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5 Ways Students Learn Data-Driven Skills

Teacher assisting student

Learning data-driven skills in high school can help students develop a strong understanding of data science concepts and techniques, as well as the ability to analyze, visualize and interpret data. When exploring how to cultivate all the skills needed, the following are some ways students can learn data-driven skills:

​1. Data Science Curriculum:

​Many high schools now offer data science classes, which provide students with an introduction to data analysis, data visualization, and other key concepts in data science. For example, in Illinois, beginning the 2022-23 school year, all school districts shall ensure that students receive developmentally appropriate opportunities to gain computer literacy skills at each grade level K-12. These classes give students a solid foundation in the concepts and tools used in data science and provide hands-on experience working with data.

​2. Data Science Clubs:

​Data science clubs are an excellent way for students to learn data-driven skills in a collaborative environment. These clubs often involve working on projects, participating in competitions, and learning from industry professionals. In addition, they provide students with opportunities to apply what they have learned in the classroom to real-world situations and network with others with similar interests. These clubs can be in-person or online.

​3. Online Learning:

​Students can use many online resources to learn data science skills, such as online tutorials, data analysis tools, and virtual classes like KidAlytics that provide a mixture of options for on-demand and live learning options.

​4. Internships:

​High school students can gain real-world experience by interning with data science companies or organizations. These internships provide students with hands-on training in data analysis, data visualization, and other key data science skills. They also allow students to network with industry professionals and learn about different data science careers.

​5. Self-study:

​Students who are motivated and interested in data science can also pursue learning independently by reading books, taking online courses, and working on personal projects. This can include learning programming languages like R or Python, using data visualization tools such as Tableau, and participating in data science competitions like Kaggle.

It's essential to note that learning data-driven skills require students to have foundational math, statistics, and computer science knowledge. Thus, it's recommended that students take classes in these subjects to build a strong foundation for their data science education. Additionally, having strong foundational knowledge in these areas will help students better understand the concepts and techniques they learn in data science classes and clubs.

Learning is a combination of many things, and these five tips will help in creating your data portfolio along your journey.


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