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Geospatial analysis with Google Earth Engine

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Google Earth Engine (GEE) combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities. This workshop will provide an introduction to GEE. We will cover data models in GEE, basic vector and raster operations, and classification in both feature and image space.

You should be familiar with vector and raster data, GIS and remote sensing. We will use the web-based IDE for the Earth Engine JavaScript API. You will need to register (free) at signup.earthengine.google.com with Google to use the Earth Engine.

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

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).

Mini-course: Introduction to Python — Sept. 11-14

By | Data, Educational, Events, General Interest, News

Asst. Prof. Emanuel Gull, Physics, is offering a mini-course introducing the Python programming language in a four-lecture series. Beginners without any programming experience as well as programmers who usually use other languages (C, C++, Fortran, Java, …) are encouraged to come; no prior knowledge of programming languages is required!

For the first two lectures we will mostly follow the book Learning Python. This book is available at our library. An earlier edition (with small differences, equivalent for all practical purposes) is available as an e-book. The second week will introduce some useful python libraries: numpyscipymatplotlib.

At the end of the first two weeks you will know enough about Python to use it for your grad class homework and your research.

Special meeting place: we will meet in 340 West Hall on Monday September 11 at 5 PM.

Please bring a laptop computer along to follow the exercises!

Syllabus (Dates & Location for Fall 2017)

  1. Monday September 11 5:00 – 6:30 PM: Welcome & Getting Started (hello.py). Location: 340 West Hall
  2. Tuesday September 12 5:00 – 6:30 PM: Numbers, Strings, Lists, Dictionaries, Tuples, Functions, Modules, Control flow. Location: 335 West Hall
  3. Wednesday September 13 5:00 – 6:30 PM: Useful Python libraries (part I): numpy, scipy, matplotlib. Location: 335 West Hall
  4. Thursday September 14 5:00 – 6:30 PM: Useful Python libraries (part 2): 3d plotting in matplotlib and exercises. Location: 335 West Hall

For more information: https://sites.lsa.umich.edu/gull-lab/teaching/physics-514-fall-2017/introduction-to-python/

 

Midwest Big Data Hub Transportation and Mobility Conference, Ann Arbor, MI

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signature-informal-verticalMBDH logo

The Midwest Big Data Hub

Conference

 

 

 

 

“Big Data for Transportation and Mobility”

 

Please Register

Details are on the main conference webpage.

 

The NSF supported Midwest Big Data Hub has made data for transportation a priority.  Its goal is to bring together experts in the increasingly powerful tools of Big Data (including visualization, machine learning, statistical models, integration of heterogeneous data, data scrubbing, privacy and security) with domain experts.  The Midwest has been a center of innovation in transportation for generations and data-related research has become a major focus of interest for corporate, academic, and governmental organizations.  This conference includes a series of presentations providing an overview of research underway in the Midwest hat will help us understand the scope of this work, encourage cross-fertilization, and possibly nucleate future collaborations.

One of the highlights of the meeting will be a series of talks by faculty from the schools that are the core of the Midwest Big Data Hub.  Additional activities include tours of research sites on the Ann Arbor Campus, short tutorials and breakout sessions on pertinent topics. 

Students are especially welcome and a poster session, part of the conference reception on June 22, will provide an opportunity for them to share their research. There is some limited travel and hosting funding available for students; restrictions apply and requests for travel/ hosting support should be submitted while registering. 

The Midwest Big Data Hub is supported by the National Science Foundation through award #1550320. 

MBDH-NSF Attribution MBDH logo

Frontiers in Data Science and Computing, Michigan State University

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The Frontiers in Data Science and Computing workshop brings together visionaries, scientists, practitioners and other stakeholders in computational science that includes both algorithmic aspects of data science and scientific computing.  Our model for the Frontiers workshop series is based on the successful format commonly seen in Silicon Valley, which combines “big picture” motivation talks with activities designed to foster ideas, create communities, and allow significant interactions between application/domain scientists, algorithm designers and end users.  Based on the successful outcomes of our pilot Frontiers workshop in 2015, we anticipate that this annual workshop series will result in fruitful scientific exchanges and new interdisciplinary collaborations across academia, industry and government agencies.  In 2016, the Frontiers workshop will focus on data-infused computing, which is a new area that merges data science with scientific computing.  “Frontiers in Data Science and Computing 2016” will be one of the first venues to explore this topic and its high-impact potential in areas as diverse as medicine, astronomy, physics, biology, computer architecture, smart grids, and climate change.

Workshop: Refining the Concept of Scientific Inference When Working with Big Data — June 8-9 (webcast)

By | General Interest, News

The National Academies of Sciences, Engineering and Medicine Committee on Applied and Theoretical Statistics is holding a workshop titled “Refining the Concept of Scientific Inference When Working With Big Data” in Washington DC, June 8-9.

The workshop will bring together statisticians, data scientists and domain researchers from different biomedical disciplines to explore four key issues of scientific inference:

  • Inference about causal discoveries driven by large observational data
  • Inference about discoveries from data on large networks
  • Inference about discoveries based on integration of diverse datasets
  • Inference when regularization is used to simplify fitting of high-dimensional models

Prof. Alfred Hero, co-director of the Michigan Institute for Data Science (MIDAS) and Professor of Electrical Engineering and Computer Science, is a co-chair of the event.

More information:

 

Engaging with DARPA, A Presentation by DSO Director Stefanie Thompkins – May 31

By | General Interest, News

Please join us for a presentation and overview of DARPA and the Defense Sciences Office by Dr. Stefanie Tompkins, Defense Sciences Office Director.

Date:  May 31, 2016
Time: 12:30 PM
Location: Room 1500 EECS, North Campus

DARPA’s mission is to make pivotal investments in breakthrough technologies for national security, thus catalyzing the development of capabilities that give the Nation new options for preventing and creating strategic surprise.

The Defense Sciences Office (DSO) is one of six technical offices at the agency.  DSO identifies and pursues high-risk, high-payoff fundamental research initiatives across a broad spectrum of science and engineering disciplines including materials science, computing and autonomy, engineering design and manufacturing, physics, chemistry, mathematics and social science.

This presentation will give an overview of DARPA, working with DARPA and the Defense Sciences Office, and description of some of the current activities DSO’s program managers are working on.

Workshops on Algorithms for Modern Massive Data Sets — June 21-24, Berkeley

By | General Interest, News

Registration for the 2016 Workshop on Algorithms for Modern Massive Data Sets (MMDS 2016) is now available.

In addition to four days of talks on algorithmic and statistical aspects of modern large-scale data analysis, MMDS 2016 will have a contributed poster session one evening.  The registration fee is waived for student poster presenters.  You may apply to present a poster at the event website.

Event: MMDS 2016: Workshop on Algorithms for Modern Massive Data Sets
Dates: June 21-24, 2016
Location: UC Berkeley, Berkeley, CA
Website: http://mmds-data.org
Contact: organizers@mmds-data.org
Synopsis: The 2016 Workshop on Algorithms for Modern Massive Data Sets (MMDS 2016) will address algorithmic, mathematical, and statistical challenges in modern statistical data analysis. The goals of MMDS 2016 are to explore novel techniques for modeling and analyzing massive, high-dimensional, and nonlinearly-structured scientific and internet data sets, and to bring together computer scientists, statisticians, mathematicians, and data analysis practitioners to promote cross-fertilization of ideas.
Organizers: Michael Mahoney (UC Berkeley), Alex Shkolnik (Stanford), and Petros Drineas (RPI)

Environmental Data Commons Workshop — June 9, Chicago

By | General Interest, News

The Center for Data Intensive Science at the University of Chicago is hosting a one day workshop in Chicago on June 9, 2016 on environmental data commons and data sharing.

There will be sessions on the environmental commons, services for environmental commons, environmental data commons applications, the NOAA Big Data Alliance, and interoperability of environmental commons, clouds, and repositories.

To register and for more information including workshop location, agenda, and options for lodging, please visit the event website.