free online data science courses. Many courses include career certificates you can share with your resume or LinkedIn profile.

Can I Become a Data Scientist Without a Degree?

Yes, you can become a data scientist without a degree. Many data science jobs and internships don't require degrees. Hiring managers generally focus on whether you have the skill set they want. Having a robust portfolio is crucial to standing out in job searches. You can complete data science projects and feature them on your portfolio, or your resume.

While a data science degree is not required, you should consider earning one at some point in your career. You may be able to get employer tuition reimbursement and other financial aid to help you pay for a degree once you gain some experience.

Read more: What Can I Do With a Data Science Degree?

Steps To Working in Data Science Without a Degree

According to the U.S. Bureau of Labor Statistics, employment of data scientists is projected to grow 36% from 2023 to 2033. The job market demand for data scientists is high, but so is the cost of higher education.

Thankfully, recruiters emphasize the need for candidates with demonstrated data science skills rather than simply holding a degree. You can learn problem-solving and software engineering competencies through online resources (e.g., data science bootcamps).

Here are the steps to working in data science without a degree.

Step 1: Build a Foundation in Mathematics and Statistics

A strong math foundation is essential in data science for several reasons. Some of these reasons include:

• reliance on statistical analysis
• data interpretation
• algorithm development
• quantitative reasoning

You can improve your math and statistical skills through online learning platforms like Khan Academy and Coursera. Both options offer a library of videos, courses, and certificates that you can study from home.

Step 2: Learn Programming

Data scientists must be well-versed in various programming languages. These coding languages include Python, R, SQL, and Java. Some great resources for learning how to code are:

• Online learning platforms: Many Coursera, edX, and Udemy courses teach learners how to code.
• Interactive Coding Websites: Websites like CodeAcademy and KhanAcademy provide lessons on programming languages.
• Video tutorials: YouTube channels like Net Ninja offer free tutorials on several coding languages.

Several companies hire candidates who are proficient at coding and have a deeper understanding of programming languages. As the field of data science expands, the demand for these skills will rise.

Step 3: Develop Machine Learning Skills

Machine learning and artificial intelligence are becoming more prevalent in data science because of their usage in data analysis and predictive modeling. Machine learning algorithms can handle time-consuming tasks (e.g., data cleaning and model evaluation).

Study Algorithms

You can study algorithms by reading books like ''Introduction to Algorithms'' by Thomas H. Cormen or taking a free course online. Put time aside every day to research algorithms and data structures.

Gain Hands-On Experience

Gain hands-on experience by coding basic algorithms, implementing data structures, and working on projects like a small application. These projects can be added to your portfolio to display your abilities.

Step 4: Master Data Visualization

Mastering data visualization takes many hours of study and commitment. Before learning advanced techniques, you must understand data types and principles. A great basic data visualization tool is Excel. This software can help you comprehend the basics of chart types like bar charts and line graphs.

Learn Tools

Powerful learning tools like Tableau and Power BI are popular among data analytics professionals. Tableau is a business intelligence tool that analyzes data and creates shareable dashboards. Power BI is a Microsoft business analytics tool that visualizes data and shares insights with organization members.

Practice Data Storytelling

Data storytelling uses data to create a narrative that communicates findings to others. Much of your job as a data scientist will be delivering data findings to executives. Key aspects of this process are:

• narrative structure
• context and relevance
• clarity and simplicity
• integration of qualitative and quantitative data

You can practice data storytelling by compiling data from sites like Kaggle and Google Dataset Search. Identify your stakeholders, visualize the data, and present your story aloud.

Step 5: Build a Portfolio

Building a portfolio is vital to landing a data scientist job. Employers want to see that you can back up the skills you mention in your resume. Aim to include at least three to five projects in your portfolio.

These projects should showcase your programming language knowledge and have an outlined approach. Cite any models or algorithms you used and the objectives of each project. Host your projects on repositories like GitHub or GitLab.

Step 6: Network and Engage with the Community

Networking in your community may help you get a job since the people you know could recommend opportunities. Some ways to network with others include joining online communities, attending industry events, and seeking mentorships.

Volunteer opportunities and internships can also help you meet professionals in your field. Who you know is just as important as what you know. As you navigate this new career path, build a network you can lean on for support.

Step 7: Apply for Entry-Level Roles

Look for entry-level positions when you feel confident you have built a good enough network and portfolio. Entry-level data science roles will help you gain more industry connections and skills. During your search, tailor your resume and cover letter to highlight skills that match the job description.

Prepare to take technical assessments to prove your skills and advanced knowledge in data science. Familiarize yourself with common data science interview questions, including case studies and scenario-based questions. Conduct practice interviews with a peer or mentor and get their feedback.

Step 8: Pursue Certifications and Bootcamps

In addition to studying online courses and video tutorials, consider pursuing a certification or bootcamp. These options offer a structured learning path with hands-on experience and networking opportunities. Some helpful data science bootcamps include:

• General Assembly
• Springboard
• DataCamp
• Thinkful

Research each bootcamp to determine which one is a good fit. Data science certifications can also advance your career and boost your resume. These flexible programs typically have industry-relevant curricula to enhance your professional credibility. Certifications to consider include:

• IBM Data Science Professional Certificate
• Microsoft Certified: Azure Data Scientist Associate
• Google Data Analytics Professional Certificate
• HarvardX Data Science Professional Certificate

Start Your Data Science Career

While you can become a data scientist without a degree, having one can enhance your career prospects. A data science bachelor's degree will build upon the skills you learn online and add to your professional credibility. Contact one of the featured institutions on Learn.org for more information.