Raja Sooriamurthi
ABSTRACT
In this teaching tip and courseware note we describe a series of hands on activities and exercises that we've used to introduce the notion of big data analytics to a wide range of audience. These exercises range in complexity from a paper and pencil thought exercise, to using Google Trends for simple explorations, to using a spread sheet to simulate the iterative nature of Google's PageRank algorithm, to programming with a Python based map-reduce framework. These exercises have been used in courses to train high school teachers in data science, full semester university courses (undergraduate and graduate), and CS education outreach efforts. Feedback has been positive as to their efficacy.
- Thomas H. Davenport and Jeanne G. Harris. 2010. Analytics at Work: Smarter Decisions, Better Results. Harvard Business Review Press.Google Scholar
- Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report 1999-66. Stanford InfoLab. http://ilpubs.stanford.edu:8090/422/Google Scholar
- Seth Stephens-Davidowitz. 2017. Everybody Lies: Big Data, New Data, and What the Internet can tell us about who we really are. William Morrow. Google ScholarDigital Library
- Ben Willington. 2015. How Software in Half of NYC Cabs Generates $5.2 Million a Year in Extra Tips. (January 2015). Retrieved Jan 21, 2018 from http://iquantny.tumblr.com/post/107245431809/ how-software-in-half-of-nyc-cabs-generates-52 1 https://pythonhosted.org/mrjob Abstract 1 Data Driven Decision Making 2 Data Detective: How much to tip? 3 How does ``Big'' make a difference? 4 Need for Massive Parallelism 5 MapReduce and PageRank 6 Reflection ReferencesGoogle Scholar
Index Terms
- Introducing big data analytics in high school and college
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