Events

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 Geospatial Fundamentals: Part 1 This workshop is the first one of the two-part series on using Python for fundamental geospatial analysis and visualization. After this workshop, you will be able to: Recognize different forms of geospatial data and coordinate reference system (CRS), Use GeoPandas and matplotlib libraries to map and analyze spatial data. Python Geospatial Fundamentals: Part 2 This workshop is the second one of the two-part series on using Python for fundamental geospatial analysis and visualization. After this workshop, you will be able to: Apply more advanced Python libraries for interactive visualization. Choose domain-specific spatial datasets to create your own maps. Knowledge Requirements You'll probably get the most out of this workshop if you have a basic foundation in Python and Pandas, similar to what you would have from taking the D-Lab Python Fundamentals workshop series. Here are a couple of suggestions for materials to check out prior to the workshop.

In this workshop, we will convey the basics of deep learning in Python using keras on image datasets. You will gain a conceptual grasp of deep learning, work with example code that they can modify, and learn about resources for further study. We start with a review of what deep learning is and then unpack what neural networks are and how they work. We then jump straight into Python, using the Keras library to build neural networks. We will explore how different architectures affect performance of predicting handwritten digit images.