
August 1314, 2018
Introduction to R for Data Science (Section 1)
Instructor: Justin Post
This course provides a basic introduction to the R software environment for the purpose of data science. The course covers importing and exporting data, manipulating data or recoding variables, and visualization and statistical analysis.
This course provides a basic introduction to the R software environment for the purpose of data science. The course covers importing and exporting data, manipulating data or recoding variables, and visualization and statistical analysis.
Introduction to R for Data Science (Section 2)
Instructor: Jonathan Duggins
This course provides a basic introduction to the R software environment for the purpose of data science. The course covers importing and exporting data, manipulating data or recoding variables, and visualization and statistical analysis.
This course provides a basic introduction to the R software environment for the purpose of data science. The course covers importing and exporting data, manipulating data or recoding variables, and visualization and statistical analysis.
Effective Information Visualization
Instructor: Eric Monson
Participants will learn how to clean and structure data; see how freely and commonly available software can be used to create effective visualizations; and learn basic design principles, so you can go beyond the defaults and create eyecatching and impactful figures and infographics!
Participants will learn how to clean and structure data; see how freely and commonly available software can be used to create effective visualizations; and learn basic design principles, so you can go beyond the defaults and create eyecatching and impactful figures and infographics!
Introduction to Python
Instructor: Jason Carter
This course is an introduction to programming as a skill, a discipline, and a profession for graduate students. We will dive into handson programming from day one and progress to evaluating and using open source libraries and frameworks to manage large and complex datasets. We will focus equally on reading and writing code.
This course is an introduction to programming as a skill, a discipline, and a profession for graduate students. We will dive into handson programming from day one and progress to evaluating and using open source libraries and frameworks to manage large and complex datasets. We will focus equally on reading and writing code.
Advanced Statistics in R:
Generalized Linear Models & Multilevel Modeling
Generalized Linear Models & Multilevel Modeling
Instructor: Din Chen
This short course covers advanced statistical modelling and computing using R. We will review the multiple linear regression for continuous data and then proceed to cover the logistic regression for binary/binomial data; Poisson regression and negative binomial regression for counts data; longitudinal data analysis and general multilevel modelling. We also cover some topics in statistical computing on data simulations and bootstrapping.
This short course covers advanced statistical modelling and computing using R. We will review the multiple linear regression for continuous data and then proceed to cover the logistic regression for binary/binomial data; Poisson regression and negative binomial regression for counts data; longitudinal data analysis and general multilevel modelling. We also cover some topics in statistical computing on data simulations and bootstrapping.

August 15, 2018
Text Analysis in R
Instructor: Allison Blaine ; Markus Wust
This course explains how to collect, classify, and analyze textbased data from the internet or other digital sources using R. The course will cover a quick overview of R as a programming language, screenscraping, interfacing with Application Programming Interfaces (APIs), and basic natural language processing such as topic models.
This course explains how to collect, classify, and analyze textbased data from the internet or other digital sources using R. The course will cover a quick overview of R as a programming language, screenscraping, interfacing with Application Programming Interfaces (APIs), and basic natural language processing such as topic models.
Programming in R (Section 1)
Instructor: Jonathan Duggins
This class provides students with an introduction to basic programming techniques in R, a program with stronger objectoriented programming facilities than most statistical computing languages. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. R’s popularity has increased substantially in recent years.
This class provides students with an introduction to basic programming techniques in R, a program with stronger objectoriented programming facilities than most statistical computing languages. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. R’s popularity has increased substantially in recent years.
Programming in R (Section 2)
Instructor: Justin Post
This class provides students with an introduction to basic programming techniques in R, a program with stronger objectoriented programming facilities than most statistical computing languages. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. R’s popularity has increased substantially in recent years.
This class provides students with an introduction to basic programming techniques in R, a program with stronger objectoriented programming facilities than most statistical computing languages. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. R’s popularity has increased substantially in recent years.
Network Analysis for Data Scientists
Instructor: Bill Shi
This course will provide an introduction to network analysis with a focus on data and applications. It will introduce basic concepts and ideas in network science, and cover methods that are practically useful in dealing with network data. At the completion of this course, participants will have a solid understanding of what network analysis does, and be able to run common methods on network data.
This course will provide an introduction to network analysis with a focus on data and applications. It will introduce basic concepts and ideas in network science, and cover methods that are practically useful in dealing with network data. At the completion of this course, participants will have a solid understanding of what network analysis does, and be able to run common methods on network data.
Dynamic/Interactive Visualization
Instructor: Lorin Bruckner
The web has become an important and popular tool for communicating research findings, but it carries a layer of complexity not found in other media: user interactivity. This course will teach participants how to engage their audience in immersive presentations of their data. Participants will learn basic user experience (UX) principles and apply them to interactive dashboards in Tableau. Beginner experience with dashboard creation in Tableau is required.
The web has become an important and popular tool for communicating research findings, but it carries a layer of complexity not found in other media: user interactivity. This course will teach participants how to engage their audience in immersive presentations of their data. Participants will learn basic user experience (UX) principles and apply them to interactive dashboards in Tableau. Beginner experience with dashboard creation in Tableau is required.

August 1617, 2018
Working with Messy Data
Instructor: Brown Biggers
When working with data, one thing is fairly certain: data is rarely in an optimized format. A misplaced space here, or an extra comma there, can mean the difference between two clicks and two hours of work. In this course, we will work with ways to manipulate, interpret, and present data from webpages and text using Python version 2.7 and OpenRefine. This class will also cover regular expressions, various imported libraries to extend Python functionality, and import/export of data in OpenRefine.
When working with data, one thing is fairly certain: data is rarely in an optimized format. A misplaced space here, or an extra comma there, can mean the difference between two clicks and two hours of work. In this course, we will work with ways to manipulate, interpret, and present data from webpages and text using Python version 2.7 and OpenRefine. This class will also cover regular expressions, various imported libraries to extend Python functionality, and import/export of data in OpenRefine.
Intermediate Programming in R
Instructor: Justin Post
The class provides students with a primer on the use of R for the writing of reproducible reports and presentations that easily embed R output using R markdown as well as the creation of interactive and customizable web applets called R Shiny applications.
The class provides students with a primer on the use of R for the writing of reproducible reports and presentations that easily embed R output using R markdown as well as the creation of interactive and customizable web applets called R Shiny applications.
Visualization in Data Science Using R
Instructor: Angela Zoss
This course is designed for two audiences: experienced visualization designers looking to apply open data science techniques to their work, and data science professionals who have limited experience with visualization. Participants will develop skills in visualization design using R, a tool commonly used for data science. Basic familiarity with R is required.
This course is designed for two audiences: experienced visualization designers looking to apply open data science techniques to their work, and data science professionals who have limited experience with visualization. Participants will develop skills in visualization design using R, a tool commonly used for data science. Basic familiarity with R is required.
Introduction to Data Mining and Machine Learning (Section 1)
Instructor: Ashok Krishnamurthy
This course will introduce participants to a selection of the techniques used in data mining and machine learning in a handson, applicationoriented way. Topics covered will include data exploration, decision trees, clustering, association rules, regression and pattern classification. The computing exercises will be based on the statistical programming language, R. At the end of the two days, you will be able to explore a data set, and determine which analysis method is appropriate for the data, and be able to use R packages to obtain results.
This course will introduce participants to a selection of the techniques used in data mining and machine learning in a handson, applicationoriented way. Topics covered will include data exploration, decision trees, clustering, association rules, regression and pattern classification. The computing exercises will be based on the statistical programming language, R. At the end of the two days, you will be able to explore a data set, and determine which analysis method is appropriate for the data, and be able to use R packages to obtain results.
Introduction to Data Mining and Machine Learning (Section 2)
Instructor: Raju Vatsavai
This course will introduce participants to a selection of the techniques used in data mining and machine learning in a handson, applicationoriented way. Topics covered will include data exploration, decision trees, clustering, association rules, regression and pattern classification. The computing exercises will be based on the statistical programming language, R. At the end of the two days, you will be able to explore a data set, and determine which analysis method is appropriate for the data, and be able to use R packages to obtain results.
This course will introduce participants to a selection of the techniques used in data mining and machine learning in a handson, applicationoriented way. Topics covered will include data exploration, decision trees, clustering, association rules, regression and pattern classification. The computing exercises will be based on the statistical programming language, R. At the end of the two days, you will be able to explore a data set, and determine which analysis method is appropriate for the data, and be able to use R packages to obtain results.