Instructor: Dr. Ming-Hsiang Tsou
Office: Storm Hall 313C
Tuesday/Thursday 3:30pm- 4:30pm
Overview: This course introduces state-of-the-art computational platforms, tools, and skills for big data science and big data analytics with numerous real-world case studies. The big data field provides untapped potential for discovering and analyzing complex problems faced by humankind, including business analytics, disease outbreaks, traffic patterns, urban dynamics, and environmental changes. This class will introduce big data platforms (Amazon EC2) and key concepts (cloud computing, virtualization, information privacy, and crowd sourcing). Students will learn to how to use Amazon EC2, Google Cloud Platform, MongoDB, R, Gephi, ArcGIS Online, and Tableau to conduct big data analytics. The course will provide basic introduction to big database management related to NoSQL databases, Hadoop and MongoDB. This course will have both the hands-on training of analytics tools and computer skills, as well as the fundamental concepts for big data science with critical thinking and problem solving. Students will have the opportunity to create their own big data platform on Amazon EC2 virtual servers, manage their own databases in MongoDB, and access and collect big data from sources of their choosing (e.g. Twitter data and business datasets). .
Prerequisites: One minimum computer programming or introductory course (from GEOG 104, CS100, CS107, or equivalent computer programming courses) and one fundamental statistics course (from GEOG 385, STAT 250, or SOC 201 or equivalent statistic courses).
Equipment Required: Each student should bring their own laptop computers (preferred Windows OS) to the class for the web-based lab exercises and class assignments.
O'Neil, C., & Schutt, R. (2013). Doing Data Science: Straight Talk from the Frontline. O'Reilly Media, Inc.
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