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National Workshop on Data Science Education Featured Multiple Hub Talks

Kim Bruch, West Big Data Innovation Hub Science Writer

Organized by UC Berkeley’s Division of Computing, Data Science, and Society, with support from Microsoft and the West Big Data Innovation Hub, the Summer 2022 National Workshop on Data Science Education offered an array of insight about current data science education initiatives across the academic spectrum, from high school to undergraduate and graduate level programs as well as adult learners.

The latter two days of the workshop focused on national perspectives and programs for data science education, including student driven data science communities of support and learning. The National Science Foundation (NSF) Big Data Innovation Hubs hosted two panels alongside a program of presenters that discussed topics such as investigating the ethics behind algorithms, incorporating Python into statistics and computer science classes, and the latest developments in data science education and community building.

“The West Hub was happy to coordinate the NSF Big Data Hubs’ contribution to this workshop,” said West Big Data Innovation Hub Executive Director Ashley Atkins. “It was an opportunity to share with a national audience some of the undergraduate-focused work the Hubs are pursuing across the country.”

Many lessons learned were discussed during the NSF Big Data Hub panel entitled “Building National Capacity for Student-Driven Data Science Communities.” The panel was moderated by Northeast Big Data Innovation Hub Executive Director Florence Hudson and included presentations by John MacMullen, Emily Rothenberg, Scott Blender, Abhishek Sinha and Rajeev Bukralia.

“The National Student Data Corps began as a grassroots effort in the Northeast region in 2021, and grew to nearly 3,000 community members by June 2022 across the U.S. and in 20 countries around the world,” said Hudson. “Students, professors, industry and nonprofit data science professionals worked together to build this dynamic community of support to increase data science awareness and provide free open online data science resources for students and educators, along with data science career panels, mentoring via a 500-person slack channel, career and chapter resources. We are working together to democratize data science for all.”

Temple University Engineering and Data Science Student Scott Blender talked about the National Student Data Corps (NSDC) from a student perspective—focusing on goals of the chapter systems. He said that their aim is “to inspire, educate, and serve local communities with professional development opportunities by leveraging NSDC resources and events.”

A similar student-aimed program discussed was the Midwest Big Data Innovation Hub’s Data Science Student Groups Community. Rajeev Bukralia, professor at Minnesota State University, Mankato, also spoke about his development of the Data Resources for Eager and Analytical Minds (DREAM) student group, which is the largest registered student organization on campus, and brings data science perspectives to students from many disciplines. Details about both DREAM and NSDC can be found on their respective websites.

“We are focused on building a group of student leaders to share best practices about how to grow inclusive, multi-disciplinary student organizations,” said Executive Director of the Midwest Big Data Innovation Hub John MacMullen. “Learning from more established groups such as DREAM can help newer student organizations understand how to build strong, diverse teams with engaged participants.”

Another great NSF Big Data Hub Panel at the workshop was entitled Data Science Program Development. South Big Data Hub Executive Director Renata Rawlings-Goss of Georgia Tech opened the panel with a thorough explanation of how they developed their data science education efforts.

West Hub principal investigator Jennifer Chayes gave an overview of Berkeley’s Division of Computing, Data Science, and Society (CDSS), where she serves as associate provost.

Eric Van Dusen speaking during a panel discussion. Photo by KLCfotos.
Workshop organizer Eric Van Dusen, outreach and technology lead for the Data Science Undergraduate Studies program, speaks during a panel discussion. (Photo/ KLCfotos)

“This is the fifth annual conference and the West Big Data Hub has always been a key partner-stakeholder in convening folks in this space. It was great to have multiple hubs collaborating to share so many perspectives,” said CDSS Technology and Outreach Lead Eric Van Dusen, who organized the workshop.

Student Group Profile: DREAM at Minnesota State University, Mankato

The Midwest Big Data Innovation Hub is developing a community of data science student groups across the Midwest region to share their experiences and best practices. This story is part of a series of student group profiles.

For this profile, we spoke with leaders from DREAM, Data Resources for Eager & Analytical Minds, a recognized student organization at Minnesota State University, Mankato. It has over 300 student members who focus on data science, data analytics, machine learning, artificial intelligence, information technology, and computer science. DREAM organizes and hosts conferences, trainings, competitions, and industry talks to support the students’ academic and professional development. The DREAM members have won many awards at various data science competitions and have authored dozens of research papers and conference presentations. DREAM is a past recipient of the Outstanding RSO of the Year award.

Minnesota State University, Mankato DREAM logo

What are the goals of your group, and who is your core audience?
DREAM was founded in 2016 when one dedicated data science professor at Minnesota State University, Mankato (MNSU), Dr. Rajeev Bukralia—the esteemed faculty advisor of DREAM—excited the students of the potential of and career opportunities in data science. Since the start, DREAM’s goal has been to explore, raise interest in, and share the wonders of data science and related fields. Our mission is to help students venture into the more interesting aspects of data science and corresponding fields, and in the process, connect students to industry mentors and professionals. We want to support anyone from any background who has interest in data analytics, data science, or computer science. Our core audience is varied because data itself is varied and can come from any field. Our audience is anyone who wants to understand that data on a deeper level, be they business majors, biology students, or just about anything else; we welcome anyone from any background who wants to participate!

What kinds of activities have you done previously, and what do you have planned for this year?
COVID has changed the format of our group considerably, but we still have regular industry talks and we act as a center for communicating events and opportunities to students interested in data science. Recently, we have had multiple industry leaders speak on their experiences working in the industry. They shared their experiences and tips to help set students up for success. So far this semester, we have hosted four industry talks with professionals from big companies such as UnitedHealth, One Drop, and Ovative. The larger projects we have planned for this semester focus around supporting students through the 2022 Data Derby Hackathon, setting up the spring election, and creating fun, themed training sessions for students to dip their toes into key tools for data science, such as Python and Power BI. We also hope to involve the members of our club in a student research showcase this spring in collaboration with MinneAnalytics.

As DREAM grows, we hope to expand our reach into the community. Through school or library programs, we hope to spark an interest in data science in kids grades 6 through 12. Programs like this would not only have to be volunteer-run, but also volunteer-created. So, after completing a few training sessions at the university, we hope to create an introductory data science curriculum that is interesting enough to captivate young students, but also approachable enough for young students.

What challenges have you faced in starting or maintaining your group?
The pandemic, of course, has been a large shift for a group like ours, which has over 300 students, dozens of which would be packed into a room eating pizza together on any given Thursday night pre-COVID. Since then, we have had to switch to Zoom for our meetings, although we’re trying to get back in person soon. There are also the general challenges of collaborating with university administration to secure and maintain the backend functions of the club and making sure to bring in a constant stream of new students to sustain the club.

What suggestions do you have for others who want to start a group on their campus, or expand their current group?
Reach out and promote your group through classes on your campus that are relevant—for example, we promote DREAM in the introductory data science courses and the database management courses.

Run events regularly—consistency will help build up more engagement, both from members of the group that are excited to participate more, or from members of the student body that just decide to pop into one meeting because they see it happening every week.

Keep a careful eye on your roster. Make sure you always have a copy backed up. Also, keep it organized so you can keep track of current students, alumni, etc. Your email roster is your direct point of contact with your group, so be sure to communicate with them regularly and to always maintain the current contact details.

Stay true to the mission. Be active and involved in community events. Try different methods to promote your group’s spirit and resources, such as Twitter and LinkedIn, etc.

Get involved

You can find the DREAM club on Twitter and their website.

Are you a student group leader or advisor? We’d like to hear more about your group’s activities. Contact us if you’d like us to profile your organization or participate in our student groups webinar series. You can also join our new Slack community to continue the discussion and make new connections.

About the Midwest Big Data Innovation Hub

The Midwest Big Data Innovation Hub is an NSF-funded partnership of the University of Illinois at Urbana-Champaign, Iowa State University, Indiana University, the University of Michigan, the University of Minnesota, and the University of North Dakota, and is focused on developing collaborations in the 12-state Midwest region. Learn more about the national NSF Big Data Hubs community.

Student Group Profile: Girls Who Code, University of Michigan DCMB

The Midwest Big Data Innovation Hub is developing a community of data science student groups across the Midwest region to share their experiences and best practices. This story is part of a series of student group profiles.

University of Michigan Girls Who Code logo

In light of Women’s History Month and International Women’s Day on March 8th, we talked with the leaders of Girls Who Code club at the University of Michigan about their work on empowering young girls to participate in coding projects and the STEM field by and large.

What are the goals of your group, and who is your core audience?
We are an organization founded by doctoral students from the Department of Computational Medicine and Bioinformatics at the University of Michigan. Our goal is to provide a collaborative and supportive environment for students of all skill levels and backgrounds interested in learning to code. Our club curriculum focuses on computational data analysis and the Python programming language. Participants learn fundamental coding concepts and implement their new skills in their chosen data science capstone project. Our core audience includes girls, women, and allies who support our mission of closing the gender gap in technology.

What kinds of activities have you done previously, and what do you have planned for this year?
Our Girls Who Code club meets weekly from September through May. During the summer, we offer a two-week intensive Summer Experience (SE) program. During club and SE, students participate in live coding lectures, work through paired programming exercises, hear from guest speakers, and complete a data science capstone project. We have also facilitated field trips to the Ann Arbor Google office and connect students to faculty at the University of Michigan for long-term research experiences. Along the way, we have partnered with other STEM outreach organizations at the University of Michigan. For instance, this year, we will collaborate with FEMMES (Women+ Excelling More in Math, Engineering, and the Sciences) and DFB (Developing Future Biologists) to provide hands-on programming activities.

University of Michigan Girls Who Code group photo

What challenges have you faced in starting or maintaining your group?
A primary challenge we faced in starting the club and SE programs was the lack of live-coded Python for data science curriculum for our target age group (high school). However, given the expertise of our student facilitators, we were able to develop a custom curriculum teaching Python fundamentals and data science skills, including statistical analysis, from scratch. We rely entirely on hard-working undergraduate, graduate, and postdoctoral volunteers, and recruiting volunteers who can dedicate time to this extracurricular activity is often difficult. To help address this challenge, we have started paying our SE instructors. The pandemic created a massive shift in how we delivered our programming, and we had to shift the club to a virtual format within a week. We have continued virtual instruction, and despite its challenges, we have been able to expand our reach.

University of Michigan Girls Who Code Zoom screenshot 1
University of Michigan Girls Who Code Zoom screenshot 2

What suggestions do you have for others who want to start a group on their campus, or expand their current group?
Find ways to collaborate with existing organizations so that you can build on their previous work instead of reinventing the wheel. Identify and understand the needs of the communities that you’re interested in working with to ensure that your programming aligns with your target audience. It’s also a good idea to consider your organization’s longevity and plan at the onset for the transfer of leadership responsibilities after the original leadership moves on. Creating documents that allow for knowledge transfer and working with faculty that can provide continuity are two such ways to address this.

Get involved

You can find the Girls Who Code club on Twitter, Facebook, and their website. The club has also compiled resources on coding, online teaching, and fostering diversity, equity, and inclusion on their GitHub page.

Are you a student group leader or advisor? We’d like to hear more about your group’s activities. Contact us if you’d like us to profile your organization or participate in our student groups webinar series. You can also join our new Slack community to continue the discussion and make new connections.

About the Midwest Big Data Innovation Hub

The Midwest Big Data Innovation Hub is an NSF-funded partnership of the University of Illinois at Urbana-Champaign, Iowa State University, Indiana University, the University of Michigan, the University of Minnesota, and the University of North Dakota, and is focused on developing collaborations in the 12-state Midwest region. Learn more about the national NSF Big Data Hubs community.

Student Group Profile: Iowa State University Data Science Club

The Midwest Big Data Innovation Hub is developing a community of data science student groups across the Midwest region to share their experiences and best practices. This story is part of a series of student group profiles.

For this profile, we talked with leaders of the Iowa State University Data Science Club.

Iowa State University Data Science Club logo

What are the goals of your group, and who is your core audience?
Our main goal is to promote the field of Data Science, whether it be information on the field, internship opportunities, school resources, or skills you need to learn to get a job in the field.

Our main audience is data science majors and any other adjacent majors with some prior coding experience. And anyone, in general, that would be interested in this type of career.

What kinds of activities have you done previously, and what do you have planned for this year?
We have focused a lot on company presentations and internship opportunities in the field. We have now been focusing on workshops surrounding data science essentials, like Google Cloud, Machine Learning, or Tableau basics.

What challenges have you faced in starting or maintaining your group?
One of the main challenges has been keeping people engaged. Workshops aren’t super fun but essential to learning about the field. Company presentations are nice but don’t appeal strongly to freshmen and sophomores. We have been working on making the club more of a community. Having members help each other with homework, talk about outside activities, have fun events occasionally that don’t relate to data science, but just make a place for collaboration and talk to others about their love for the field.

What suggestions do you have for others who want to start a group on their campus, or expand their current group?
Start big, expect small. In the beginning, focus on appealing to as many as possible. Do as many things as you can to interest people. But always have a foundation for your goal as a group, stay centered, stay consistent. You may have a ton of people at the first meeting and very few at the next, but the key is to stay consistent and think big picture.

In terms of expansion, bring outside help, see if your school can help, collaborate with outside companies. Put yourself in a position where your group will not just be a fun place to hang out but a place that could benefit your resume and help bring you to experience for future internship opportunities.

Get involved

Are you a student group leader or advisor? We’d like to hear more about your group’s activities. Contact us if you’d like us to profile your organization or participate in our student groups webinar series. You can also join our new Slack community to continue the discussion and make new connections.

About the Midwest Big Data Innovation Hub

The Midwest Big Data Innovation Hub is an NSF-funded partnership of the University of Illinois at Urbana-Champaign, Iowa State University, Indiana University, the University of Michigan, the University of Minnesota, and the University of North Dakota, and is focused on developing collaborations in the 12-state Midwest region. Learn more about the national NSF Big Data Hubs community.

MBDH Learning Innovation Fellows Program Builds on Success with Second Cohort

The Midwest Big Data Innovation Hub and the Gala Sustainability Learning Initiative at the University of Michigan School for Environment and Sustainability continue to build on the success of last year’s Learning Innovation Fellows pilot program with a second cohort of fellows. The student fellows, hailing from a range of midwestern institutions, work with faculty advisors at the intersections of the Midwest Hub’s “Cyberinfrastructure and Data Sharing” and “Data Science Education and Workforce Development” themes. The program brings together data science and sustainability, delivering open-access, data-enriched learning tools on the Gala platform, along with experiences and mentoring for student fellows.

Teams

Alternative Transportation Scenarios
Shanshan (Shirley) Liu

Shanshan (Shirley) Liu (Student Fellow) is a PhD student from the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign. Her research interests include transportation electrification policy and planning, sustainable transportation systems, and transportation energy. Shirley’s project is based around Shelie Miller’s case study, Assembling Our Transportation Future, which asks readers to think about transportation policy hinge points in American history. She is using Python to create tools that allow students to analyze scenarios of alternative vehicle adoption and evaluate them from the perspective of energy consumption and carbon emissions.

Shelie Miller

Shelie Miller (Faculty Advisor) is a professor at the University of Michigan School for Environment and Sustainability. Her research uses life-cycle assessment and scenario modeling to identify environmental problems before they occur. Miller’s research group works on a variety of energy-related topics, including the energy-water nexus, bioenergy, refrigeration in the food system, and autonomous vehicles.





Modeling Rainforest Carbon Cycling
Anneke van Oosterom

Anneke van Oosterom (Student Fellow) is a sophomore double majoring in biology and data science at St. Catherine University. She is currently involved with the biology department at St. Kate’s through the Biology Club and as a microbiology lab prep assistant. Through the fellowship she is creating a systems model using the Insight Maker modeling tool to demonstrate carbon cycling in tropical rainforests for Ann Russell’s forthcoming case Healing the Scars: Tropical Rainforest Carbon Cycling (developed through the OCELOTS network for tropical ecology).

Ann Russell

Ann Russell (Faculty Advisor) is a terrestrial ecosystems ecologist at Iowa State University, with special expertise in the biogeochemistry of tropical and managed ecosystems. Her research addresses links between traits of plant species and ecosystem processes, focusing on species and management effects on belowground processes, and subsequent implications for human impacts on soil fertility and carbon sequestration. Her research is designed to enhance our understanding of human impacts on the biosphere, improve biogeochemical models, and help guide selection of species for sustainable management of agroecosystems.


Scenario Planning for the Rouge River
Julie Arbit

Julie Arbit (Student Fellow) is in her final semester as an environmental policy and planning student within the School for Environment and Sustainability at the University of Michigan (UM). She works as a research associate for the Center for Social Solutions at UM, where her main project focuses on equity in flood risk, response, and recovery. Julie is using ArcGis Online and Python to create scenario planning tools for the case The Rouge River: Redlining, Riverbanks, and Restoration in Metro Detroit.


Perrin Selcer

Perrin Selcer (Faculty Advisor) is an associate professor and director of undergraduate studies at the University of Michigan Department of History. He works at the intersection of environmental history, history of science, and international relations.







Accessible Data Science Tools for Water Utilities
Thien Nguyen

Thien Nguyen (Student Fellow) is a second-year computer science undergraduate and sustainability enthusiast at the University of Minnesota, Twin Cities (UMN). He has previously worked with UMN’s Institute on the Environment, writing geospatial analysis algorithms in Google Earth Engine to observe soil degradation in Senegal’s Peanut Basin. Thien is working with PhD student Matt Vedrin to create tools for a PIT-UN funded collaboration working to help classrooms, communities, and workforces confront challenges in the monitoring and improvement of drinking water distribution systems.

Lutgarde Raskin

Lutgarde Raskin (Faculty Advisor) is a professor at the University of Michigan School for Civil & Environmental Engineering. She works to rethink engineered systems to better harness the power of microorganisms to treat water and recover resources from waste streams. Dr. Raskin and her team work to understand and improve various aspects of the engineered water cycle microbiome to improve human health using sustainable design approaches, with a focus on biofiltration, disinfection, distribution, and building plumbing biostability.



Get involved

This work was supported by the National Science Foundation through the MBDH Community Development and Engagement (CDE) Program.

Contact the Midwest Big Data Innovation Hub if you’re aware of other people or projects we should profile here, or to participate in any of our community-led Priority Areas. The MBDH has a variety of ways to get involved with our community and activities.

The Midwest Big Data Innovation Hub is an NSF-funded partnership of the University of Illinois at Urbana-Champaign, Iowa State University, Indiana University, the University of Michigan, the University of Minnesota, and the University of North Dakota, and is focused on developing collaborations in the 12-state Midwest region. Learn more about the national NSF Big Data Hubs community.

New MBDH Community Development and Engagement partners

By Qining Wang

The Midwest Big Data Innovation Hub (MBDH) recently partnered with multiple institutions in the region for new data science activities under its Community Development and Engagement Program. This program incubates new projects and provides support to help them grow.

In the last proposal cycle, the MBDH Seed Fund Steering Committee selected three projects to support, led by the Tribal Nations Research Group (TNRG), St. Catherine University, and Trinity Christian College.

TNRG Digital Agriculture Meeting

The TNRG, together with the University of North Dakota and Grand Farm/Emerging Prairie, will host a one-day workshop in 2022, at the Microsoft Business Center in Fargo, North Dakota. This workshop will connect tribal colleges and universities working with their local tribal governments to extend digital agriculture and educational opportunities to Native farmers.

Approximately 30% of the nation’s Native population and 20 of the 37 of the nation’s tribal colleges and universities are located in the MBDH service area. Because of this, the MBDH is well-positioned to engage tribal stakeholders on issues related to Data Science Education and Workforce Development. This is especially true in the context of Digital Agriculture, where many of these institutions are working with their local tribal governments to extend agricultural programs and educational opportunities to Native farmers.

Tribal communities have not had the dedicated capital for building a resilient and sustainable infrastructure for harnessing food on their lands for a long time. The lack of such infrastructure creates food insecurity that can be detrimental to Indigenous peoples. In addition, due to climate change, it is crucial to build sustainable farming practices that can provide sufficient food and preserve the ecosystem everywhere in the long run.

One way to realize optimal farming practices is to incorporate digital agriculture, which integrates digital technologies into crops and livestock management. Technologies such as machine learning and big data analysis tools can improve agricultural production while minimizing the harm to the ecosystem. For instance, by correlating multiple parameters related to crop growth using machine learning, farmers can better predict crop yield based on other parameters such as nutrients in the soil, weather, and fertilization. Those technologies can therefore make information on ecosystems, crops, and animals more findable and interpretable to farmers.

However, implementing digital agriculture on tribal lands involves extra layers of nuance. Data scientists and agricultural experts must conduct digital agriculture research in tribal regions under proper data sovereignty standards, such as the CARE Principles for Indigenous Data Governance. Indigenous peoples are entitled to know what data is collected and how data scientists use and analyze their data. The data should enable Indigenous peoples to derive benefit from any fruits of the research involving tribal communities.

This workshop will serve to increase the accessibility of digital agriculture in Native communities, emphasizing respecting the culture, traditions, and sovereignty of the Native people. In addition, this workshop will enlist more tribal stakeholders nationwide for broader engagement in digital agriculture, potentially developing a Data Science Workforce Development and Education proposal for Native communities. Anita Frederick, the President of TNRG, will lead this workshop and present the importance of Data Management and Data Sovereignty.

“Outreach to Indian tribes is often difficult for non-tribal entities and individuals,” Frederick said. “As a direct result, tribal populations are often left out of initiatives that could help to address some of the economic, health, and other societal conditions that tribes face. Clearly, American Indian citizens must have access to the opportunities envisioned in the Big Data Revolution. The proposed project is a first step in helping to close the growing Big Data gap that is emerging between Indian country and the rest of the nation.”

St. Catherine Data Science Boot Camp

MBDH will also support a data science program “created by women for women” at St. Catherine University (aka St. Kate’s), one of the USA’s largest private women’s universities, located in St. Paul, Minnesota. This program aims to cultivate a new generation of women and historically underrepresented data scientists. In addition to teaching data science and data analytic principles, this program will also raise students’ awareness of using data science in ethically, socially, and environmentally just ways.

Introduced in the fall semester of 2018, the data science program at St. Kate’s reaches both current and prospective students of the University. Monica Brown, the Mary T. Hill Director of Data Science at St. Kate’s, will lead the program’s two initiatives in 2021-2022. Working alongside her colleagues at St. Kate’s for over 13 years, Brown aspires to make data science and data analytics principles accessible to every student in the St. Kate’s community.

Brown will launch a one-week Data Science Boot Camp in the summer of 2022. This boot camp will provide hands-on coding experience to middle- and high-school students, particularly those historically excluded from data science. In addition, Brown will invite data science professionals to speak about future career opportunities. Overall, this program aims to enable younger students to envision themselves as future data scientists and to elicit their passion for coding and data science. The lessons learned organizing this event will be shared with others who wish to do so with their own student populations.

“St. Kate’s is grateful for the partnership with MBDH towards the support of a boot camp,” said Brown. “We very much look forward to bringing younger students onto our campus to encourage and empower them through data science activities.”

Trinity Data Science for Social Good Workshop

The third project to be incubated under the MBDH’s Community Development and Engagement program will be an annual workshop and conference on Teaching with Data for Social Good (DSG) in summer 2022. DSG addresses the importance of teaching data science for positive social impact, and this conference serves as an opportunity that encourages teaching faculty to include DSG in their curricula proactively.

Trinity Christian College, a faith-based institution located on the outskirts of Chicago, will host this meeting. The workshop chair will be Dr. Karl Schmitt, an assistant professor in the Data Analytics department at Trinity and the coordinator of the Data Analytics program.

The meeting format resembles that of regional professional society meetings, consisting of a workshop, keynotes, and contributed talks. To provide more practical assistance to teaching faculty incorporating DSG, faculty will directly generate teaching materials that include DSG in the primary workshop sessions. Additionally, faculty will also have a chance to practice teaching DSG by actively advising student teams participating in a colocated datathon. In this student competition, student teams will use data science to solve practical problems.

“An important component of increasing persistence and success for our current generation of students is connecting their coursework to meaningful change or outcomes,” Schmitt said. “Through the Workshop on Data for Good in Education, the MBDH will be supporting faculty in developing their teaching to better incorporate the Data for Social Good (DSG) movement. This provides a natural connection to relevance with grass-roots level improvements in our society while promoting the broad applicability of data science.”

Beyond these outcomes, Schmitt said, “the workshop will be a professional development opportunity for all instructors seeking to more deeply engage their students through meaningful social good projects within a classroom setting. It will inspire, educate, and most importantly, allow faculty the chance to share, and prepare, materials for use within their own teaching context.”

Get involved

Learn more about other Community Development and Engagement partnerships, and contact the MBDH if you have an idea for a project to help build the data science community in the Midwest.

Contact the Midwest Big Data Innovation Hub if you’re aware of other people or projects we should profile here, or to participate in any of our community-led Priority Areas. The MBDH has a variety of ways to get involved with our community and activities.

The Midwest Big Data Innovation Hub is an NSF-funded partnership of the University of Illinois at Urbana-Champaign, Iowa State University, Indiana University, the University of Michigan, the University of Minnesota, and the University of North Dakota, and is focused on developing collaborations in the 12-state Midwest region. Learn more about the national NSF Big Data Hubs community.

Teaching During a Pandemic

By Raleigh Butler

This story is part of a series on coronavirus research in the Midwest region. To explore other NSF-funded research addressing the COVID-19 pandemic, please visit the COVID Information Commons, a project of the four NSF Big Data Innovation Hubs.

In early 2020, the USA was brought to a standstill when stores, schools, and other everyday locations closed to help fight the COVID-19 pandemic. This piece will discuss a variety of RAPID awards funded by the National Science Foundation in the Midwest to support research to mitigate a variety of education-related pandemic challenges. Links to the NSF award abstracts will be linked in the article.

Remote Learning Research

A common topic for early school-related COVID-19 research was the exploration of remote or online styles of teaching and learning. In the early stages of the pandemic, it was unclear when schools would be able to reopen. Teachers and administrators were attempting to learn from prior work on remote learning at all levels, kindergarten through college.

For instance, in a project led by researchers from the Chicago Board of Education, researchers discuss the impact of public school students studying computer science remotely. Issues broached include access to the appropriate technology outside of school and possible socioeconomic variables at play. An award to the University of Kansas touches on a similar topic at the graduate level. This work is STEM-focused, addressing COVID-19-related challenges in engineering. One question the researchers ask is: “To what extent do the relationships between perceived e-mentoring support and student outcomes vary by demographics, disciplines, and institutional characteristics?”

A project at the University of Nebraska–Lincoln explores this topic from the faculty perspective. The project goal is described as “to identify cognitive and emotional themes concerning faculty and staff adaptability and community engagement during a crisis compared to those found under typical teaching circumstances.“ Adaptability is a key theme here—professors, regardless of their experience using online-teaching technology, were expected to learn how to do so. In Illinois, the Chicago Public Schools did its own research on forced remote learning and “mitigating the impact” of the sudden transition from in-person to online learning.

Remote Learning Activities

Researchers at the University of Minnesota–Twin Cities put an interesting spin on remote learning with their work on virtual reality (VR). Virtual reality allows learners to immerse themselves in a different world using a pair of electronic goggles or a headset. This device is used to portray a different world, like being “inside” a video game. The Minnesota researchers recognize that “many people, especially young adults, typically being used to active social life, can find this physical/social distancing leading to social isolation. Unfortunately, social isolation is strongly associated with negative outcomes for mental health and therefore represents a serious threat to long-term compliance.” The project aims to promote web-based VR as a way for people to interact safely in a shared environment, despite not actually being physically together.

The need for activities—especially for young learners—is addressed in Indiana University Bloomington’s research project. They have launched a Facebook group called CoBuild19, which works on making STEM activities more accessible to children.

Remote learning has also been addressed in an award granted to the Georgia Research Alliance. This award focuses on “ALOE”—Adult Learning and Online Education. Given the current state of the pandemic, it is immensely difficult, if not impossible, to continue education safely in person. The National AI Institute for Adult Learning and Online Education (AI-ALOE) addresses this by working to move adult-education opportunities online.

Mis/Disinformation

There’s a lot of information available about COVID-19. Some is real, some is misinformation (simply incorrect), and some is disinformation (incorrect with the intent to deceive). Many people have trouble deciding what sources to trust. A project led from the University of Michigan–Ann Arbor plans to follow a sample of university students. The premise of the research is to determine “whether and to what extent people follow recommendations and change behavior.”

Teaching About the Pandemic

Teaching about the pandemic itself is important. A relevant award is an exploration by researchers at the University of Nebraska–Lincoln into using popular media to educate youth on COVID-19-related issues. By using illustrated media such as comics to raise youth awareness of accurate coronavirus-related information, perhaps it’s possible to lessen the mis/disinformation discussed in the University of Michigan’s work. The project proposes assembling “an integrated package of high-quality, widely accessible media and other outreach materials designed to engage middle school youth, educators, and libraries in learning about viruses in relation to COVID-19.”

The researchers, wanting the material to be accessible, propose that “[t]hese resources will be disseminated broadly and at no cost to youth and educators of all kinds, including schools, libraries, museums, and other established networks for formal and informal science education.” Indeed, children’s lives are being altered drastically by COVID-19. It’s important that they know what is occurring to cause such alterations. In fact, the University of Missouri and the University of North Carolina at Chapel Hill collaborated to develop a curriculum for high school students. The curriculum covers epidemics in both scientific and social contexts.

Get involved

The projects described above were all funded by the NSF, which published a related story.

Contact the Midwest Big Data Innovation Hub if you’re aware of other people or projects we should profile here, or to participate in any of our community-led Priority Areas. The MBDH has a variety of ways to get involved with our community and activities.

The Midwest Big Data Innovation Hub is an NSF-funded partnership of the University of Illinois at Urbana-Champaign, Iowa State University, Indiana University, the University of Michigan, the University of Minnesota, and the University of North Dakota, and is focused on developing collaborations in the 12-state Midwest region. Learn more about the national NSF Big Data Hubs community.

MBDH Learning Innovation Fellows program – first cohort projects

The Midwest Big Data Innovation Hub Learning Innovation Fellows Program, housed at the University of Michigan School for Environment and Sustainability, enables teams to form for work toward better understanding of the intersections of the Hub’s “Cyberinfrastructure and Data Sharing” and “Data Science Education and Workforce Development” themes.

Our fellows work with faculty and teaching staff to create innovative interactive data analysis activities that can nest within sustainability science case studies. They design, prototype, and pilot these features in classrooms within the MBDH network. The program leverages talent and resources from two existing, open-source science learning environments. Gala (www.learngala.com) is a community-based, responsively designed sustainability science learning environment. Quantitative Undergraduate Biology Education and Synthesis (QUBESHub, or Qu) is a virtual center for faculty development and open educational resource sharing (https://qubeshub.org) that has had long-term support from NSF, formalizing and professionalizing open educational resources.

Through a series of virtual “Networkshops,” we connect undergraduate data science majors, graduate/professional students, faculty, and professionals. We can thus be inclusive, incorporating into classrooms problem-driven, data-rich material that speaks to lived infrastructural and environmental challenges from a range of communities across our region, and beyond. The team includes the following:

Leadership—

Rebecca Hardin (PI) is an anthropologist and Associate Professor at the University of Michigan School for Environment and Sustainability (UMSEAS), where she leads collaborations on the open-source, open-access learning platform Gala (www.learngala.com) and research group on Digital Justice. Rebecca also coordinates the Environmental Justice Field of Specialization and related Certificate program at UMSEAS.



Ann E. Russell (Co-PI) is an ecosystems ecologist, with special expertise in the biogeochemistry of tropical ecosystems. She is an Associate Adjunct Professor in the Department of Natural Resource Ecology and Management at Iowa State University, and PI of the NSF Research Collaborative network ALIVE: Authentic Learning in Virtual Environments.





M. Drew Lamar (Co-PI) is a mathematician and Associate Professor of Biology at William & Mary. His teaching and research are highly interdisciplinary in nature, using techniques and concepts from mathematics, statistics, biology, and computational sciences. Drew is Co-PI and Director of Cyberinfrastructure for the Quantitative Undergraduate Biology Education and Synthesis (QUBES) virtual center, with an interest and passion in open-source software development, quantitative biology education, and development of education gateways.

Ed Waisanen (Program Manager) is Program and Platform Lead for Gala (learngala.com). He has a master’s degree in Natural Resources and Environment from the University of Michigan, with a focus in Environmental Informatics and a background in multimedia production. Ed is focused on developing tools and communities that emphasize curation, open exchange, and narrative approaches to deepen learning.





Teams—

Data Learning for Restoration Ecology

Kyra Hull (Fellow) is a native of Grand Rapids, Michigan, and a first-year graduate student at Grand Valley State University, studying Biostatistics. Kyra is working on the following case about forest restoration, which is bilingual (Spanish and English versions): https://www.learngala.com/cases/a3224235-cdc0-44fc-a98b-46735dfef6c9




Karen Holl (Faculty Advisor) is a Professor of Environmental Studies at the University of California, Santa Cruz. Her research focuses on understanding how local and landscape-scale processes affect ecosystem recovery from human disturbance and using this information to restore damaged ecosystems. She advises numerous public and private agencies on land management and restoration; recently, she has been working to improve outcomes of the effort of the many large-scale tree-growing campaigns.




Data Learning to Address Groundwater Contamination

Saba Ibraheem (Fellow) is a second-year Health Informatics student at the University of Michigan, focusing on data analytics and research in health care. Saba is working on the following case, which is bilingual (English and French versions): https://www.learngala.com/cases/dioxane-plume





Rita Loch-Caruso (Faculty Advisor) is a toxicologist in the Department of Environmental Health Sciences at the University of Michigan School of Public Health, with a research focus in female reproductive toxicology and, in particular, mechanisms of toxicity related to adverse pregnancy outcomes such as premature birth.





Alan Burton (Faculty Advisor) is a Professor at the School for Environment and Sustainability and the Department of Earth and Environmental Sciences at the University of Michigan. His research focuses on sediment and stormwater contaminants and understanding contaminant bioavailability processes, effects, and ecological risk at multiple trophic levels. He is also a specialist in ranking stressor importance in human-dominated watersheds and coastal areas.





Data Learning in Livestock Ecologies

Daniel Iddrisu (Fellow) is a second-year student in Masters in International and Regional Studies, with a specialization in Africa, at the University of Michigan. He earned a BA degree in Integrated Community Development from the University for Development Studies, Tamale, Ghana. His research focuses on health, development, gender, and environmental health. The case he is working on takes place on the Greek Island of Naxos, but comprises skills for modeling and analyzing human/livestock interactions more broadly: https://www.learngala.com/cases/livestock-grazing

Johannes Foufopoulos (Faculty Advisor) is an Associate Professor at University of Michigan’s School for Environment and Sustainability, who focuses his lab research on fundamental conservation biology questions and on issues related to the ecology and evolution of infectious diseases. Major research projects examine how habitat fragmentation, invasive organisms, and global climate change result in species extinction.





Data Learning on Safari

Rahul Agrawal Bejarano (Fellow) has a background in computer science and he is currently working on a master’s degree at the University of Michigan School of Environment and Sustainability, with a concentration in Sustainable Systems. Rahul uses data from a diverse range of sources to shed light on today’s environmental challenges and develop innovative solutions, and is working on identifying climate-related vulnerabilities to our supply chains. He is working on this case, about the interactions of various wildlife species in the Serengeti: https://www.learngala.com/magic_link?key=oOTYOXyDRpmY_yM4AFlnXQ


Charles Willis (Faculty Advisor) is a Teaching Assistant Professor, Biology Teaching and Learning at the University of Minnesota. He is currently interested in the research and development of pedagogy practices for non-major biology students. In particular, he is focused on studying student-student and instructor-student feedback in online spaces. His research is also concerned with understanding how changing environments shape plant diversity on both evolutionary and ecological time scales. Currently, he is focused on using historical specimen data to study how historic climate change (over the past century) has impacted plant phenology and diversity across North America.

Jeffrey A. Klemens (Faculty Advisor) is an Assistant Professor of Biology at Thomas Jefferson University, where he serves as program director for the undergraduate biology curriculum. His current research activities are focused on the use of agent-based models to describe habitat use by organisms in the urban environment and the role of active learning in science education, particularly the use of systems thinking and other modeling techniques to improve student understanding of complex phenomena.




Data Learning in Detroit’s Eastern Market

Ghalia Ezzedine (Fellow) is a second-year master’s student studying Health Informatics. She is interested in leveraging data and digital tools to improve population health. In her free time, she likes to try new recipes, work out, and occasionally jump off a bridge or airplane. She chose this case study because of her interest in nutrition, and the shift in foods available at this iconic marketplace: https://www.learngala.com/cases/2b92db37-de87-4321-a531-510dea225189



Josh Newell (Faculty Advisor) is an Associate Professor in the School for Environment and Sustainability at the University of Michigan. He is a broadly trained human-environment geographer, whose research focuses on questions related to urban sustainability, resource consumption, and environmental and social justice. His research approach is often multiscalar and integrative and, in addition to theory and method found in geography and urban planning, he draws upon principles and tools of industrial ecology and spatial analysis.


Midwest Big Data Hub co-leads local events for 4th Annual Global Women in Data Science Conference

The Midwest Big Data Hub co-led local participation in the 4th annual Global Women in Data Science (WiDS) Conference, with sponsorship from the National Center for Supercomputing Applications (NCSA) and the University of Illinois. The event was free and open to all. The WiDS Conference, hosted on March 4th at 150 locations around the world, seeks to unite and connect women working in data science fields.

“We were very excited to co-sponsor this with NCSA, and support this inaugural Illinois event for Stanford’s Global Women in Data Science Day,” said Melissa Cragin, Executive Director of the Midwest Big Data Hub. “Partnering with others on events such as the Illinois WiDS allows us to best use our human resources and experts network to broaden participation in data science and Big Data research and education. I was honored to participate and have the opportunity to moderate such a terrific panel of accomplished leaders, who shared their perspectives on data science, data-enabled research, and opportunities for women in this space.”

panel discussion
Faculty panel moderated by MBDH Executive Director Melissa Cragin

The WiDS local events, hosted this year at NCSA, featured a variety of speakers from diverse backgrounds presenting sessions on opportunities for women in data science, technical vision talks, and the variety of data science and technology careers available in the Midwest.

“I always enjoy telling my story about how I got started working big data research,” said Ruby Mendenhall, Illinois Professor of Sociology and African-American Studies and NCSA faculty affiliate. “My story also demonstrates the importance of doing outreach to groups that are not traditionally represented in data science such as African American Studies.”

As part of her 2017-2018 NCSA Faculty Fellowship, Mendenhall and NCSA research programmer Kiel Gilleade completed a pilot study called the Chicago Stress Study that examines how the exposure to nearby gun crimes impacted African American mothers living in Englewood, Chicago. Mendenhall and Gilleade developed a mobile health study which used wearable biosensors to document 12 women’s lived experiences for one month last fall. As part of their research, Mendenhall, Gilleade, and their team were able to create an exhibit based on the study data they collected in order to bring the unheard, day-to-day stories of these mothers to life.

panel discussion
Panel discussion moderated by iSchool Professor Catherine Blake

Professor Donna Cox, Director of NCSA’s Advanced Visualization Lab, was a panelist at this year’s local conference, and praised the insights of the other speakers while emphasizing the importance of the larger WiDS conference. “It was valuable to hear other panelists,” said Cox. “The future of Women in Data Science should include raising awareness about important issues emerging in data science, especially socially-relevant issues. We need more women actively involved in the ethics of data science.”

Alice Delage, Associate Project Manager for NCSA and Program Coordinator for the MBDH, said, “Hosting WiDS Urbana-Champaign at Illinois was an opportunity to highlight the campus expertise around data science led by women.” Delage, who co-chairs the local Women@NCSA group, said, “Data science and technologies are increasingly impacting our lives and society, and it is imperative that women and minorities be part of these transformations. We wanted to showcase the groundbreaking work being done in that area by Illinois female data scientists and to inspire more women and underrepresented communities to engage in the field.”

There are also opportunities to expand the event next year by better incorporating student work in the program, Delage said, or running a datathon, for example. Some of this year’s participants have already volunteered to help with next year’s event.

A full list of this year’s speakers at the WiDS Conference at NCSA is here. For more information about the global WiDS conference and ways to get involved, please visit https://www.widsconference.org.

The MBDH is one of four regional Big Data Innovation Hubs with support from the National Science Foundation (award # 1550320), and works to build capacity and skills in the use of data science methods and resources in the 12-state U.S. Midwest Census region. Learn more about the Hub at https://midwestbigdatahub.org.

Thanks to NCSA Public Affairs for contributing to an earlier draft of this post.

Guest post – Data Science Education at Two-Year Colleges

By Matt Fall

Executive Director, Center for Data Science, Lansing Community College

Recently, the American Statistical Association (ASA), with support from the National Science Foundation (NSF), hosted a two-day summit in Washington D.C. to discuss outcomes and curricula for data science programs at two-year colleges. The Two-Year College Data Science Summit (TYCDSS) was intended to help spur the growth of data science programs at these institutions and included representatives from two and four-year institutions, government, and industry.

Sallie Keller (Virginia Tech) plenary talk (photo: Nicholas Horton)

The summit included several plenary talks discussing the role of two-year colleges in addressing the need for data scientists as well as a brief presentation from a graduate of a community college data science program. The majority of the summit, however, was devoted to a series of working sessions where the participants discussed ideal outcomes and competencies for three categories of students:

  • Category 1: students intending to complete an Associate’s degree and begin working
  • Category 2: students intending to earn an Associate’s degree and transfer to a 4-year program
  • Category 3: students seeking a certificate

The working discussions provided an opportunity for the summit participants to discuss what was expected and feasible for a student from each category to complete. The discussions were captured by a designated writing group and there will be a forthcoming write-up summarizing the recommendations of the summit participants with guidelines for two-year college data science programs.

This summit was particularly timely for my colleagues at Lansing Community College (LCC) as we have recently begun development of a data science program. Prior to the summit, participants were provided access to a list of resources that included relevant research, reports from related workshops, and sample syllabi. Of particular interest to us, as we design the layout of our program, were the Park City Math Institute’s Curriculum Guidelines for Undergraduate Programs in Data Science (2016) [PDF], the Oceans of Data Profile of the Data Practitioner (2016), and the Oceans of Data workshop report on Building Global Interest in Data Literacy (2016). The resources provided, candid discussions with other two-year colleges regarding their programs, and the discussions about realistic competency expectations were also of interest and informative to our program design.

The intent of the TYCDSS directly supports the MBDH’s priority area of interest in data science, education and workforce development. Two-year colleges provide higher education accessibility to many students who could not or would not otherwise pursue an advanced degree. An increasing number of these schools are offering certificate and Associate’s degree programs in data science and analytics to support growing workforce demand. Growth in these types of programs should naturally lead to an increase in data competency, enrollment in university programs, and larger hiring pools for data science based careers.

Related information:

Midwest Big Data Summer School 2018

Midwest Big Data Summer School reveals how big data can advance research efforts

By Paula Van Brocklin, Office of the Vice President for Research, Iowa State University

Iowa State University logo
The Midwest Big Data Summer School, held May 14-17 at Iowa State University, helped nearly 140 academic and industry researchers, graduate students and post-docs from nine states broaden their understanding of big data and its ability to advance their research interests. Iowa State has organized and hosted the event since 2016.

 
“The summer school seeks to bridge the gap between scientists and engineers using data science technology by introducing them to data science techniques and vocabulary,” said Hridesh Rajan, lead organizer of the Midwest Big Data Summer School and professor of computer science at Iowa State. “The idea is to help these individuals better communicate and leverage their data-science needs.”

The curriculum

The school’s first three days introduced attendees to a range of big data topics, including data acquisition, data preprocessing, exploratory data analysis, descriptive data analysis, data analysis tools and techniques, visualization and communication, ethical issues in data science, reproducibility and repeatability, and understanding domain/context.

On the final day, participants selected one of four tracks, which focused on a sub-area of big data analysis. The tracks were:

  • Foundations of Data Science
  • Software Analytics
  • Digital Agriculture
  • Big Data Applications

Several individuals at Iowa State were instrumental in developing and organizing the tracks’ curricula. Click here for a list of those involved.

Speakers

Keynote presenters at this year’s summer school were:

  • Chid Apte, director, Mathematical Sciences and Blockchain Solutions, IBM Research
  • Tom Schenk, chief data officer, City of Chicago
  • Jacek Czerwonka, principal software engineer, Microsoft Research
  • Will Snipes, principal scientist, ABB Research

A complete list of speakers, including their bios, is available here.

Data science evolving quickly

The field of big data, also referred to as data science, is relatively new yet advancing quickly. For this reason, organizers encourage researchers and scientists to learn as much as they can through resources like the Midwest Big Data Summer School.

“Our aim is for early career researchers and professionals – both in academia and industry – to get a taste of what it’s about, what the state of the art is and how they can start thinking about using data science in their own domains,” said Chinmay Hegde, assistant professor of electrical and computer engineering at Iowa State and a co-organizer of the summer school.

Many thanks

Rajan recognizes the summer school would not be possible without the help of many.

“We are especially thankful for the Midwest Big Data Hub, the National Science Foundation, the Office of the Vice President for Research, Iowa State’s College of Liberal Arts and Sciences, and the departments of computer science and statistics for providing both funding and personnel support for this event.”

Next year
Plans are in the works for the 2019 Midwest Big Data Summer School, though no dates have been set. Rajan said more application-specific tracks may be added to next year’s curriculum. Watch the Midwest Big Data Summer School website for more details in the spring of 2019.

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Reposted from Iowa State University’s Research News blog. View the original post here.

MBDH partners on US Ignite Reverse Pitch challenge

part of Hub’s focus on Smart, Connected, and Resilient Communities

US Ignite Hackathon
UIUC collaborators and mentors meet with HackIllinois teams on US Ignite Challenge

The University of Illinois at Urbana-Champaign (UIUC) was awarded a $20,000 grant from US Ignite to host a Smart Gigabit Communities Reverse Pitch Challenge. The MBDH, along with other local partners (see below), contributed towards matching the grant, bringing to $40,000 the total resources available to support the development of smart gigabit applications for the benefit of the local community. Read More

New Report on “Keeping Data Science Broad”

A new report on the “Keeping Data Science Broad: Negotiating the Digital and Data Divide Among Higher Education Institutions” initiative was released by the South Big Data Hub and collaborators, including the Midwest Hub. This initiative brought together the BD Hubs community and other stakeholders to explore pathways for keeping data science education broadly inclusive. Read More