Using R for ML!
+Using R for ML
Working with spatial data
+Working with spatial data in R
MaxQDA: Introduction+ + + +
MaxQDA Fundamentals
Using MaxQDA for data analysis
Python Data Visualization+ +
Pandas, Matplotlib, and Seaborn
++ In this workshop, we provide an introduction to data visualization with Python. First, we'll cover some basics of visualization theory. Then, we'll explore how to plot data in Python using the matplotlib and seaborn packages. +
+Python Data Wrangling+ +
Manipulate DataFrames using Pandas in Python
++ In this workshop, we provide an introduction to data wrangling with Python. We will do so largely with the pandas package, which provides a rich set of tools to manipulate and interact with data frames, the most common data structure used when analyzing tabular data. We'll learn how to manipulate, index, merge, group, and plot data frames using pandas functions. +
+Python Deep Learning+ +
Create and train neural networks using Tensorflow and Keras
++ This workshop conveys the basics of deep learning in Python using keras on image datasets. Students are empowered with a general grasp of deep learning, example code that they can modify, a working computational environment, and resources for further study. +
+Python Fundamentals+ +
The absolute basics
++ This three-part interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience, with a focus on data science application. It covers the basics of Python and Jupyter, variables and data types, and a gentle introduction to data analysis in Pandas.
+Python Geospatial Fundamentals+ +
Analyzing geospatial data using GeoPandas in Python
++ Geospatial data are an important component of data visualization and analysis in the social sciences, humanities, and elsewhere. The Python programming language is a great platform for exploring these data and integrating them into your research. +
+Python Intermediate+ +
Building on the basics
++ This three-part interactive workshop series is a follow-up to D-Lab's Python Fundamentals. It is intended for people who want to learn about core structures of Python that underpin data analysis. We cover loops and conditionals, creating your own functions, analysis and visualization in Pandas, and the workflow of a data science project.
+Python Machine Learning+ +
Classification, regression, clustering in Python
++ In this workshop, we provide an introduction to machine learning in Python. First, we'll cover some machine learning basics, including its foundational principles. Then, we'll dive into code, understanding how to perform regression, regularization, preprocessing, and classification. +
+Python Web APIs+ +
Obtain data from web platforms
++ In this workshop, we cover how to extract data from the web with APIs using Python. APIs are often official services offered by companies and other entities, which allow you to directly query their servers in order to retrieve their data. Platforms like The New York Times, Twitter and Reddit offer APIs to retrieve data. +
+Python Web Scraping+ +
Scrape HTML/CSS data from websites
++ In this workshop, we cover how to scrape data from the web using Python. Web scraping involves downloading a webpage's source code and sifting through the material to extract desired data. +
+Qualtrics Fundamentals+ +
Data collection and survey design
++ This workshop will introduce Qualtrics with an orientation to the main interfaces for web survey design, sample management, corresponding with sample members, and exporting data. +
+R Data Visualization+ +
Visualizing with R
++ This workshop will provide an introduction to graphics in R with ggplot2. Participants will learn how to construct, customize, and export a variety of plot types in order to visualize relationships in data. +
+R Data Wrangling and Manipulation+ +
Working with data
++ This workshop will introduce packages in R (notably dplyr and tidyr) that make data wrangling and manipulation much easier. +
+R Deep Learning+ +
Working with Keras in R
++ This workshop introduces the basic concepts of Deep Learning — the training and performance evaluation of large neural networks, especially for image classification, natural language processing, and time-series data. Like many other machine learning algorithms, we will use deep learning algorithms to map input data to their appropriately classified outcome labels. +
+R Fundamentals@@ -192,7 +643,7 @@
The absolute basics
R Geospatial Fundamentals+ +
Working with spatial data in R
++ Geospatial data are an important component of data visualization and analysis in the social sciences, humanities, and elsewhere. The R programming language is a great platform for exploring these data and integrating them into your research. +
+R Machine Learning with tidymodels+ +
Using R for ML
++ This workshop offers an introduction to machine learning algorithms by making use of the tidymodels package. +
+The absolute basics
+The basics of Stata