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Disciplinary Data Science Topic: Statistics - Descriptive Statistics, Histograms, Scatter Plot Data Science Learning Goals: Students will know how to calculate basic statistics such as mean, standard deviation and relate the use of these statistics learned in class with real-world data and use them to describe the data Students will be able to construct visualization tools such as a histogram to get the range and distribution of the data set. Students will also learn how to interpret the results. Students will be able to use popular data science tools such as Python to analyze the data.
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Using machine learning techniques to aid environmental policy analysis: a teaching case in big data and electric vehicle infrastructure
For a growing class of prediction problems, big data and machine learning analyses can greatly enhance our understanding of the effectiveness of public investments and public policy. However, the outputs of many machine learning models are often abstract and inaccessible to policy communities or the general public. In this article, we describe a hands-on teaching case that is suitable for use in a graduate or advanced undergraduate public policy, public affairs or environmental studies classroom. Students will engage on the use of increasingly popular machine learning classification algorithms and cloud-based data visualization tools to support policy and planning on the theme of electric vehicle mobility and connected infrastructure. By using these tools, students will critically evaluate and convert large and complex datasets into human understandable visualization for communication and decision-making. The tools also enable user flexibility to engage with streaming data sources in a new creative design with little technical background.
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- PAR ID:
- 10165859
- Date Published:
- Journal Name:
- Case studies in the environment
- ISSN:
- 2473-9510
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1525/cse.2020.961302
