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When & Where
Tue, October 15, 2019 - 1:00 PM to 4:00 PM
D-Lab Convening Room

This is a six-hour tutorial on machine learning in R that covers data preprocessing, cross-validation, ordinary least squares regression, lasso, decision trees, random forest, xgboost, and superlearner algorithms. These methods that are important across scientific disciplines for computational investigation of virtually all academic research questions and can help you gain an edge for employment in university, business, industry, and technology settings. 

Prior knowledge requirements: R FUN!damentals: Parts 1 through 4 or previous intermediate working knowledge of R.

**Please install the necessary packages before the date of the workshop - instructions in the link below**


Primary Tool: 
Training Host: 
D-lab Facilitator: 
Evan Muzzall
Format Detail: 
Hands-on, interactive
Participant Technology Requirement: 

Basic Competency

These workshops are designed for participants with beginner fluency. They already have a little coding, tool or method experience but need to learn more intermediate applications such as conditional subsetting and appropriate data visualizations for their research. 

Examples: Introduction to Pandas, R-wrang, Data Visualization with Python, R-graphics, Survey Sampling, Weighting Data, Introduction to Qualtrics, Finding Health Statistics and Data, Data Viz Theory and Best Practices, Python Machine Learning, Machine Learning in R, Intro to Computational Text Analysis, Geospatial Fundamentals in Python/sf/QGIS/ArcGIS, Intermediate Tableau

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