ASA Symposium on Data Science & Statistics

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SAVE THE DATE FOR SDSS 2018!

Beyond Big Data: Leading the Way

The ASA’s newest conference, the Symposium on Data Science & Statistics, will take place in Reston, Virginia, May 16-19, 2018. The symposium is designed for data scientistscomputer scientists, and statisticians analyzing and visualizing complex data.

The annual SDSS will combine data science and statistical machine learning with the historical strengths of the Interface Foundation of North America (IFNA) in computational statistics, computing science, and data visualization. It will continue the IFNA’s tradition of excellence by providing an opportunity for researchers and practitioners to share knowledge and establish new collaborations.

Offering sessions centered on the following six topic areas:
Data Science                                            Data Visualization
Machine Learning                                  Computing Science
Computational Statistics                      Applications

Key Dates:
December 5, 2017 – Contributed and E-Poster Online Abstract Submission Opens
January 18, 2018 – Contributed and E-Poster Online Abstract Submission Closes
February 1, 2018 – Conference Registration Opens

Video available from MIDAS Research Forum

By | General Interest, Happenings, News, Research

Video is now available from the MIDAS Research Forum held Dec. 1 in the Michigan League at http://myumi.ch/6vA3V

The forum featured U-M students and faculty showcasing their data science research; a workshop on how to work with industry; presentations from student groups; and a summary of the data science consulting and infrastructure services available to the U-M research community.

NOTE: The keynote presentation from Christopher Rozell of the Georgia Institute of Technology will be available in the near future.

PyData December Meetup: Drew Fustin, PhD, SpotHero

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Join us for a PyData Ann Arbor Meetup on Thursday, December 7, at 6 PM, hosted by TD Ameritrade and MIDAS.

Although ideal experimental design involves hypothesis testing with randomized controlled trials on concurrent populations to minimize selection bias and convoluting variables, it often arises that experiments cannot be run on variant populations simultaneously — for instance, when a stimulus necessarily impacts the entire population at a specific point in time (as in measuring a non-digital ad campaign’s effectiveness). Dealing with these situations is common in the social sciences, where the method of Interrupted Time Series Analysis is commonly used. In order to measure the effect size of a stimulus in situations such as these, we have to consider many convoluting factors caused by our populations being non-concurrent before deriving meaning from the experimental results.

In this Python-based tutorial, we will walk through a Monte Carlo-generated experiment measuring the lift induced by a simulated ad campaign. By the end of this tutorial, you will understand many complicating factors that could lead to faulty conclusions being drawn from the experimental results. However, you will also learn how best to design experiments and interpret results to mitigate these risks and take proper account of irreducible convoluting factors.

About: Drew Fustin is a former physicist and current data scientist in Chicago. He created and led the data science organization at SpotHero, focusing primarily on optimizing acquisition marketing spend and balancing supply and demand to generate inventory and rate recommendations. He’s also worked for GrubHub as the insights analyst, turning food facts into media content for the PR department and transforming data into actionable initiatives within the organization. He was also a data scientist with Digital H2O, a SaaS startup providing water intelligence for the oil/gas industry. He holds a PhD in physics from the University of Chicago, where he studied dark matter by looking for tiny bubbles in a chamber over a mile underground in a Canadian nickel mine.

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”

Data Science for Music Challenge Initiative RFP released

By | Funding Opportunities, General Interest, News, Research

The Data Science for Music Challenge Initiative will award four, one-year  grants of up to $75,000 each for research projects at the intersection of music and data science.

A two-page letter of intent is due on January 19, 2018.

Project principal investigators and co-principal investigators must be faculty members at the University of Michigan, Ann Arbor campus. Multi-disciplinary teams are encouraged to apply. An individual may participate as PI/co-PI on only one full proposal.

For more details, visit midas.umich.edu/music. To view the RFP, visit midas.umich.edu/rfp, or download directly.

For questions, please contact Jing Liu, MIDAS senior scientist, ljing@umich.edu, 734-764-2750

2017 U-M Data Science Research Forum

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Forum Highlights

  • Oral and poster presentations on
    • Theoretical foundations of data science
    • Data science methodology
    • Data science applications in any research domain
    • Social impact of data science research
  • Networking Reception

All presentations will come from submissions in response to our call for abstracts.
Oral presentations are closed. Posters are welcome and encouraged. 

Abstract Submission – Posters Only – Deadline: November 15, 2017

We welcome submission from all U-M data science researchers (faculty, staff, trainees)

Please register for this event.  Please also see the call for abstracts for instruction, and submit through the Abstract Submission Form.

Preliminary Schedule

Downloadable Flyer

Research Poster Printing options.

PyData November Meetup: David Rogers, MS, Sight Machine

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“Delivering Data Products for Operations”

Join us for a PyData Ann Arbor Meetup on Tuesday, November 14, at 6 PM, hosted by TD Ameritrade and MIDAS.

David Rogers from Sight Machine will discuss the technical and non-technical aspects of delivering a scalable data product for use in enterprise operations. The aspects, ranging from data pipelining to customer education, will be blended with examples and anecdotes from my experiences delivering data products for some of the largest companies in the world.

David Rogers is the Lead Data Scientist at Sight Machine, where he solves complex manufacturing problems for Global 500 companies with Digital Twin and AI technologies. His background includes full stack software development and applying system thinking for Boeing and nonprofit organizations. David holds a BS in computer engineering from Michigan State University and an MS in systems engineering from the University of Virginia.

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”

The National Academies Webinar Series: Data Science Undergraduate Education

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Webinar

 

REGISTRATION

Webinar Series: Data Science Undergraduate Education

Join the National Academies of Sciences, Engineering, and Medicine for a webinar series on undergraduate data science education. Webinars will take place on Tuesdays from 3-4pm ET starting on September 12 and ending on November 14. See below for the list of dates and themes for each webinar.

This webinar series is part of an input-gathering initiative for a National Academies study on Envisioning the Data Science Discipline: The Undergraduate Perspective. Learn more about the study, read the interim report, and share your thoughts with the committee on the study webpage at nas.edu/EnvisioningDS.

Webinar speakers will be posted as they are confirmed on the webinar series website.

Webinar Dates and Topics

•    9/12/17 – Building Data Acumen
•    9/19/17 – Incorporating Real-World Applications
•    9/26/17 – Faculty Training and Curriculum Development
•    10/3/17 – Communication Skills and Teamwork
•    10/10/17 – Inter-Departmental Collaboration and Institutional Organization
•    10/17/17 – Ethics
•    10/24/17 – Assessment and Evaluation for Data Science Programs
•    11/7/17 – Diversity, Inclusion, and Increasing Participation
•    11/14/17 – Two-Year Colleges and Institutional Partnerships

All webinars take place from 3-4pm ET. You will have the option to register for the entire webinar series or for individual webinars.

Info Session: Consulting and computing resources for data science

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Advanced Research Computing at U-M (ARC) will host an information session for graduate students in all disciplines who are interested in new computing and data science resources and services available to U-M researchers.

Brief presentations from members of ARC Technology Services (ARC-TS) on computing infrastructure, and from Consulting for Statistics, Computing, and Analytics Research (CSCAR) on statistics, data science, and computing training and consulting will be followed by a Q&A session, and opportunities to interact individually with ARC and CSCAR staff.

ARC and CSCAR are interested in connecting with graduate students whose research would benefit from customized or innovative computational or analytic approaches, and can provide guidance for students aiming to do this. ARC and CSCAR are also interested in developing training and documentation materials for a diverse range of application areas, and would welcome input from student researchers on opportunities to tailor our training offerings to new areas.

Speakers:

  • Kerby Shedden, Director, CSCAR
  • Brock Palen, Director, ARC-TS

The National Academies Webinar Series: Data Science Undergraduate Education

By |

Webinar

 

REGISTRATION

Webinar Series: Data Science Undergraduate Education

Join the National Academies of Sciences, Engineering, and Medicine for a webinar series on undergraduate data science education. Webinars will take place on Tuesdays from 3-4pm ET starting on September 12 and ending on November 14. See below for the list of dates and themes for each webinar.

This webinar series is part of an input-gathering initiative for a National Academies study on Envisioning the Data Science Discipline: The Undergraduate Perspective. Learn more about the study, read the interim report, and share your thoughts with the committee on the study webpage at nas.edu/EnvisioningDS.

Webinar speakers will be posted as they are confirmed on the webinar series website.

Webinar Dates and Topics

•    9/12/17 – Building Data Acumen
•    9/19/17 – Incorporating Real-World Applications
•    9/26/17 – Faculty Training and Curriculum Development
•    10/3/17 – Communication Skills and Teamwork
•    10/10/17 – Inter-Departmental Collaboration and Institutional Organization
•    10/17/17 – Ethics
•    10/24/17 – Assessment and Evaluation for Data Science Programs
•    11/7/17 – Diversity, Inclusion, and Increasing Participation
•    11/14/17 – Two-Year Colleges and Institutional Partnerships

All webinars take place from 3-4pm ET. You will have the option to register for the entire webinar series or for individual webinars.