Codio’s 2025 Data Science and Analytics Talent and Skills Survey analyzes the current landscape regarding data analytics talent acquisition, development, and management across various industries. We gathered responses from 111 senior executives responsible for hiring and overseeing data science and analytics teams.
Our findings reveal a persistent gap between the skills employers seek and those candidates possess.
Technical competencies in statistical analysis, programming languages, and data manipulation remain highly valued yet challenging to find in the talent pool. Equally concerning is the shortage of essential nontechnical skills, with communication abilities and problem-solving capabilities often cited as lacking among candidates.
Addressing the talent gap becomes increasingly urgent as the demand for data talent grows. Educational programs can help bridge the gap by integrating opportunities for students to gain practical experience, such as real-world projects, which employers highly value.
Additionally, increasing collaboration between industry and data education, training, and workforce development programs enables programs to tailor curricula to industry needs so students are job-ready from graduation day.
The Data Skills Organizations Lack
Many data analytics employers say their organizations lack critical technical skills. Among new hires, foundational skills like statistical analysis and modeling, knowledge of programming languages, and data manipulation and cleaning are the most lacking.
The Most Difficult Skills to Recruit For
Additionally, the most lacking skills are equally challenging to recruit and hire: statistical analysis, data manipulation, and knowledge of programming languages.
The Technical Data Skills Employers Value Most
The importance of data manipulation and cleaning, knowledge of programming languages, and statistical analysis and modeling skills becomes even more apparent as executives ranked these skills as their most valued when evaluating potential new hires.
These foundational skills are among the most lacking within organizations, the hardest to recruit for, and the most valued in new candidates, underscoring their cruciality within training and education programs.
The Non-Technical Skills Employers Value Most
Shifting now to non-technical or “soft” skills, our survey respondents emphasized the importance of strength in problem-solving, critical thinking, and written and oral communication, among other skills. Data executives are increasingly looking for professionals with technical expertise who can deftly navigate complex environments and collaborate effectively with teams. A well-rounded skill set in the data analytics workforce is imperative.
The Importance of Practical Experience.
Practical experience is crucial for new hires, yet over 56% of employers lack it. Our survey found that newly hired data talent lacks essential familiarity with industry best practices (57%) and up-to-date technical knowledge (56%). Real-world projects, internships, and case studies rank among the most valued forms of practical experience, suggesting that integrating more hands-on opportunities into education and training programs is key to bridging the gap and preparing new professionals to meet industry expectations.
Challenges for Data Science & Analytics Education Programs
Tech stack complexities, learner frustration, and increasing learning technology costs impede efforts to scale enrollment and drive better completion rates. The range of post-secondary data science and analytics programs—from traditional universities and community colleges to bootcamps, workforce development programs, and online universities—face significant operational hurdles that can limit their effectiveness.
Recommendations
- Align learning objectives with industry needs: Provide a comprehensive curriculum that includes essential data analytics skills—such as data wrangling, visualization, and statistical analysis—while incorporating specialized topics like predictive modeling and big-data tools to ensure graduates possess competitive, real-world competencies.
- Enhance hands-on learning opportunities: Utilize a learning experience platform (LXP) to seamlessly provide practical learning opportunities for mastering essential skills like Python, SQL, foundational machine learning, and emerging technologies like AI.
- Tailor curricula for industry-specific contexts: Different industries have unique data challenges. Aligning curricula with specific sectors helps learners apply their skills more effectively in real-world situations.
Want to explore the survey analysis further? You can download the full report here! Or schedule a call with a member of our team to discuss how Codio can help you enhance your data courses to better prepare your students for the workforce!
