Introduction to Machine Learning in R: Part 2

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. 

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Introduction to Machine Learning in R: Part 1

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. 

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Introduction to Qualitative Research Coding

This session focuses on explaining the proces of creating, organizing, and applying codes within the context of qualitative research. An overview of qualitative data analysis (QDA) software will be provided, noting general advantages and disadvantages, as well as comparing popular programs. Participants will also be introduced to analysis using QDA software.

The agenda will be as follows:

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Qualitative Research Primer

This session introduces qualitative research, and is recommended for folks who are new to research in general or to qualitative research in particular. 

The agenda will be as follows:

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R Fundamentals: Part 4

R Fundamentals Part 4: Putting it all together

In the final part, we will review data importation, subsetting, and visualization. Students will then be given the majority of time to reproduce a workflow on two different datasets, ask questions, and review the solutions as a group.

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R Fundamentals: Part 3

R Fundamentals Part 3: Data Exploration and Analysis

Students will be introduced to data exploration and analysis in R. You will learn how to summarize data and explore it with histograms, scatterplots, and boxplots. You will also be introduced to coding statistical data analysis via t-tests, analyses of variance, correlation, and linear regression.

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The available spaces and the waitlist for this event are both full.

R Fundamentals: Part 2

R Fundamentals Part 2: Subsetting and Reshaping

Students will be introduced to loading data from files and various ways to subset it with an emphasis on bracket notation. You will also learn how to use logical vectors, search for and subset missing data, and merge data frames. Terms like subset, bracket notation, and logical vectors will be defined or reintroduced in Part 2.

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R Fundamentals: Part 1

Data are the foundations of the social and biological sciences. Familiarizing yourself with a programming language can help you better understand the roles that data play in your field. Learn to develop and train your data skills for free at our R workshops!

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R Graphics with ggplot2

This workshop will provide a comprehensive overview of graphics in R, including base graphics and ggplot2. Participants will learn how to construct, customize, and export a variety of plot types in order to visualize relationships in data. We will also explore the basic grammar of graphics, including the aesthetics and geometry layers, adding statistics, transforming scales, and coloring or panelling by groups.

Click here for install instructions and workshop materials.

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Remote Sensing FUN!damentals: Working with Digital Imagery

This workshop introduces the basic principles of understanding digital imagery, including the fundamentals of multi-spectral imagery. Participants will learn how to find and download satellite and aerial imagery, how to display and enhance digital imagery, and basic techniques for image interpretation and analysis. This workshop will be taught using QGIS, a popular free and open source geospatial software package.

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