Data Science Education and Workforce

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DATA SCIENCE EDUCATION & WORKFORCE OVERVIEW

The ability to utilize and understand data is an increasingly critical skill for the evolving 21st century workforce, as espoused in studies and reports by National Academies and Federal Agencies. Because data literacy at multiple levels is now needed in almost every technical and business sector, there is a severe shortage in skilled workforce to meet current and emerging demands. To combat this shortfall, an all hands on deck approach is needed. K-12 systems, colleges and universities, including community colleges, underrepresented groups, and women must be engaged in data science training for the modern workplace.

 

IMPACT

  • $280,000+ in funding for students to gain real-world data-related career experience
  • Data science training workshops at 7 minority-led, -serving, primarily teaching institutions, community colleges and four-year liberal arts colleges
  • 100+ direct and indirect learners who benefited from workshops/bootcamps, modules, and courses taught by DataUp trained faculty members

 

VIEW ABOUT OUR WORK IN DATA SCIENCE EDUCATION AND WORKFORCE DEVELOPMENT

The project is a collaborative effort among the University of Tennessee Chattanooga, Tuskegee University, Spelman College, and West Virginia University to integrate and automate biological big data into student training and education. The project will offer training workshops, engage faculty and students in developing a protocol to automate field data collection, and will prototype automated methods to enhance plant digitization.
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In the week of July 29-Aug 2, 2019, more than 50 faculty and students from more 21 institutions participated in two R bootcamps at the University of Tennessee at Chattanooga (UTC). The iCompBio REU is supported by NSF Award 1852042, REU Site: ICompBio – Engaging Undergraduates in Interdisciplinary Computing for Biological Research. The first bootcamp on data wrangling using R was taught by Hong Qin, a computational biologist at UTC. Materials for this R Data Wrangling bootcamp is available at a public GitHub repository https://tinyurl.com/UTC-R-camps2019. The second bootcamp, Electronic Health Records, was taught by Elvena Fong and Zhuqi Miao from the Center for Health Systems Innovation at the Oklahoma State University.
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The South Big Data Hub’s Program to Empower Partnerships with Industry (PEPI) pairs early-career faculty and researchers throughout the South with Industry Partners and support their travel to make collaboration possible.
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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.  The National Academies of Sciences (NAS) formed a study committee to consider the core principles and skills undergraduates should learn and the pedagogical issues that must be addressed to build effective data science education programs.
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Hub Group
Keeping Data Science Broad in-person workshop explored the Data Divide by convening stakeholders from teaching institutions, community colleges, tribal colleges, and minority-serving institutions to discuss challenges related to capacity building and capability. Specific issues discussed included access to data, critical thinking, designing curriculum and assessment, data literacy, diversity, ethics, resources and staffing, building collaborations, and the pipeline to higher education from K-12. Recent education-enabling projects were showcased at the event.
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Workshop part of its Keeping Data Science Broad: Bridging the Data Divide series. Each webinar highlighted 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.
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Earning a college degree takes more than time and effort—it requires a significant financial investment. According to a report from Thrivent Mutual Funds highlighted in a recent edition of Forbes magazine, choosing a major that can translate into a data science career is one way to ensure that your career earning power will allow you to pay off those student loans quickly. Is an understanding of data, how to use it, manage it, and act on it, the newest foundational skill essential for career success in the 21st century? Probably so. It can also help you with very practical concerns, for example interpreting the automatic diagnostics that are done daily on your new car so you can figure out a better route to work and improve your gas mileage. How to afford that fancy data-driven car? A data science education that opens up many well-paying career opportunities is a good place to start. View post for the full report.
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The Center of excellence in Research and Education for big military Data InTelligence otherwise known as the C.R.E.D.I.T Center is Prairie View A&M University’s premiere graduate level program for the processing and effective sorting of complex data. Funded by the Department of Defense, the C.R.E.D.I.T Center is one of three centers funded by the DoD at Historically Black Colleges and Universities. It is a one-stop-shop for engaging students in Big Data education, analytics and solving complex real-time problems for the military. Dr. Renata Rawlings-Goss the Executive Director of the South Hub gave a keynote speech at the C.R.E.D.I.T Center’s First Workshop of Mission-Critical Big Data Analytics (MCBDA 2016) held at Prairie View A&M University (Prairie View, TX). Click to learn more.
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DataStart participant fellowship recap of experience at Black Oak Analytics, a Little Rock data startup. Fellowship mainly focused on data integration of unstructured entity references. The primary goal of my work was to develop and test a more general approach to the problem of resolving entity references in free text format.
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The Data Science for Social Good- Atlanta (DSSG-ATL) program brought together nine graduate students from various southern states to participate in a ten-week paid internship experience. The program has engaged student interns with diverse academic backgrounds including computer science, statistics, digital media, public policy, civil engineering, industrial engineering and urban planning, as well as diverse demographic backgrounds including underrepresented groups. View the post to learn more about the program and the student projects.
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