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The State of Data Science Master’s in 2025: The Academic-Industry Gap

The State of Data Science Master’s in 2025: The Academic-Industry Gap

Introduction

As data science continues to evolve at a rapid pace, students pursuing a career in the field face a complex set of challenges in preparing for the job market.

We surveyed students from over 100 Top Tech Programs worldwide, covering disciplines such Data Science, Business Analytics, AI & Machine Learning, and more. Our findings reveal the following:

  • Academic Preparation Gap: Only 4 out of every 10 students believe their programs have adequately prepared them for careers.
  • Rise of Supplementary Learning: 67% of Students Rely on Outside Resources for Additional Support.
  • Unprepared for Careers: 55% of students feel uncertain or unready to enter the job market.

In this report, we explore the reasons behind these trends and what students and educators can do for the future.

Research Overview

The research was conducted through an online survey distributed via email to Interview Query’s student user list. The survey included a series of questions that addressed various aspects of student academics and careers including:

  • University and Program Information: Details about their academic institutions and the data science programs they are enrolled in.
  • Self-Assessment and Confidence: Self-perceived strengths and areas for improvement within the field of data science.
  • Job and Career Readiness: Included preparedness for interviews and their career development.
  • Interview Preparation and Outside Learning: Strategies for interview preparation and the role of supplementary learning resources outside of formal education.

The Academic-Industry Divide

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The data above reflects the average number of students who responded with “Contributed” or “Strongly Contributed” when asked about their program’s contribution to their career preparation. While data programs effectively teach foundational skills like SQL, Python, and basic machine learning, they often struggle to keep pace with emerging technologies such as advanced AI systems and cloud computing. Student feedback includes:

  • “Courses covered some tools and topics that aren’t relevant in data science today.” - Paul, AI & Machine Learning Student.
  • “No exposure to cloud computing and machine learning.” - Nikhil, Computer & Information Sciences Student.
  • “Theoretical courses lacked practical application.” - Pai, Data Science Student.

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The misalignment between academic coverage and the career readiness may contribute to a confidence gap among students. When asked to self-assess their data skills, only about 40% rated themselves as confident or strongly confident. As one data science student puts it “despite mastering fundamental data science concepts and being proficient in widely used tools like SQL, Python, and Tableau, many employers still emphasize the need for expertise in specific technologies.”

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We observe a positive correlation between confidence in data science skills and the belief that their program prepared them for a data science career. This suggests that students who feel well-prepared by their program also tend to have greater confidence in their skills. This relationship highlights the importance of a comprehensive curriculum that not only teaches technical competencies but also fosters a sense of readiness for the challenges of the data science field.

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The confidence across all programs are below the score 4 (Confident), showing that students feel generally either unsure or unconfident. Among specific programs, AI & Machine Learning had the highest average skill confidence among respondents. This suggests that specialized programs—particularly those focused on emerging fields like AI & Machine Learning—are more likely to produce confident students. When students see a direct connection between their coursework and industry demands, they tend to feel more prepared.

Conversely, the Computer & Information Sciences program had the lowest average confidence. In a competitive job market filled with specialists, foundational knowledge alone is often insufficient. As one of the students from this program noted, “Many topics are taught at a high level where mastery isn’t the goal.” This gap is further compounded by a lack of emphasis on soft skills, which are essential for success in cross-functional teams. A Data Analytics student echoed this concern, stating, “The program could benefit from more focus on soft skills, such as communication and teamwork, which are crucial for collaboration.”

The Role of Supplementary Learning

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As students recognize gaps in their formal education, many are turning to external resources to enhance their learning. Our findings show that about 67% of students turn to outside learning aside from the academic preparation they receive.

Looking at the broader market, this trend is not entirely new. The job market has increasingly shifted toward self-study, often integrated with formal university education, as highlighted in a LinkedIn poll conducted by Interview Query’s CEO, Jay Feng.

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Students often note that university curricula don’t adequately cover industry-specific tools and skills. As an AI & Machine Learning student explained, “Many roles require experience with big data tools like Spark or Hadoop, or advanced statistical methods, which can feel challenging if they’re not part of my regular coursework.” Unlike university courses, which may take time to update, external courses are often more dynamic and can integrate the latest developments more quickly.

Furthermore, as a data science student mentioned, “Finding the right resources targeted for the specific company and role” can be difficult. This is an area that universities may miss as they focus more on fundamentals and theory. Aside from external learning resources, many also seek hands-on experience with coding challenges or case studies to apply their knowledge in real-world scenarios.

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Now, let’s examine any differences in approach across programs. Our graph shows that:

  • Similar to the overall trend, individual scores also show that most programs tend to favor a blended approach.
  • AI & Machine Learning students are more inclined toward academic learning, which aligns with earlier findings that their confidence in the skills they gained from their programs is closely tied to this approach.
  • Data Science students appear to be the demographic most inclined toward supplementary learning outside of their academic programs.

This trend toward supplementary learning underscores a broader reality: traditional academic models alone are no longer sufficient to meet the demands of the fast-evolving job market and the student’s feeling of readiness. To bridge this gap, students are increasingly combining formal education with self-directed learning, making this hybrid approach essential for thriving in the competitive data science job market.

Outside of this research, a lot of students in the past have shared their success in a blended approach of learning. Some of these stories include:

  • Hoda Noorian, a Data Science graduate from University of San Francisco, found that structured guidance and practice questions helped solidify her understanding and approach on product data science questions effectively.
  • Asel Wafa, a Business Analytics Master’s graduate, felt that “situations in my real interviews included the same questions I’d already done on Interview Query,” an interview preparation platform.
  • Dhiraj Hinduja, a Business Ananalytics graduate from University of Minnesota, used Interview Query for technical preparation and scheduled mock interviews.

Career Readiness and Interview Preparation

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55% of students feel either unsure or unprepared to enter the job market right now. This uncertainty stems not from a lack of technical knowledge but from the challenge of translating academic learning into the practical demands of the job market.

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We looked at the weighted average scores per major programs. AI & Machine Learning Students still show a high score, while Computer & Information Sciences show the lowest score and is the only score below the neutral score of 3.

Students often recognize a gap between their education and what employers seek. As a Computer & Information Sciences student shared, “There’s a huge gap between what the market demands and what I have learned so far in my program.” Others feel somewhat prepared but still lack hands-on experience, with one Data Science student noting, “I feel well-prepared to enter the data science job market… but I still need more hands-on experience with projects.”

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Our analysis of job preparation activities reveals a concerning trend: while students spend significant time job searching, they invest surprisingly little in interview prep—a substantial number only spend less than 5 hours per month. This misalignment is problematic, given that technical interviews require more than theoretical knowledge. As one AI & Machine Learning student said, “I don’t feel very good at interviewing. I need to put a lot of time into practice.” Another mentioned, “I need to work more on interview questions and answering quickly.” These responses highlight that technical interviews require a unique blend of speed, precision, and problem-solving skills that differ from academic problem-solving.

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Even the overall job market trend shows that the majority of job seekers may not be so active in practicing for their interviews.

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On the other hand, when students do practice, they primarily do it through coding platforms, as 52.8% of respondents indicated using this method of practice. But even well-prepared students can lack confidence in their interview readiness. One Data Analytics student from Northeastern University shared, “I have been practicing questions and watching tutorials but still feel like something isn’t working.” Another one from Georgia Institute of Technology said, “I feel like there’s something missing in my interview prep, even though I’ve practiced technical questions.” This suggests that interview preparation is not just about quantity but also the depth and quality of practice, and many students feel there’s a final “piece” missing to bridge their theoretical knowledge and the practical skills needed for interviews. Gone are the days when simply grinding through questions almost guarantees a job’s waiting for you in the market.

Struggles of Breaking Into the Job Market

When students begin applying for jobs, they are quickly confronted with the harsh realities of the job market. Rejections, often before even reaching the interview stage, can be emotionally draining. One Data Science student from San Jose State University shared, “Dealing with frequent rejections can be emotionally draining and impact self-confidence.” Another one from the University of Houston added, “The most challenging part is the lack of responses after putting in significant effort to tailor applications.” These experiences highlight the demoralizing toll constant rejections and radio silence can have on students, especially when they’ve invested time and energy into each application.

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There’s also a disconnect between the perception of abundant job opportunities and the reality of securing a position. One Business Analytics student from University of California-Davis, noted, “Many job boards post roles, but they disappear or get eliminated out of nowhere.” This frustration reflects how students struggle to get their applications noticed, despite the volume of available listings. It also highlights how competitive the job market has become, reaching a point where job openings seem to come and go.

When students finally land interviews, the process can be equally discouraging. Many report a lack of communication from employers, with one Engineering student from the University at Buffalo saying, “Getting no calls, no OAs, no interviews, nothing. Hundreds of applications and tailored resumes—zero feedback from auto-reply rejection emails.” Another Mathematics & Statistics student from IIT Delhi echoed, “Many companies don’t provide feedback due to the sheer volume of applications, leaving me wondering what went wrong.” The lack of feedback compounds the uncertainty of the job search, leaving candidates questioning how to improve their approach moving forward.

The Way Forward

While universities may now not fully cover all aspects of career preparation, they play an essential role in laying the foundation for students’ future careers. These new realities show that academia doesn’t need to shoulder this burden alone—supplementary learning has become a vital complement that students increasingly turn to as they navigate the job market while continuing their education.

One positive development is the advancement of interview preparation tools. Today, students have access to detailed guides and resources tailored to specific roles and companies, often based on real-world experiences. These resources provide actual interview questions and insights into the interview process, enabling students to better prepare for what lies ahead. Additionally, students can now engage in peer mock interviews, receive supplementary coaching from industry experts, and leverage AI-driven interview platforms that offer flexibility and accessibility to enhance their preparation at any time. These simulated environments are crucial for enhancing preparation, as they provide a realistic, low-pressure setting where students can practice their skills and build confidence before facing actual interviews.

By combining the foundational education from universities with these supplementary resources, students are empowered to bridge the gap between academia and the evolving demands of the job market. With the right tools and preparation, students can increase their readiness, gain the confidence they need, and successfully transition into their careers.