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#UMTweetCon2019

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A Conference on the Use of Twitter Data for Research and Analytics

 

#UMTweetCon2019 will connect U-M scholars across a diverse set of disciplines in an interdisciplinary exchange about common challenges and lessons learned. We further seek to facilitate new connections to help U-M scholars create opportunities for future joint research, collaborative grant writing, training and other activities. Conference attendance will be open to anyone interested in learning about the wide array of Twitter data applications in current research at the University. The conference is sponsored by the Social Science and Social Media Collaborative, the Michigan Institute for Data Science, the #Parenting Rackham Interdisciplinary Group, and coordinated by the Center for Political Studies and the Institute for Social Research.

Call for Abstracts

Do you use Twitter data in your research? Then, you are invited to submit an abstract for the first

 university wide conference at the University of Michigan (Ann Arbor, Dearborn, and Flint) on the use of Twitter data in research and analytics. #UMTweetCon2019 will connect U-M scholars across a diverse set of disciplines in an interdisciplinary exchange about common challenges and lessons learned. We further seek to facilitate new connections to help U-M scholars create opportunities for future joint research, collaborative grant writing, training and other activities. Conference attendance will be open to anyone interested in learning about the wide array of Twitter data applications in current research at the University.

To reflect the wide range of ongoing research across disciplines, we invite submissions that 1) directly examine dynamics of Tweet behavior and Twitter networks, 2) explore the representativeness and validity of Twitter data for making scientific inference, 3) develop new computational methodology for obtaining, processing, or archiving Twitter data, or 4) present applications of Twitter data for studying diverse social phenomena. During the 2-day conference, research presentations will be complemented with participatory sessions to provide participants with an opportunity to plan future activities and help create a regular user community across campuses (e.g., seminar series, computational training sessions, hackathons, regular coding meetups, etc.)

Interested U-M researchers are asked to use the form linked here to submit a short abstract of 200-300 words in length that describes their research project, along with information about participating co-authors. Submissions are due by Friday April 12, 2019.

Click here to submit an abstract for a panel or poster presentation.

Attending #UMTweetCon2019 will require a small, non-refundable registration fee from presenters and attendees alike (students/post-docs: $15 pre-conference online, $20 on-site; faculty/staff/other: $30 pre-conference online, $40 on-site). Presenters and attendees from Dearborn and Flint campuses will receive a registration discount (students/post-docs: $15, faculty/staff/other: $20). We will use the revenue from registration fees to fund best paper awards.

PyData July Meetup: Designing an Algorithmic Trading Strategy with Python

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

Gus Gordon is a Data Engineer at Quantopian, an algorithmic investing platform and hedge fund manager. He works on the research team, developing tools for analyzing financial data and evaluating the performance of trading strategies. Gus studied physics and economics at Bucknell University.

In this talk, Gus will go through a clean example of how to design a financial trading strategy using open source Python tools. We’ll start off by analyzing a raw trading signal in alphalens, then transition that signal into an algorithm that we can backtest with zipline. Finally, we’ll review the results of the backtest by going through some plots generated by pyfolio.

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 June Meetup: Intro to Azure Machine Learning: Predict Who Survives the Titanic

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Join us for a PyData Ann Arbor Meetup on Thursday, June 8th, at 6 PM, hosted by TD Ameritrade and MIDAS.

Interested in doing machine learning in the cloud? In this demo-heavy talk, Jennifer Marsman will set the stage with some information on the different types of machine learning (clustering, classification, regression, and anomaly detection) supported by Azure Machine Learning and when to use each. Then, for the majority of the session, she’ll demonstrate using Azure Machine Learning to build a model which predicts survival of individuals on the Titanic (one of the challenges on the Kaggle website). She’ll talk through how she analyzes the given data and why she chooses to drop or modify certain data, so you will see the entire process from data import to data cleaning to building, training, testing, and deploying a model. You’ll leave with practical knowledge on how to get started and build your own predictive models using Azure Machine Learning.

Jennifer Marsman is a Principal Software Development Engineer in Microsoft’s Developer Experience group, where she educates developers on Microsoft’s new technologies with a focus on data science, machine learning, and artificial intelligence. Jennifer blogs at http://blogs.msdn.microsoft.com/jennifer and tweets at http://twitter.com/jennifermarsman.

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 May Meetup: Scalable, Distributed, and Reproducible Machine Learning

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Join us for a PyData Ann Arbor Meetup on Thursday, May 25th at 6 PM, hosted by TD Ameritrade and MIDAS.

The recent advances in machine learning and artificial intelligence are amazing!  Yet, in order to have real value within a company, data scientists must be able to get their models off of their laptops and deployed within a company’s data pipelines and infrastructure.  Those models must also scale to production size data. In this talk, we will implement a model locally in Python. We will then take that model and deploy both it’s training and inference in a scalable manner to a production cluster with Pachyderm, an open source framework for distributed pipelining and data versioning. We will also learn how to update the production model online, track changes in our model and data, and explore our results.

Daniel Whitenack (@dwhitena) is a Ph.D. trained data scientist working with Pachyderm (@pachydermIO). Daniel develops innovative, distributed data pipelines which include predictive models, data visualizations, statistical analyses, and more. He has spoken at conferences around the world (ODSC, Spark Summit, Datapalooza, DevFest Siberia, GopherCon, and more), teaches data science/engineering with Ardan Labs (@ardanlabs), maintains the Go kernel for Jupyter, and is actively helping to organize contributions to various open source data science projects.

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 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”