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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.

NASEM Math & Statistics: Roundtable on Data Science Postsecondary Education

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The National Academies of Sciences, Engineering, and Medicine will hold a one-day meeting and webcast on “Improving Coordination Between Academia and Industry” on March, 29 2019 in Irvine, CA. The meeting will bring together data scientists and educators in academia, government, and industry to 1) discuss common challenges in establishing, maintaining, and evolving partnerships between academia and industry in data science education, including issues in data sharing, intellectual property, and confidentiality, and 2) learn about ongoing programs and partnerships at universities and research groups around the U.S.

Download the Draft Agenda

For more information, please visit the event website.

For this event, you have the option to register as an in-person participant or as a remote participant. Please select the appropriate ticket type so we can get a correct head count!

Instructions for Remote Participants

Watch the meeting here: https://livestream.com/accounts/15221519/DataScience

During the event, we encourage remote participants to send questions for the speakers to Ben Wender at bwender@nas.edu, who will read them out if time permits. Please note that the afternoon breakout session will not be webcast.

ABOUT THE ROUNDTABLE

This event is part of an ongoing series of roundtable meetings that take place approximately four times per year. It is organized by the Committee on Applied and Theoretical Statistics in conjunction with the Board on Mathematical Sciences and Their Applications, the Computer Science and Telecommunications Board, and the Board on Science Education. Learn more about the roundtable and watch videos of past meetings on the roundtable website.

 

Register to Attend In Person or Online

NASEM Math & Statistics: Roundtable on Data Science Postsecondary Education

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The National Academies of Sciences, Engineering, and Medicine invites you to attend a one-day data science education meeting and webcast on December 10, 2018 in Washington, DC. The meeting will bring together data scientists and educators in academia, government, and industry to 1) learn about academic, government, non-profit, and private sector projects promoting data science for socially desirable outcomes and their intersection with education and hiring, and 2) explore how socially motivated projects and topics can engage and excite students. For more information, please visit the event website.

ABOUT THE ROUNDTABLE

This event is part of an ongoing series of roundtable meetings that take place approximately four times per year. It is organized by the Committee on Applied and Theoretical Statistics in conjunction with the Board on Mathematical Sciences and Their Applications, the Computer Science and Telecommunications Board, and the Board on Science Education. Learn more about the roundtable and watch videos of past meetings on the roundtable website.

Learn more about the roundtable and watch past meetings at nas.edu/dsert.

 

Register to Attend In Person or Online

New course for fall 2018: On-Ramp to Data Science for Chemical Engineers

By | Educational, General Interest, Happenings, News

Description: Engineers are encountering and generating a ever-growing body of data and recognizing the utility of applying data science (DataSci) approaches to extract knowledge from that data. A common barrier to learning DataSci is the stack of prerequisite courses that cannot fit into the typical engineering student schedule. This class will remove this barrier by, in one semester, covering essential foundational concepts that are not part of many engineering disciplines’ core curricula. These include: good programming practices, data structures, linear algebra, numerical methods, algorithms, probability, and statistics. The class’s focus will be on how these topics relate to data science and to provide context for further self-study.

Eligibility: College of Engineering students, pending instructor approval.

More information: http://myumi.ch/LzqPq

Instructor: Heather Mayes, Assistant Professor, Chemical Engineering, hbmayes@umich.edu.

Dinov article: Building consensus on data science education and training

By | Research

Dr. Ivo Dinov, professor of Computational Medicine and Bioinformatics, Human Behavior, and Biological Science, and associate director of MIDAS, recently published an article on the training and education curricula needs of data science.

Title: Quant data science meets dexterous artistry
Published in: International Journal of Data Science and Analytics
DOI: 10.1007/s41060-018-0138-6
Author: Ivo D Dinov
Abstract: Data science is a bridge discipline connecting fundamental science, applied disciplines, and the arts. The demand for novel data science methods is well established. However, there is much less agreement on the core aspects of representation, modeling, and analytics that involve huge and heterogeneous datasets. The scientific community needs to build consensus about data science education and training curricula, including the necessary entry matriculation prerequisites and the expected learning competency outcomes needed to tackle complex Big Data challenges. To meet the rapidly increasing demand for effective evidence-based practice and data analytic methods, research teams, funding agencies, academic institutions, politicians, and industry leaders should embrace innovation, promote high-risk projects, join forces to expand the technological capacity, and enhance the workforce skills.

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

NASEM Webinar: Data Science for Undergraduates – Opportunities & Options

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Data Science for Undergraduates: Report Release Webinar

As our economy, society, and daily life become increasingly dependent on data, new college graduates entering the workforce need to have the skills to analyze data effectively. At the request of the National Science Foundation, the National Academies of Sciences, Engineering, and Medicine organized a study to explore what data science skills are essential for undergraduates and how academic institutions should structure their data science education programs. We invite you to join us for a report release webinar on May 2, 2018 at 11am ET. During this webinar, study co-chairs Laura Haas and Alfred Hero will discuss the report’s findings and recommendations, followed by a question and answer session with webinar participants.

Learn more about the study, download the interim and final reports, and watch past webinars on the study webpage at nas.edu/EnvisioningDS.

Register for the Webinar.

WEBINAR INSTRUCTIONS

Click here to join the webinar

Password: data


NASEM Webinar: Data Science for Undergraduates – Opportunities & Options

By | Al Hero, Educational, News

As our economy, society, and daily life become increasingly dependent on data, new college graduates entering the workforce need to have the skills to analyze data effectively.

At the request of the National Science Foundation, the National Academies of Sciences, Engineering, and Medicine organized a study to explore what data science skills are essential for undergraduates and how academic institutions should structure their data science education programs.

We invite you to join us for a report release webinar on May 2, 2018 at 11am ET.

During this webinar, study co-chairs Laura Haas and Alfred Hero will discuss the report’s findings and recommendations, followed by a question and answer session with webinar participants. Learn more about the study, download the interim and final reports, and watch past webinars on the study webpage at nas.edu/EnvisioningDS.

Register

WEBINAR INSTRUCTIONS
Click here to join the webinar
Password: data

Women in Data Science: Stanford University, March 5, 2018

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Women in Data Science (WiDS) Conference

and Datathon

Registration for Livestream

Schedule

The Global Women in Data Science (WiDS) Conference aims to inspire and educate data scientists worldwide, regardless of gender, and support women in the field. This annual one-day technical conference provides an opportunity to hear about the latest data science related research and applications in a broad set of domains, All genders are invited to participate in the conference, which features exclusively female speakers.

Next WiDS Conference: March 5, 2018 at Stanford University & 100+ locations worldwide
WiDS will be held at Stanford university, and at 100+ regional events hosted by WiDS Ambassadorsand available via livestream. The 2018 program will feature fantastic speakers on a broad array of topics ranging from cybersecurity to astrophysics to computational finance, and more. Register now for an event near you.

New for 2018: WiDS Datathon
This year, we’ll be conducting the first-ever WiDS Datathon, a joint effort between Stanford, Kaggle (a Google company), Intuit, InterMedia (a recipient of the Bill & Melinda Gates foundation, and West Big Data Innovation Hub.. The datathon runs from February 1-28, 2018, and winners will be announced at our March 5, 2018, conference at Stanford.

2017 Conference Highlights

  • 75,000+ participants from 75 countries via live stream and Facebook Live, at regional events or online
  • 80+ regional events worldwide from 30 countries, simultaneous or delayed broadcast, many with regional speakers.
  • #WiDS2017 hashtag trended on Twitter all day long
  • WiDS Stanford: 400 attendees from 31 universities and 114 companies and other organizations​, with 1/3 students and 2/3 academics and industry professionals
  • 33 distinguished female speakers, moderators, and panelists

MIDAS Working Group: Teaching Data Science

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The Michigan Institute for Data Science (MIDAS) continues to convene a working group on teaching data science. As we incorporate data science into almost every level of teaching, many issues need to be thoroughly thought out: How do we teach data science to students with various levels of preparation, from those with little quantitative training to STEM students? How do we build data science modules to incorporate into existing domain science courses? How do we raise awareness of ethics and social responsibility in data science teaching? How do we teach data science to independent researchers, including faculty, who want to build data science into their research? What teaching resources are available at UM? Our working group welcomes anyone interested in these topics. We are developing an interdisciplinary team to foster new ideas and collaborations in the development of data science teaching methods and materials.

Please RSVP.  

The agenda for the meeting includes:

  • Introduction
  • Short presentations
    • Kerby Shedden (Professor, Statistics, and CSCAR director) will share insight from his experience teaching “capstone” style courses for undergraduate and MS students, based around case studies and focus on methods, formulating good questions, and writing.
    • Heather Mayes (Assistant Professor, Chemical Engineering) will talk about the design of a Data Science ramp-up course for engineering students and how to integrate it with existing course offerings.
    • Aaron Keys (data scientist, Airbnb) will give the industry perspective on the various training paths that students can take for a career in data science.
  • Open discussion of ideas and collaboration, and sharing resources

For questions, please contact Jing Liu, MIDAS Senior Scientist and Industry Partnership Leader (ljing@umich.edu734-764-2750).