The DataUp Workshop – Instructor Training: Inspiring Professional Development & Capacity-Building

Faculty teams from the DataUp program during the Instructor Training Workshop on Nov 6 & 7, 2018.

Society is increasingly becoming more data-driven and data-literate. It is vital every institution has the capabilities and infrastructure to engage and develop learners prepared to interact and succeed in such a society. Numerous studies have identified the expanding data divide between institution types and the need to develop successful bridge initiatives. The South Hub begin to address this need by creating a 3-part program, DataUp. Through this program, the South Hub is directly impacting each participating institution’s data science education capacities.

The first component of the program is a hosted 2-day data or software workshop presented by the Carpentries. This provided an opportunity for each participating institution to engage in a workshop that specifically addressed their data knowledge gaps (for more information on these workshops, Click Here). Exposing students to these intensive workshops, students are able to gain hands-on training and exposure to principles and tools, such as shell and JupyterHub. Removing the associated ‘fear factors’ empowers learners to employ and address challenges with data. The second component of the DataUp program is a 2-day pedagogy intensive instructor training.

On Nov 6 & 7, 2018, The DataUp program welcomed participating institutions for the instructional training. During this instructional training workshop, faculty teams engaged in a pedagogy intensive to learn best practices concepts for data science education.  Many instructors noted the timeliness of this training for not only their students but for faculty overall professional development. One faculty member noted the ‘workshop is great to teach techniques [necessary] to teach concepts like these [at my home institution]. In [a] purely doctoral program, they don’t teach pedagogy’. The workshop did not include analytical software training but discussed mindset cultivation, participatory live coding benefits, managing diverse classrooms, and more. The workshop also included multiple participatory experiences for faculty teams to practice and commit to memory techniques and best practices needed to actively engage learners and complex concepts.

Here are a few points from the workshop:

    • Most students/learners approach computational and analytical concepts with a fixed mindset.  Typically these mindsets are negative.  It’s not that they can’t learn the skills, they simply start thinking they are unable to learn the concepts and their actions begin to follow their mindset
    • Participatory live coding is great for demonstrating, reinforcing, and engaging all learning styles.  Don’t be afraid to make mistakes. Through imperfection, learners can watch and learn proper troubleshooting techniques.
    • Patience is key.  Don’t expect students to learn and understand at your pace.  Move at the speed of the class. The goal is competence, not speed.

The workshop also provided faculty members the opportunity to learn use case scenarios involving the interactive notebook, JupyterHub.   JupyterHub is an open web-application that allows for creating and sharing live code, equations, visualizations, narrative text.  Faculty from numerous institutions noted a challenge to teaching data and analytical concepts is the lack of institutional infrastructure to support these initiatives.  One faculty member stated, ‘we are a small institution and don’t have the large IT [department] to help set up [or troubleshoot]’.  Utilizing JupyterHub helps to alleviate this issue.  Typically, to teach a lesson, instructors would need to ensure each student has the correct software versions and updates installed on their computers.  If an issue arises, this takes valuable instructor time away from actually teaching the lessons, to troubleshoot any challenges.  Students may become less engaged in the lesson or believe the concepts and lesson to be cumbersome.  Either way, this does not encourage students or faculty to utilize analytical software.

For institutions with limited or no infrastructure, JupyterHub provides a great alternative that alleviates the challenges of setup, increases classroom instruction time, and enhances participatory learning.

The third programmatic component requires participants to utilize the pedagogical best practices, learned during the instructor training, to teach either a bootcamp, workshop, seminar, or 2-day training to their institution in 2019.  Check back for information for their 2019 self-directed workshops.

To view photos from the event, click here.

For more information regarding the on-campus workshops, please click here.  

Strategies for hiring and maintaining a diverse data scientists workforce

RTI’s Kristina Brunelle (left) moderates a panel discussion with Amy Roussel, RTI (center); Gracie Johnson-Lopez, Diversity and HR Solutions (right); and Sackeena Gordon-Jones, Transformation Edge and NC State University (on screen).

Data science is hot. That’s good news for workers with data science skills. It also means organizations competing to hire data scientists need to understand how to recruit talent that will solve their data science challenges and contribute to creating a productive and diverse workforce.  Continue reading

Data Education–Inclusivity is the Word

 

As organizational and societal decisions become more data-driven academic institutions, industry, and government officials continuously identify data literacy as an important skillset for individuals currently in and entering the workforce.  Unfortunately, a dearth of qualified data literate employees exists producing a need for effective data science education and training for undergraduates.  Continue reading

Data Science and Emerging Economies: Students Attend #YCBS2018

 

Top Left: The South Hub SNAP award recipients with the Co-Executive Director, Renata-Rawlings Goss and an organizer of the Young CEOs Business Summit. Top Right: South Hub SNAP award recipients, Abdoulaye Gueye, Favour Ori, and Sylvester Ogbonda, pose with their awards during the Young CEOs Business Summit Awards Banquet. (R) The South Hub SNAP award recipients with the Co-Executive Director, Renata-Rawlings Goss, organizers of the Young CEOs Business Summit, and presenters.

The South Hub continually identifies opportunities to expose students and professionals to data science.  For example, the South Hub awarded five student’s registration fellowships through the SNAP-DS program, “Stimulating New Activities and Projects in Data Science,” to attend the Young CEOs Business Summit’s (YCBS) 2018 Annual Summit in Atlanta.  The South Hub developed the SNAP-DS program to provide travel support, student stipends, or registration fellowships for students to attend data-related workshops, conferences, and projects, such as the Young CEO’s Business Summit, that expose students to data science and the ways data science can better societies and businesses.   Continue reading

Workshop looks at forming EU-US big data partnerships

Participants in the international big data workshop in Versailles, France, take a break for a group photo.

In November 2017, the National Science Foundation’s Big Data Innovation Hubs sponsored a workshop in Versailles, France to discuss the formation of public-private partnerships in big data research among institutions in the United States and the European Union. Organized in conjunction with the Big Data Value Association, the PICASSO Project, and Inria, the workshop was the first of its kind to bring together international big data experts representing government, industry, and academia. Continue reading

South Hub, Microsoft team up to provide Azure credits for researchers

Earlier this year, the South Big Data Hub partnered with Microsoft Research to offer researchers in the South Hub region the opportunity to apply for cloud credits on Azure, the comprehensive cloud services platform offered through Microsoft. The opportunity was designed to provide cloud computing resources to support data-intensive research projects.

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Confronting the data challenges of ‘smart health’

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NSF’s Wendy Nilsen speaking at a South Big Data Hub Roundtable.

Each day countless devices—from monitors in hospitals to diagnostic tests to Fitbits—capture huge amounts of health data. That data could change how patients and doctors interact, how diseases are diagnosed and treated, and the amount of control individuals have over their health outcomes.

But there’s a catch, says Wendy Nilsen, PhD, program director of the Smart and Connected Health Initiative at the National Science Foundation.

The data is plentiful, Nilsen acknowledged. The challenge, she said, is how to make that data easier to use, how to standardize it so it can be analyzed, how to scale it, keep it safe, and how to account for external factors such as the environment or a person’s genome.

Nilsen discussed these challenges and how to address them in a roundtable discussion hosted by the South Big Data Hub on October 14. Nilsen’s talk, titled “Smart Health and Our Future” provides an overview of the challenges that must be addressed as well as the ultimate goal: A system where patients use data to take more control of their health and where healthcare practitioners can use data from multiple sources to improve diagnoses and health outcomes.

To view the presentation slides, click here.

Data science education in traditional contexts: Reflections on a recent webinar

On August 28, Karl Schmitt, PhD, an assistant professor in the department of mathematics and statistics at Valparaiso University, attended the webinar Data Science Education in Traditional Contexts, hosted by the South Big Data Innovation Hub as part of its Keeping Data Science Broad: Bridging the Data Divide series. The webinar featured five speakers, including Schmitt, who is also the director of data sciences at Valparaiso. Each speaker talked about their own programs and experiences in data science education as well as some of the challenges involved in creating and implementing educational programs in a field that is still very new and in the process of being defined. Continue reading

Big data and public health: New scenes and a new state of mind

Bigdatahealthcare-3By Eun Kyong Shin

The 2017 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS 2017) was held in Washington, DC, in July, and prominent fields applying social computing techniques include public health and healthcare. In early modern epidemiology, data collection processes relied heavily on painstaking manual labor. Data on a large scale was hard to obtain and resulted from careful observation and intensive recording. Since the introduction of the internet and advances in digital communication, massive amounts of dynamic data have accumulated exponentially. Along with the digitization of medical practices and other social data collection process, the nature of scientific discovery has been fundamentally changed. Continue reading

Visuals, storytelling help make sense of data

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Panelists discuss data visualization at a recent workshop sponsored by the South and West Big Data Hubs.

By Mark Schroeder

Throughout human history, stories have helped us make sense of sequences of events in our lives, infer cause and effect relationships, and share them with others. Just as our own memories are fallible and retelling stories can shape how we remember events, data can be fallible too. Its value is shaped by the process used to collect it and can be incomplete, incorrect, or biased in some fashion. How can we use data to gain true insights about the world and share them despite these challenges?

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