The skills gap is problematic for job seekers and the economy alike—especially in the data science and analytics field.
In fact, 95% of employers say data science and analytics skills are hard to find among candidates, according to a report by Business Roundtable.
In an effort to balance the mismatched job market for data skills, PwC joined forces with the Business-Higher Education Forum (BHEF) to publish a report highlighting four ways higher education can take advantage of the opportunity to train workers in this field:
1. Teach students to collaborate
PwC and BHEF point out that data scientists often work with a range of stakeholders and need to understand a variety of approaches to solving problems.
For example, the report explains how data scientists in the health care field must consider how their work will affect constituents across the board, including doctors, nurses, patients, and administrators.
To teach this approach to data science, the authors encourage colleges to create programs that give students the opportunity to study in different disciplines, work in teams, and tackle real-world problems.
Soft skills are just as important as technical skills for data scientists
2. Teach data science in all fields
Data science and analytics remain a relatively niche area of study at colleges. PwC and BHEF cite a Gallup poll in which only 21% of university presidents and provosts at institutions offering data science courses said work in the field was required of all of their math and science majors.
The report authors argue that every student should be exposed to data science and analytics.
"All majors should require some foundational knowledge of analytics and the data science process," the report states.
In a panel of industry and educational experts hosted by The Atlantic last week on "Data Analytics and Tomorrow's Workforce," Katherine Rowe, the provost and dean of faculty at Smith College, shared how she blends data science into her liberal arts classes.
In a Shakespeare course, Rowe asks students to use data science and analytics techniques to chart, quantify, and analyze Shakespearean vocabulary as it appears in today's dictionaries. "Data sciences match so well with liberal arts since it's creative and critical thinking with data," Rowe said.
Schools can also offer access to data science coursework through minors and certificates, so that students can pursue these areas further while retaining their primary course of study.
3. Partner with professional organizations
It's easy for colleges to fall into a trap of almost—but not quite—preparing students for jobs in data science and analytics. The report recommends ensuring your students have the last-mile skills and knowledge they need by partnering with professional organizations, such as those in math, engineering, humanities, health, or finance.
These organizations can help colleges integrate data science into other coursework, create experiential learning opportunities, and understand the future of data science and analytics careers, according to the report's authors.
Outside vendors have the common data language your students will need to articulate. Here's the smart way to approach a partnership.
4. Make data studies accessible to diverse groups of students
Only 12% of presidents and provost respondents in the Gallup survey agree that data science courses "attract more underrepresented minority students than other STEM courses," the report points out.
To address this lack of diversity in data science, the report authors encourage schools to:
- Train faculty to introduce data coursework into their classes;
- Create pathways to data science from more diverse programs; and
- Bring a little fun into introductory data courses to make them more popular with students.
(Arnett, Education Dive, 3/31; PwC report, accessed 4/3; Atlantic Live Forum, 3/30).
Soft skills, hard skills, missing skills—More ideas for tackling the skills gap
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