Community Spotlights

Staff from the San Diego Supercomputer Center (SDSC) at UC San Diego - including West Hub Science Writer Kim Bruch - recently worked with middle and high school students from the Pala Native American Youth Council to conduct an informal data science study. The students specifically examined pH levels of the San Luis Rey River that flows through Pala tribal land and then compared their local findings with a larger data set from the California Natural Resources Agency.

We invite you to join us for the Learning and Doing Data for Good Conference on September 9-11, 2022, co-hosted by the University of Washington's eScience Institute and the West Hub. At this forum, current students and alumni of university-based data for good programs will have the opportunity to present their work alongside past partners and data science professionals. We hope this will be a space of interesting and inspiring discussions, as well as a time for connecting with colleagues and partners who are motivated to learn from and meet the needs of communities and people using data for change. In-person participation on the University of Washington campus and virtual participation are available. Please register here, and for any questions, please contact the Conference Director, Juandalyn Burke, at jcburke@uw.edu.

Data Matters Short-Course Series | August 8 - 12

Data Matters™ is a week-long series of one and two-day courses aimed at students and professionals in business, research, and government. Sponsored by the Odum Institute for Research in Social Science at UNC-Chapel Hill, the National Consortium for Data Science, and RENCI, the short-course series gives students the chance to learn about a wide range of topics in data science, analytics, visualization, curation, and more from expert instructors. Among the classes available are:

• Overview of AI and Deep Learning, Ashok Krishnamurthy. Many key advances in AI are due to advances in machine learning, especially deep learning. Natural language processing, computer vision, speech translation, biomedical imaging, and robotics are some of the areas that have benefited from deep learning methods. This course is designed to provide an overview of AI, and in particular, deep learning. Topics include the history of neural networks, how advances in data collection and computing have caused a revival in neural networks, different types of deep learning networks and their applications, and tools and software available to design and deploy deep networks.​

• Introduction to Effective Information Visualization, Eric Monson. Visualization is a powerful way to reveal patterns in data, attract attention, and get your message across to an audience quickly and clearly.  However, there are many steps in that journey from information to influence, and many questions – what visualization tools to use, how to get data into the right format, and which choices to make when putting it all together to tell your story? This course will quickly walk participants through a wide variety of data and chart types to help even beginners feel comfortable embarking on a new visualization project.​

• Introduction to Discrete Choice Modeling in R, Matt Bhagat-Conway. This course introduces participants to discrete choice models, econometric models of how people choose between discrete outcomes, such as mode of travel to work or type of treatment for pain. The course will cover the subset of discrete choice models known as random utility models. These models are often used in disciplines such as economics, transportation, and public health. No prior knowledge is expected, and the course will cover logistic regression, multinomial logistic regression, and nested logistic regression. Hands-on exercises will be conducted in R.​

• Introduction to Programming in R, Jonathan Duggins. Statistical programming is an integral part of many data-intensive careers and data literacy, and programming skills have become a necessary component of employment in many industries. This course begins with necessary concepts for new programmers—both general and statistical—and explores some necessary programming topics for any job that utilizes data. ​

• Geospatial Analytics Using Python, Laura Tateosian. This course will focus on how to explore, analyze, and visualize geospatial data. Using Python and ArcGIS Pro, students will inspect and manipulate geospatial data, use powerful GIS tools to analyze spatial relationships, link tabular data with spatial data, and map data. In these activities, participants will use Python and the arcpy library to invoke key GIS tools for spatial analysis and mapping. 

Register by August 3 for Monday/Tuesday courses, August 4 for Wednesday courses, and August 7 for Thursday/Friday courses. Learn more and register here.

The Big Data Neuroscience (BDN) workshop, organized by the Advanced Computational Neuroscience Network, brings together researchers in neuroscience, computer science, statistics, computer engineering, structural biology, neuroinformatics, and related disciplines. The workshop aims to bring together a transdisciplinary set of speakers to educate trainees and faculty on tackling society's biggest challenges in critical areas of interdisciplinary strength, including health & well-being and technology & society. Submit abstracts and applications for travel scholarships by August 18, 2022. The BDN workshop will be held in Austin, Texas, September 15-16, 2022.