@inproceedings{61b22e976a544f879e1d6f2999217100,
title = "A practical and sustainable model for learning and teaching data science",
abstract = "This paper details our experiences with design and implementation of data science curriculum at University at Buffalo (UB). We discuss (i) briefly the history of project, (ii) a certificate program that we created, (iii) a data-intensive computing course that forms the core of the curriculum and (iv) some of the challenges we faced and how we addressed them. Major goal of the project was to improve the preparedness of our workforce for the emerging data-intensive computing area. We measured this through assessment of student learning on various concepts and topics related to data-intensive computing. We also discuss the best practices in building a data science program. We highlight the importance of external funding support and multidisciplinary collaborations in the success of the project. The pedagogical resources created for the project are freely available to help educators and other learners navigate the path to learning data science. We expect this paper about our experience will provide a road map for educators who desire to introduce data science in their curriculum.",
keywords = "Cloud computing, Curriculum, Data Science, Data analysis, Hadoop, MapReduce, Spark",
author = "Bina Ramamurthy",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 47th ACM Technical Symposium on Computing Science Education, SIGCSE 2016 ; Conference date: 02-03-2016 Through 05-03-2016",
year = "2016",
month = feb,
day = "17",
doi = "10.1145/2839509.2844603",
language = "English",
series = "SIGCSE 2016 - Proceedings of the 47th ACM Technical Symposium on Computing Science Education",
publisher = "Association for Computing Machinery ",
pages = "169--174",
booktitle = "SIGCSE 2016 - Proceedings of the 47th ACM Technical Symposium on Computing Science Education",
address = "United States",
}