Data Science offers one of the most promising and rapidly growing career fields of the last decade. Technological advancements on the mining and analyzation of data have transformed it into a valuable commodity for a variety of fields, from healthcare to finance to academic research. Data Scientists are responsible for interacting with data and extracting valuable information from them that can be applied to develop solutions that relate to real-world problems.
As one of the most lucrative and important career fields in the world, there is a growing need for the next generation of students to learn how to effectively interact with data and apply data science in their jobs. Just like physics, chemistry, and math, high school students should start building a foundation in data science early on to support proficiency in data science later.
As of now, most students are only exposed to introductory computer processing or computer science during high school and interact with Data Science only after entering college, or starting their first job. However, computer science is just one part of the data science puzzle. As the ability to mine larger amounts of data becomes more feasible, it also becomes important for the next generation of students to learn how to analyze and interact with this data. Updating current curriculum to include data science provides high school students with an initial toolbox that they can use to build additional skills throughout their careers and prepares them to work in the global economy.
Data science is present in every field of life today – from manufacturing to healthcare to STEM- it is the foundation on which every industry operates. For example, in finance, data science helps in risk assessment and monitoring, potential fraudulent behavior, payments, customer analysis and experience, among many other utilizations. Data science helps financial companies and institutions get actionable insights and obtain a sustainable development. Similarly, in healthcare, by connecting pattern recognition, analytics, statistics and deep learning algorithms, data science makes healthcare more efficient. In 2008, Google staffers used data on flu-related searches to map flu outbreaks in real time, and revolutionized the management of flu outbreaks across the world. Data science also applies to the travel industry. For example, UPS uses data science to optimize package transport from drop-off to delivery. It uses Data Science to crack challenging logistics puzzles such as how packages should be rerouted around bad weather or service bottlenecks. According to a company forecast, the platform could save UPS $100 to $200 million by the end of 2020. Data Science is playing an active role in improving the efficiency of companies across various industries, and demand for people who know how to use data to get valuable insights is rising rapidly. Therefore, an understanding of data science enables students to work across a number of industries and opens them up to several career paths where their skills will be in high demand.
A 2019 report by Deloitte predicted that while the US will create 3.5 million STEM jobs by 2025, approximately 2 million of those jobs, or over 50%, will go unfilled due to a lack of skilled workers. Specifically, these jobs will require workers with technological, computer, and critical thinking skills. Early exposure to Data Science provides a constructive environment for students to build a foundation in all three areas, and prepares them for a variety of career paths.
With particular regard to data science as an independent career field, the U.S. Bureau of Labor Statistics anticipates that employment for data scientists will grow to 16% by 2028 – faster than the average of all other professions. As the demand for improved data-mining technology increases, so will the demand for highly skilled data scientists. Therefore, as one of the fastest growing and most-in demand professions, it is imperative for students to learn about what a data scientist does so they can think about it as a prospective career path for themselves.
Additionally, students in today’s technologically-rich world already use data made available by current technologies to explore complex questions. Even if they do it unconsciously, students already collect and make decisions based on data that they see in their everyday lives. For example, students use the internet and social media to research data on what people think about the latest Avengers or Batman movie, and use it to intelligently decide whether they will watch that film in the theater or not. Therefore, when students realize this and understand how data impacts their everyday lives, they will understand its growing relevance in today’s world and not think about it as some sort of distant and far-fetched concept.
Effectively teaching high school students data science begins by having a clear roadmap for what you are going to teach and setting effective curriculum goals and expectations. One of the main things to keep in mind when teaching data science to high schoolers is that you can’t expect everyone to be tech-savvy. Every student will have a different level of interest, skills and expertise, so you have to make sure that the sessions are not too technical initially to retain the interest of the students. Also, it is necessary to keep things interactive when teaching students data science. For most students, data science will be a familiar term, but they may not know much about it in-depth. Therefore, it is necessary to use interactive teaching methods to engage students. For example, using interactive templates and presentations and asking students questions will ensure that they are paying attention and thinking about the concept themselves. Some curriculum ideas are mentioned below:
Understand the importance of data science: Introductory data science courses should provide students with an overview of data science and discuss how data science can be applied in various contexts
Exploring career options in data science: Students should understand what career paths they can pursue through data science, and what opportunities they will have in the future to apply data science at their job
Collaborating with local universities and businesses: Collaborating with local universities and businesses will expose students to hands-on and practical applications of data science. This will help them understand the domain better and learn how data science applies to other industries and career fields
Identify real-world examples and applications of Data Science: Students should be given projects and course assignments where they use data science tools and analytical techniques to answer real-world questions and create reports and presentations based on their findings.
Teach students newer programming languages: Basic programming languages such as C, C++, and Java are already taught as part of the curriculums of most schools. However, languages like Python, R, and Scala are also imperative in data science. Therefore, students should also be taught how to code in these newer programming languages through interactive exercises and projects that relate to real life. That way, they will not only understand how to program in new languages, but also how to apply it to the real world, which will increase their understanding.
Provide students presentation opportunities: Working in data science requires more than technical knowledge and experience. Data scientists routinely use communication and collaboration skills in day-to-day operations. Therefore, data science coursework should be integrated with other school courses such as writing and public speaking. For example, students should compile detailed written data analysis reports and present their findings to an audience. This will provide students with a more well-rounded training experience in data science and help them be more prepared for their careers.
Consistent technical and programming assessments: Technical assessments to evaluate student understanding should be included to track student progress and ensure that they are comfortable with the new skills and techniques they’ve acquired through the course.
Once a comprehensive course curriculum has been established, qualified teaching faculty will need to be onboarded. While data scientists may seem as the most obvious option to teach data science, data scientists without a teaching background may lack in-depth knowledge about the various elements and career subdomains of data science, such as Data scientist, data analyst and date engineer. The job of a data scientists varies from industry to industry and company to company, and even though they might be very knowledgeable and good at what they do, they’re not necessarily the best teachers. Therefore, data science faculty with prior teaching experience should lead data science courses, because they would know about the different elements and sub-domains of data science, and how to explain them in an engaging manner to students.
In a world that is increasingly influenced by the power of data, introducing high school students to data science early in their education careers provides them a solid foundation in important concepts and opens them up to a variety of career paths. Together, the right combination of non-technical and technical curriculum and experienced teaching faculty will form an effective training course that provides students with a clear understanding of data science, and more importantly, help shape the skilled STEM workforce of tomorrow.