Do You Need a Master's Degree for Data Science Jobs? (Updated in 2025)

A master’s degree in any field often correlates with a marginally higher salary, averaging around $1,700 per week; though it is typically associated with a lower employment rate compared to a bachelor’s degree. However, data scientists stand out as an exception, earning a median salary exceeding $2,000 per week, regardless of whether they hold a master’s degree.

Despite the diverse career backgrounds of data scientists, the question of whether a master’s degree is necessary for securing data science jobs remains a topic of debate.

Our answer would’ve been different if you had asked us 10 years ago, but in our recent experience with candidates interviewing for both entry and advanced-level data science jobs, the answer is no. While there exist a few niche cases where a master’s degree may be relevant, it’s not worthwhile for most.

If you’re still exploring your options, here are some reasons to consider not pursuing a master’s degree in data science. For those leaning toward earning the degree, we’ve got something for you, too, at the end of the article.

Here are the reasons behind not considering a data science master’s degree for the sole purpose of landing a decent job:

A master’s degree doesn’t help much in getting an entry-level data science job

Data science is a technical skills-focused field rarely prioritizing master’s degree candidates over practically skilled ones. Many companies prioritize practical experience, problem-solving abilities, and a strong portfolio over formal education.

Most entry-level data science roles focus on technical skills like Python, SQL, machine learning, and statistics rather than academic qualifications. These skills can be acquired through online courses, bootcamps, or self-directed learning at a fraction of the cost.

For example, a master’s in data science degree from a recognized university can cost anything from $30,000 to $80,000, whereas online courses and bootcamps typically cap at around $15,000. Internships and real-world projects, moreover, are free of cost.

The opportunity cost of gaining experience is more significant

As mentioned, employers mostly value work experience over academic credentials, especially in dynamic fields like data science.

As an alternative to securing a data science master’s degree, those 1–2 years could instead be used to gain real-world experience. Internships, freelance projects, or entry-level roles in related fields, such as data analytics or business intelligence, often provide faster paths to career growth.

You don’t need a master’s degree to perform typical data science duties

Typical data science duties, especially in entry and mid-level roles, don’t require a master’s degree to perform efficiently. You can acquire core data science skills, including data cleaning, analysis, visualization, and model building, through high-quality learning materials available on platforms like Interview Query and Coursera.

Mastering tools like Python, SQL, statistical analysis, and ML algorithms mostly comes with experience and not theoretical knowledge. Moreover, most job postings for data scientists list a degree in a relevant field as preferred but not mandatory. Experience, certifications, and a solid understanding of the field often suffice.

You don’t need a master’s degree to get promoted in the data science field

Promotions in data science are typically driven by the ability to deliver impactful insights, build robust models, and solve complex problems. Success in this field is measured through tangible outcomes rather than academic qualifications.

Moving up in data science often requires strong communication, leadership, and collaboration skills. The ability to translate data-driven insights into actionable business strategies is a key factor for career advancement, and these skills are developed through experience rather than formal education.

There is always time to complete your master’s degree later

There’s no age limit for pursuing further education, especially in data science, where transitions from different fields are common. Starting your career earlier allows you to assess whether a master’s degree aligns with your goals before committing time and resources.

Data science master’s degrees often focus on specialized learning paths within the field. Many mid-career professionals choose to return to school once they identify a need for advanced knowledge or leadership training.

Furthermore, some employers offer tuition reimbursement or sponsorship programs for employees seeking advanced degrees. By working first, you might reduce or eliminate the financial burden of graduate education.

Requirement of significant time commitment

Completing a master’s degree requires a substantial time commitment, which may not be feasible for everyone, especially those with family or financial responsibilities. As mentioned, online courses or part-time bootcamps offer more flexible alternatives for acquiring relevant skills.

For instance, securing a full-time data science master’s degree from the University of Washington will take 1.5 to 2 years to complete, while their part-time program requires an average commitment of 20 hours per week, taking approximately 2.5 years to finish.

There are a few reasons, especially when you’re seeking a more advanced role in the data science field, for considering a master’s degree:

Transition from an unrelated field without prior technical expertise

Given the number of individuals who transition to data-driven roles mid-career, a master’s degree in data science can serve as an excellent bridge for you moving into the field from unrelated domains, especially for those without a technical background.

Many data science programs offer foundational courses that cover key areas like programming, statistics, and machine learning, helping individuals gain the necessary expertise to enter the field.

For example, a non-technical professional in finance might pursue a master’s degree to gain the quantitative and coding skills required to thrive in data-driven roles.

Access higher-level roles that explicitly require advanced degrees

Many—absolutely not all—senior positions in data science, such as data science managers, machine learning engineers, or AI researchers, explicitly require advanced degrees. These roles often involve complex problem-solving and leadership responsibilities, where an in-depth academic understanding of algorithms, data structures, and statistics is essential.

Some companies or research institutions may prefer candidates with a master’s or PhD for leadership roles or research-focused positions since these often necessitate both practical and theoretical knowledge.

Build a strong academic foundation for research or PhD studies

For those interested in pursuing research in fields like artificial intelligence, machine learning, or data analytics, a master’s degree offers a solid foundation. It can prepare you for PhD programs by providing research opportunities, exposure to cutting-edge methodologies, and a deeper understanding of theoretical concepts.

Networking and Resources

The most practical use for pursuing a master’s degree in data science comes down to graduate programs providing access to robust professional networks, including professors, fellow students, and alumni who can open doors to job opportunities, internships, or collaborations. These programs also often have strong ties with companies and industries that regularly hire graduates.

Universities provide resources like career services, job fairs, research projects, and internships, which can enhance your resume and professional experience. If you lack university experience, IQ has a robust community of successful candidates willing to help newcomers.

Well, getting a job requires building your experience and credentials. Think about that $50,000 you would spend on a master’s as an investment toward improving yourself. Find more about becoming a data scientist here.

If you are someone who doesn’t have specific skills and wants a data science job, then that $50,000 should be funneled into educational resources to build your foundational knowledge. You’ll probably have to start lower on the totem pole than someone with a master’s in data science, as employers are looking for people who have already built up some of the necessary skills. If you have a degree in a related field, apply away!

However, if you have skills and feel comfortable outside of a structured learning environment, or if you’ve picked up the fundamentals and just need to refine them, it’s probably worth investing that money elsewhere.

While a master’s degree in data science can open doors for those looking to specialize, transition from an unrelated field, or pursue higher-level roles, it is not a necessity for most data science jobs. Many entry and mid-level positions prioritize practical experience, technical skills, and problem-solving abilities over formal education. For those just starting out, online courses, bootcamps, or self-guided learning can provide the necessary skills at a significantly lower cost. With the right mix of hands-on experience and continuous learning, a master’s degree may not be required to succeed in the dynamic field of data science.