Incorporating Notebooks in STEM Classes and Deploying Interactive Applications
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Abstract
Interest in the development of data science applications has grown rapidly in recent years. To introduce many fundamental data analysis and predictive modeling topics, instructors can benefit from incorporating the use of computational notebooks to encourage reproducible analysis in an effective and standard manner. Jupyter and R Notebooks allow instructors to create and easily share documents that can contain live code, equations, visualizations, and narrative text all in one single file. Notebooks are an implementation of literate programming that allows for direct interaction with data science tools, can be easily shared, and promotes reproducible research with publication-quality output. They are useful for data analysis, numerical simulation, statistical modeling, data visualization, machine learning, and much more. In this talk we discuss how notebooks can be introduced as working examples for hands-on classwork activities in different courses. We also briefly discuss the development and deployment of interactive applications useful for data exploration, analysis, and reporting, that users can host as standalone apps on a webpage or embed in notebooks or dashboards.
Bio
Reinaldo (Rei) Sanchez-Arias earned his Bachelor of Science degree in Mathematics from Universidad del Valle in Cali, Colombia, and a PhD in Computational Science from The University of Texas at El Paso. He completed a postdoctoral researcher appointment for the Army High Performance Computing Research Center (AHPCRC) working in reduced order models for underbody-blast simulations and data compression techniques. Since the Fall 2018 term, he is part of the Department of Data Science and Business Analytics at Florida Polytechnic University, where he teaches courses in data science, statistical learning, scientific computing, and data mining, while participating in research projects with undergraduate and graduate students. His general areas of interest include data mining and machine learning, computational linear algebra and optimization, and data science education. His work has been presented at international and national conference meetings including the Society for Industrial and Applied Mathematics meetings, the International Conference for High Performance Computing, the IEEE International Conference in Machine Learning and Applications, and the International Conference of the Engineering in Medicine and Biology Society.
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