Python

Python Web Scraping & APIs

June 29, 2022, 1:00pm
In this workshop, we cover how to extract data from the web using Python. We focus on two approaches to extracting data from the web: leveraging application programming interfaces (APIs) and web scraping.
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Python Text Analysis Fundamentals: Parts 1-2

June 14, 2022, 1:00pm
This two-part workshop series will prepare participants to move forward with research that uses text analysis, with a special focus on humanities and social science applications.
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Python Deep Learning: Parts 1-2

June 7, 2022, 1:00pm
This workshop presents a brief history of Artificial Neural Networks (ANNs) and an explanation of the intuition behind them; a step-by-step reconstruction of a very basic ANN, and then how to use the scikit-learn library to implement an ANN for solving a classification problem.
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Python Fundamentals: Parts 1-4

June 21, 2022, 1:00pm
This four-part, interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application.
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Python Visualization

June 2, 2022, 1:00pm
For this workshop, we'll provide an introduction to visualization with Python. We'll cover visualization theory and plotting with Matplotlib and Seaborn, working through examples in a Jupyter notebook.
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Python Introduction to Machine Learning: Parts 1-2

May 24, 2022, 1:00pm
This workshop introduces students to scikit-learn, the popular machine learning library in Python, as well as the auto-ML library built on top of scikit-learn, TPOT. The focus will be on scikit-learn syntax and available tools to apply machine learning algorithms to datasets. No theory instruction will be provided.
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Python Data Wrangling and Manipulation with Pandas

May 31, 2022, 1:00pm
Pandas is a Python package that provides 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.
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Frank Hidalgo Ruiz

Data Science Fellow
Chemistry

I am currently a 5th-year Chemical Biology Ph.D. student. My research focuses on understanding the mechanism by which mutations in a protein called Ras lead to tumorigenesis. More specifically, I aim to integrate high-throughput mutagenesis, coevolutionary analysis, and machine learning algorithms to generate a predictive model. Over the last year, I have built a Python package to process, analyze, and visualize Next Generation Sequencing datasets. I love collaborating across research fields and sharing my passion for data science.

Amanda Jorgensen

IUSE Undergraduate Advisory Board
Data Science
Economics

I am currently a fourth-year undergraduate, studying Data Science and Economics.

Working with spatial networks

April 25, 2022
Yiyi He

When working with spatial networks, both ArcGIS and Python packages such as NetworkX and iGraph are very useful tools. In the past, I have used both tools to help me better understand spatial network topology and network flow. In this blog post, I hope to share with you some cool features that these tools have...