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PyData April Meetup: Interactive Data Visualization in Jupyter Notebook Using bqplot

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Join us for a PyData Ann Arbor Meetup on Thursday, April 13th at 6 PM, hosted by TD Ameritrade and MIDAS.

This month’s meetup will focus on bqplot which is a Python plotting library based on d3.js that offers its functionality directly in the Jupyter Notebook, including selections, interactions, and arbitrary css customization. In bqplot, every element of a chart is an interactive widget that can be bound to a python function, which serves as the callback when an interaction takes place. This allows the user to generate full fledged interactive applications directly in the Notebook with just a few lines of Python code. In the second part of the talk, drawing examples from fields like Data Science and Finance, we show examples of building interactive charts and dashboards using bqplot and the ipywidgets framework.

The talk will also cover bqplot’s interaction with the new JupyterLab IDE and what we plan for the future.

Presenter: Dhruv Madeka is a Quantitative Researcher at Bloomberg LP. His current research interests focus on Machine Learning, Quantitative Finance, Data Visualization and Applied Mathematics. Having graduated from the University of Michigan with a BS in Operations Research and from Boston University with an MS in Mathematical Finance, Dhruv is part of one of the leading research teams in Finance, developing models, software and tools for users to make their data analysis experience richer.

 

PyData Ann Arbor is a group for amateurs, academics, and professionals currently exploring various data ecosystems. Specifically, we seek to engage with others around analysis, visualization, and management. We are primarily focused on how Python data tools can be used in innovative ways but also maintain a healthy interest in leveraging tools based in other languages such as R, Java/Scala, Rust, and Julia.

PyData Ann Arbor strives to be a welcoming and fully inclusive group and we observe the PyData Code of Conduct. PyData is organized by NumFOCUS.org, a 501(c)3 non-profit in the United States.

“use what you have learned to make something better and share with others”

PyData March Meetup: Carol Willing, Jupyter Project

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Join us for our first PyData Ann Arbor Meetup on Thursday, March 2nd at 6 PM, hosted by TD Ameritrade and MIDAS. Please RSVP.

 

This month, we are excited to host Carol Willing who will be discussing the Jupyter eco-sytem.  Carol develops software, electronics, educational tutorials, and is passionate about outreach.  She is a core developer on the Jupyter Project and is a former director at the Python Software foundation.  She continues to contribute her time to OpenHatch, Systers, PyLadies San Diego, and San Diego Python.

PyData Ann Arbor is a group for amateurs, academics, and professionals currently exploring various data ecosystems. Specifically, we seek to engage with others around analysis, visualization, and management. We are primarily focused on how Python data tools can be used in innovative ways but also maintain a healthy interest in leveraging tools based in other languages such as R, Java/Scala, Rust, and Julia.

PyData Ann Arbor strives to be a welcoming and fully inclusive group and we observe the PyData Code of Conduct. PyData is organized by NumFOCUS.org, a 501(c)3 non-profit in the United States.

“use what you have learned to make something better and share with others”

School of Information Guest Lecture: Zhenhui (Jessie) Li, PhD, Pennsylvania State University

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jessie-li-photo

Zhenhui (Jessie) Li, PhD

Assistant Professor of Information Sciences and Technology

Pennsylvania State University

 

 

“Toward Semantic Understanding of Spatial Trajectories”

Abstract: How could we harness the increasingly available big data to understand our dynamic ecosystem? For example, why people or animals move in the space in certain ways and how do their movements respond to surrounding environments? Why are crimes more frequent in certain regions and can we explain it using heterogeneous urban data? Is shale gas development contaminating our environment and how to mine the correlations between environment and all potential factors?

Our research aims to develop data mining techniques for geospatial data collected from different sources to semantically understand trajectories, urban dynamics, and environment, by closely collaborating with domain experts. In this talk, I will focus on data mining techniques to understand spatial trajectories. I will first discuss why existing methods often make trivial discoveries when contexts are not considered. I will then present our recent results in semantic understanding of trajectories with rich spatial-temporal contexts. I will also show that using cross-domain big data is critical to understand crimes and environment. Throughout the talk, I would like to share my experiences in exciting interdisciplinary collaborations.

Bio: Dr. Zhenhui (Jessie) Li is Assistant Professor of Information Sciences and Technology at the Pennsylvania State University. Prior to joining Penn State, she received her PhD degree in Computer Science from University of Illinois Urbana-Champaign in 2012, where she was a member of data mining research group. Her research has been focused on mining heterogeneous and large-scale geospatial data with applications in ecology, environment, social science, urban computing, and transportation. She is a passionate interdisciplinary researcher and closely collaborates with social scientists, animal scientists, criminologists, and geoscientists. To learn more, please visit her homepage: https://faculty.ist.psu.edu/jessieli

Lunch is served at 11:45 am.