How Can Higher Education Better Prepare Students to Enter the Data Science Field?
If you look at the LinkedIn profile of a data scientist, you’ll likely scroll through years and years of education experience — both higher and continued education. In a study by Indeed’s engineering team, 75% of data scientists had advanced degrees (either an MBA or a Ph.D.).
Based on all of these years of schooling, you may assume that those entering the data science field are more than ready to thrive in a professional setting. However, in recent research, almost half (40%) of students said their biggest obstacle to obtaining their ideal data science job was experience, and a quarter said they lacked the right technical skills.
There is a gap between what enterprises need from their data scientists and how universities prepare students. Interest in data science and related fields is continuing to grow — successfully preparing the next generation of scientists is critical.
Based on our research and conversations with our employees, here are our recommendations for how higher education could better prepare students to enter a professional setting.
Stay up-to-date with relevant technology skills
A career in data science requires mastering foundational skills, like mathematics and programming, while also keeping pace with rapidly evolving technology. In addition to expert technical skills, the discipline also demands that practitioners have fluency in a wide range of tools and technologies, from data management to visualization tools, familiarity with open-source packages and libraries, and even DevOps.
Two of the most frequently cited skills gaps among respondents working in enterprise environments–big data management (38% of respondents) and engineering skills (26%)– do not rank in university programs’ top ten skills
“Right now, the goal of higher education is not really to prepare students for the data science workforce. It is to build a foundation in skills like mathematics or computer science, which are super important and help students develop their independent thinking skills. The intent is to set them up for any relevant career path,” said Sophia Yang, one of our senior data scientists. “One thing professors and institutions could do to better prepare students for data science careers is to update their courses to include the most current technologies. Technologies change all the time, and professors should update their courses more often.”
Multiple Anaconda employees echoed this sentiment that universities don’t use or teach the most up-to-date technologies. Universities and professors must make an effort to keep pace with the overall industry.
Improved support for non-academic career paths
In our conversations with Anaconda employees, most remembered wanting to be a professor “when they grew up.” And, indeed, some of them have spent time teaching. A career in academia is a popular path for those in technical fields of study. But it shouldn’t be treated as the only option.
“Professors tend to think that they can’t help students unless they are interested in an academic career. In addition to that, there can be a disconnect between the skills taught in school and industry needs. Students often have to bootstrap themselves,” said Albert DeFusco, a data scientist on our product team. “Professors and institutions need to provide resources for students who are interested in industry careers — point them toward specific extra courses, network connections, or outside certifications to help with industry roles.”
What would additional resources and job search services look like?
Job offer negotiation
Alumni mentorship programs
Partnerships for third-party certifications
Educational institutions and employers should work together to ensure that students know how to approach and navigate the job market and search process. Networking and mentorship opportunities, in particular, present a helpful way for universities and enterprises to work together.
Facilitate real-world experience
Education is great, but companies that are hiring for data science roles will be looking for real-world application of these skills, which can be a barrier to entry for those just getting started in their careers. As mentioned, students that participated in our research reported that their biggest obstacle to finding a job was experience (41% of respondents).
Universities should provide different options for students to put their training into practice.
“Course projects and internships are important. Many of the courses I took had final projects that required students to use methods and technologies to solve problems, which was a great practical experience,” said Sophia. “Internship opportunities are similarly helpful to learn new fields. More internship opportunities would better help students prepare for the industry career path.”
Course projects, internships, and practicum programs can address this experience gap. Good experience doesn’t just test technical skills; it prepares students for the harder-to-teach soft skills. Serving as a “data translator,” demonstrating business impact from their work, and influencing colleagues cross-functionally are necessary when navigating a business organization.
Universities that also facilitate open-source projects can give students a leg up. Open-source projects allow students to make real, meaningful contributions, network with a community, and be a part of exciting work. As interest in the data science field grows, we’re seeing more and more universities offer open-source programs or labs for students.
The skills and experience needed to succeed as a data scientist today will not be the same as those required in five or ten years. The worlds of education and business must collaborate to best prepare students to enter the data science workforce.
If you’re looking to pursue a data science career, be sure to check out our whitepaper with foundational skills to help you be successful.