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3 Data Science Education in the Future
Pages 19-25

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From page 19...
... Before making curricular changes, an academic institution needs to take into account its infrastructure, its budget, and its business model, as well as the potential collateral benefits for the rest of the institution. Some universities start with curricular changes at the master's level because those programs are generally easier to develop than undergraduate programs; however, because professionals with undergraduate degrees will be using different skill sets to fill different workforce roles than those with graduate degrees, essential data science skills training needs to be included at all levels of postsecondary education.
From page 20...
... Having industry involved in developing and/or retooling data science courses can help ensure that programs meet workplace needs and that students going through these data science programs have employment opportunities upon completion. Improved collaboration can also help shape and enhance career paths in industry with positions that can both utilize data science skill sets and provide interesting opportunities for growth.
From page 21...
... This interdisciplinary undergraduate statistics program incorporates a breadth of topics central to the study of data science -- real problems, lessons in reproducibility, statistical computing, advanced data analysis, methodology courses, oral and written communication, and interdisciplinary projects. University of Illinois, Urbana-Champaign The data science discipline at the University of Illinois, Urbana-Champaign,2 demonstrates the key theme of interdisciplinary data science education with a particular focus on the diversity of collaborative programs.
From page 22...
... The Department of Statistics at the university is housed within the Donald Bren School of Information and Computer Sciences.6 The variety of courses offered within the statistics department include classes on statistical concepts that are supplemented with computerized applications, such as "Introduction to Probability and Statistics for Computer Science," "Statistical Computing and Exploratory Data Analysis," and "Statistical Computing Methods." Undergraduate students who choose to pursue the data science bachelor's degree have the ability to put equal focus on computer science and statistics when choosing their coursework, as opposed to having to select a specialty. The undergraduate program culminates with a capstone project in the final year that allows for students to apply the statistics and computer science skills learned within the classroom to a large-scale, interdisciplinary, real-world problem.
From page 23...
... Indeed, the methods of data science could readily be applied to ascertain the data to be collected, the analysis methods to be used, and the metrics through which any analysis can specify program success or suggest efforts to adapt or modify the program to meet the success metrics. Having an evaluation process in place could assist institutions in preparation for any formal accreditation that is already being utilized or that might arise as the field of data science grows.
From page 24...
... Economic structures, especially tuition-driven models, often form silos within departments and between disciplines. For example, if there are no mechanisms in place to adjust tuition payments, faculty pay rates, faculty course-load distributions, and general education requirements to accommodate cross-department and cross-disciplinary course offerings, data science course options could be more limited in scope and reach a smaller audience of students.
From page 25...
... , it is important to identify and address potential gaps in knowledge to ensure data science programs are accessible to all future students, regardless of past experiences. Approaches to attract students who are interested in data science but have less quantitative backgrounds are important.


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