Data Science

How to help high schoolers understand big data

high schoolers understand big data

Data science is currently among the most rapidly growing and promising careers of the future. The technology surrounding data mining and analysis has transformed initially useless raw data into an important commodity for most organizations. Data scientists combine math, computer science, and other skills when interacting with raw data for insightful results.

With the growing demand and application of data science, it is prudent for students to include big data in their educational curricula, just as kids learn to code. Like physics, biology, and chemistry, high schoolers should be equipped with a strong data science foundation for progressive proficiency.

Paradigm Shifts in Learning Data Science

Most high schools have updated their curricula to include computer science and computational thinking. As such, most students learn about data science in college or while at work. While computational thinking and computer science are part of the puzzles in data science, they don’t reflect the real big data realm.

With the collection of large amounts of data becoming eminent, it is prudent for next-generation students to learn practically how to interact with large data sets. That said, updating the school curricula to reflect these shifts and include data science provides a foundation that students can build onto while in college or pursuing their careers.

Providing an early foundation of data science equips students with several skills beyond basic technical knowledge. Essentially, data scientists uncover answers to pressing issues by exploring data trends. Therefore, students with a basic understanding of data science can apply their curiosity in various fields and develop effective communication skills.

Even though not all students will choose to become data scientists, many other careers intersect with this field. For instance, engineers with some data science background ease cross-communication with data analysts in an organization. Generally, several careers benefit from an early introduction to data science.

Why Learn Data Science in High School?

While a report predicted more than 3.5 million STEM jobs in the U.S by 2025, an estimated 2 million opportunities will remain unfilled due to the shortage of experienced personnel. Most of these unfilled jobs require individuals with adept computer, technological, and critical thinking skills. Exposing students to data science early equips them with the necessary foundation to develop these skills.

Studies have also suggested that exposing high schoolers to math and science courses encourages them to pursue STEM-related careers. As for data science, job opportunities for data scientists are expected to surpass 16% by 2028. With the increasing application of data mining technologies, employer demand for skilled data scientists also increases.

Teaching data science in high school offers students practical experience on how to achieve effective communication and collaboration. With more careers embracing the importance of data mining and big data, the future workforce should understand various applications of data science for business growth.

As mentioned, most high schools have incorporated computer science in their lessons. Unfortunately, very few schools have data science dedicated curricula. However, with the continuously evolving technology, schools should update their lessons to reflect these topics that prepare the next generation of workers.

How to Teach Data Science in High School

Teaching data science to high school students requires the proper integration of these lessons into their curricula. While data science has a steep learning curve, teachers should balance the technical and non-technical aspects evenly. Excessive concentration on the technical elements might lessen the learners’ interests prematurely.

That said, some curriculum ideas to include when teaching data science in high school include;

  • Teaching the importance of data science – introductory lessons to data science should provide an overview of data science and its importance in the current digital era. It should also feature the various applications of this discipline.
  • Career options in data science – students should also be informed of various career options they can pursue.
  • The intersection between data science and other fields – students should be exposed to the practical application of data science in businesses, health, and other fields. Assignments within these lessons should include real-world examples where students use actual tools and analytical techniques.
  • Routine examinations – teachers should administer technical examinations to test the students’ level of comprehension. However, exams shouldn’t be the main focus during the learning period.

Note that the complexity of technical training increases during the course. Similarly, data science curricula should include an introduction to various programming languages, such as Scala, R, Python, and Java. However, it worth noting that not all professional-grade tools are teacher and student-friendly. Fortunately, various instructor-friendly courses provide hands-on teaching and learning opportunities.

While teaching data science can be quite challenging, the tips below can help simplify the learning process for high school students.

  1. Start small – data science is a wide subject and overwhelming to teach in its entirety. Therefore, teachers should introduce small contexts for students to understand with ease.
  2. Encourage in-depth studies – as class lessons introduce the basics of data science to students, they should study further. Fortunately, several learning platforms, including courser and Lynda, have sufficient learning resources. Conferences and workshops are also good learning points.
  3. Familiarize students with hardware and software tools – data analysis tools help data scientists organize and sample large data sets. Teachers should ensure that their students understand how these tools function.
  4. Provide real-life examples – you should provide real-life data sets, such as census data, for students to work on. Provide applicable case studies and simulations to enhance students’ understanding.
  5. Leverage storytelling and visualization – data science will make sense to students who can see its practical importance. Therefore, demonstrate to students how accountants use software to analyze Twitter feeds or keywords.

As mentioned, data scientists should have both technical knowledge and a great experience. Data scientists also rely on effective communication and collaboration skills in their daily operations. Like other high-school lessons, data science classes should include other courses, such as public speaking and writing, for an all-rounded learning experience.

Once a data science curriculum has been established, high schools should identify qualified teaching staff to facilitate the program. While onboarding data scientists may sound like the best choice, such professionals may not have the required teaching background or lack in-depth knowledge on career paths within data science. Therefore, high schools should find a data science expert with teaching experience to guide data science courses.

Bottom Line

Data science is a rapidly growing field with applications in various sectors, including finance, healthcare, academic research, and business. The current cohort of high school students is lucky to grow in a technology-focused era, making them suitable candidates for early training. Introducing them to data science at an early age gives them a solid foundation for data science and other technology-related fields.

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