Big Data Summer School - accepting applications

**Call for Participation**

We are currently accepting applications for the second annual Big Data Summer School to be held June 28 - July 2, 2021.  Due to the on-going COVID-19 pandemic, the Summer School will be held virtually.  The aim of the summer school is to provide undergraduate and early graduate students with the foundational skills necessary to work with the types of large-scale datasets that are common in 21st century scientific research. 

We anticipate two tracks within this five-day immersive learning event:

  • Track 1 welcomes students at or near the beginning of their computer science education. Students will learn and practice facility with the command line, working with common text editors such as Nano and Vim, the basics of remote computing, and elementary version control with Git. Students will also be exposed to the Python programming language, and work towards the completion of a data visualization project by the end of the week.

  • Track 2 is for students who have previously completed Track 1 or have similar levels of previous experience. Students will learn more advanced topics in remote computing such as scheduling, more advanced topics in version control including branching and merging repositories, more extensive Python programming, and work towards the completion of a project involving more advanced data analysis.


The Summer School curriculum will include short lectures and/or video tutorials followed by hands-on practice and labs, as well as guest lectures by leading researchers who have leveraged large-scale data for scientific advancement.  

The summer school is supported by an NSF EPSCoR Track-II Research Infrastructure Improvement Program grant and will be led by the PIs: 

  • Michelle Greene, PhD (Bates College)

  • Ben Balas, PhD (North Dakota State University)

  • Mark Lescroart, PhD (Univ. of Nevada, Reno)

  • Paul MacNeilage, PhD (Univ. of Nevada, Reno)

Participation Stipend

Students who complete the entirety of the Summer School will be offered a $500 stipend to offset costs associated with attending the summer school. 

Eligibility and Requirements

We encourage submissions from undergraduate students and early-stage graduate students with little or minimal computer science training.  We actively encourage applications from students whose identities have historically been under-represented in computational science.

Per NSF EPSCoR funding requirements, to be eligible for the Big Data Summer School, you must be a resident of one of the following EPSCoR states: Alabama, Alaska, Arkansas, Delaware, Guam, Hawaii, Idaho, Iowa, Kansas, Kentucky, Louisiana, Maine, Mississippi, Montana, Nebraska, Nevada, New Hampshire, New Mexico, North Dakota, Oklahoma, Puerto Rico, Rhode Island, South Carolina, South Dakota, Vermont, US Virgin Islands, West Virginia, or Wyoming. By applying, you certify that you intend to attend the entire summer school if your application is accepted.

Due to the virtual nature of the Summer School, students will be required to provide a computer with an internet connection with a download speed of at least 5 MB/sec. 

Applications

To apply for the Big Data Summer School, please complete this application form.

Review and Selection Process

All applications meeting the eligibility requirements will be placed in a random lottery for available spots.

Important Dates

  • Applications are accepted on a rolling basis until the spaces are filled.  Applications received after that point will be put on the waiting list

  • March 15, 2021:  First round of admissions notified

  • June 28 - July 2, 2021:  Workshop

Contact

For more information, please contact: Maggie Diamond-Stanic, Grant Coordinator, mdiamond@bates.edu

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