Dec 05 2017
Data Analytics

3 Aspects of a Good Data Science Education

Researchers look to develop a universal way to train students to analyze data.

College grads looking for a successful career should get to know data. For the second year in a row, job website Glassdoor has named data scientist the top career because of the salary, the number of job openings and satisfaction rating, THE Journal reports.

As the world continues to turn to data analytics to help make business decisions, determine success and optimize nearly everything, the need for college graduates who can make sense of data has become even more important.

So, what makes a good data scientist? The National Academies of Sciences, Engineering and Medicine is aiming to figure that out. With the release of a new report, NASEM is seeking to define what universities must consider in the creation of a data science program. In addition, NASEM will continue to research and hold webinars to create a solid definition by 2018 in order to make data science education a universal experience.

“Current data science courses, programs and degrees are highly variable in part because emerging educational approaches start from different institutional contexts, aim to reach students in different communities, address different challenges and achieve different goals,” reads the report.

Here are three aspects that go into a good data science education, courtesy of the NASEM report:

1. Students Must Develop Data Acumen

To begin to construct a data science curriculum, the NASEM urges leaders to look at the work cycle of an actual data scientist. Following this guideline, the data science work cycle should include the following six steps:

  1. Data description and curation

  2. Mathematical foundations

  3. Computational thinking

  4. Statistical thinking

  5. Data modeling

  6. Communication, reproducibility and ethics

After this process, students will develop data acumen, or the ability to know what to do with data.

“This trait is increasingly important, especially given the large volume of data typically present in real-world problems, the relative ease of (mis)applying tools and the vast ethical implications inherent in many data science analyses,” reads the report.

2. Apply Data Implications to the Real World

For students to grasp exactly how data would have an impact in the real world, NASEM suggests that educators have students work with real data so that they will have no trouble applying what they’ve learned in the classroom to a career.

One example of this occurs at Southern Connecticut State University, where students take part in an internship program with local businesses. The student interns use IBM Watson to analyze real data from the business leaders to create efficiencies and drive success.

“We changed the dynamic [of an internship]. Now the interns bring expertise and experience to the internship that a business doesn’t have on its own,” says Michael Ben-Avie, who helps run the SCSU internship program and is the director of the office of assessment and planning.

3. Data Governance, Ethics Rank as Highly Important

If students are using real data in their learning, it’s integral that they understand the security and ethics implications that go hand in hand with using data.

NASEM recommends that students learn the following regarding data:

  • Fairness, or avoiding inherent bias when analyzing data.

  • Validity, or making sure data was collected in an honest, complete way.

  • Data context, or making sure that students know where, when and how data was collected.

  • Data confidence, or recognizing the limitations of data science.

  • Stewardship, or supervising data sets at all stages of its existence.

  • Privacy, or respecting the privacy of individuals in how data is collected and analyzed.

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