In my Data Visualization course at the University of Texas, I teach the essential and practical skills necessary to communicate information about data clearly and effectively through graphical means. Rendering data clearly and effectively with appropriate visual analytics reduces the time required to achieve understanding and helps in managing the ever growing amount of available digital data. Students learn to use the data.world platform and various software tools including Tableau, SQL, R, dplyr, ggplot, and Shiny.
Working with data.world is an obvious choice for my class, as the platform gives my students the opportunity to collaborate around their specific projects, take advantage of the wealth of data readily available on the platform, and learn how to store their data in a space available to them long after their time with the University.
data.world is the environment that ties all of this together via the flexible connectors and APIs
Tableau is one of the most popular commercial data visualization tools on the market today. My students learn how to use this tool to quickly analyze, visualize their data and share the information they discover on the Web.
R is the most popular free software environment for statistical computing and graphics. ggplot2 is a data visualization package for R that can be used to produce publication-quality graphics. In this course students learn how to use R and ggplot to not only produce production-quality graphics but also how to produce large multiplot images (i.e., dozens and dozens of different plots in one image) that can be used as a standardized form of analysis.
data.world is the environment that ties all of this together via the flexible connectors and APIs. It also makes it possible for students to archive their data so that their analysis is available long after the semester ends. Students in follow-on semesters can quickly pick up the previous semester projects and carry them forward.
An example of the analysis that my students produced in the 2017 Semester can be seen here.
Dr. Philip Cannata received his PhD. in 1980 from the University of Notre Dame in High Energy Physics and has worked in the Computer Science industry for over 38 years starting with doing Unix development at Bell Laboratories in the early 80s where his most significant contribution to Unix was the design and inclusion of Shared Memory, Semaphores, and Memory Mapped Files into Unix 4 and 5. After that, he was a Research Director at MCC in Austin, Texas and then worked at IBM and Sun Microsystems. Dr. Cannata currently works as a performance data analyst for Exadata in the Oracle Cloud Division and has been an Adjunct Professor at the University of Texas at Austin for 16 years teaching "Data Management", "Data Visualization", "Data Analytics", "Programming Languages", "Data Structures and Algorithms in Java and Python", and "Networking".
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