This workshop introduces Artificial Neural Networks (ANNs), a group of popular machine learning algorithms. No prior knowledge is required, though previous experience with other machine learning algorithms would be helpful. The workshop will be divided into three parts:

Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with 'relational' or 'labeled' data both easy and intuitive. It enables doing practical, real world data analysis in Python.

In this workshop, we'll work with example data and go through the various steps you might need to prepare data for analysis.

We plan to cover:

Geospatial data are an important component of social science and humanities data visualization and analysis. This workshop will introduce basic methods for working with geospatial data in Python using GeoPandas, a relatively new Python library for working with geospatial data that has matured and stabilized in the last few years. In the workshop we will import geospatial data stored in shapefiles and CSV files into geopandas objects. We will explore methods for subsetting and spatial reshaping these objects.

Students will learn about the different ways in which entities in the real world are represented as geographic data. They will be introduced to QGIS, an open-source Geographic Information System (GIS) tool for working with geographic data. By the end of this three-hour long workshop, students will be introduced to projections and coordinate systems and explore the impact of coordinate reference systems (CRSs) on the shape and area of visualized data, as well as understand how to find, load, display, symbolize, and explore different types of geographic data using QGIS.