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An Education in Data Science

An Education in Data Science should encourage students to collaborate and share ideas. The classroom environment should be supportive of such collaboration, as students should be able to help each other with concepts, code implementations, and project work. As an example, the Mobilize program enables students to help each other by sharing their knowledge of data science. These types of collaborations are vital to the success of the program. The article describes some of the challenges of teaching data science, as well as the impact of the program.

Challenges of teaching data

The current state of K-12 computer science and statistics education has met with limited success. However, teaching data science to students of all ages presents both unique challenges and opportunities. Unlike in traditional statistics education, data sense can be developed earlier than ever before. For example, students may develop an appreciation of data analysis and interpretation using smaller datasets, which are more manageable for teaching. However, as the world of data becomes more dynamic, core statistical concepts must evolve to meet the new demands.

The future of the data-informed society is growing, but there is a shortage of workers. To meet the demand, academic programs in data science have been created in various disciplines. Computer Science, Business, and LIS are leading disciplines in offering DS education. Some institutions have also begun developing interdisciplinary programs in data science education. However, teaching data science to students in a university environment has its challenges. The following are a few challenges of teaching data science.

Need for flexible curricula

While a data science course can be taught in many subject areas, it’s often best to incorporate the field’s nuances into different subject areas. Some courses might include software engineering, which can help students better understand the concepts behind data analysis. Others may focus on data science as a discipline in particular, with courses that teach subject matter-matter-centered data science methods and techniques. These courses should utilize a variety of educational models to help students achieve the highest level of learning.

In 2018, the National Academy of Sciences hosted a Roundtable on Postsecondary Data Science Education to explore new approaches to educating data scientists. This event focused on developing PhD programs and establishing models to foster faculty development in the field. In a similar vein, a new report from the Association for Computing Machinery takes a different approach, focusing on computer science objectives. The authors argue that more flexible curricula are needed to prepare data science professionals for the rapidly growing field of data science.

Need for a multidisciplinary curriculum

The need for a multidisciplinary curriculum in data science is a pressing one, as it provides a foundation for students to be able to ask valid questions about data. Data science students need to know how to collect and analyze data, develop tools and algorithms, and make predictions and decisions based on that information. In addition, students need to be exposed to the ethical issues associated with using data and to how the source of the data influences the outcomes.

A multidisciplinary curriculum can be developed to give students a foundation for their studies and motivate their curiosity about the field. Curriculum development should draw on the experiences of seasoned faculty as well as those who are new to the field. It can also include interdisciplinary approaches and co-curricular activities. Choosing among various approaches requires careful evaluation and established good practices. There are many approaches to develop data science education curriculums.

Impact of Mobilize program

The Mobilize program has a multifaceted mission that engages students in computational thinking, statistics, algorithms, and civic engagement. It fosters critical thinking and problem-solving skills, along with interdisciplinary collaboration, all while promoting data science education. Students collect data on real-world issues with an app. In the process, they can develop new skills and explore the world around them. The program’s goal is to make data science accessible to all.

To build a community of data scientists, Mobilize will partner with the Rockefeller Foundation and Mastercard Center for Inclusive Growth. This partnership will build a data science education pipeline to empower civic and nonprofit organizations to use data for social good. The money will be used to support data science training, education, and networking. Data scientists will also help these organizations find the best use for applied data.

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