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ODU CS 395 Spring 2020 -- Research Methods in Data and Web Science

Instructor: Michael L. Nelson mln@cs.odu.edu

Office Hours: Wednesdays 2-4 and by appointment

Time: Wednesdays 4:20pm - 7pm

Place: Dragas, r. 1102

Pre-/Co-requisites: CS 330, CS 300T

Syllabus

Class Email list

Class Goals

This class is intended for academic juniors (or higher) considering a career in research in data and web science. Since research is not a solitary activity, class work will occur in groups. To prepare students for a research career, we will cover a range of topics including:

  • Data management and software engineering tools: Git/GitHub, Docker, Travis CI, cloud computing, virtual machines, etc.

  • Languages and Environments: Python, R, Unix CLI, LaTeX, Overleaf, REST APIs, etc.

  • Participating in the scholarly communication process: reading and summarizing papers, preparing and giving presentations, documenting your own research findings, the spectrum of scholarly communication, etc.

  • Preparing proposals: students will read, review, and finally prepare their own actual research proposals to be submitted to places like the National Science Foundation (NSF), Virginia Space Grant Consortium (VSGC), National Aeronautics and Space Administration (NASA), and others as idenfitied (and pending the student's eligibility).

  • Reproducibility and Replicability: students will work in teams to identify published data and web science studies that they will reproduce and/or replicate.

Class Schedule

Note: this class schedule is subject to change. Watch the class repo and monitor the class email list for updates.

Class Deliverables

At the end of the class, each student will have:

Class Grading

A combination of 11 assignments (written and/or oral) and class participation. Note the class will not have conventional mid-term and final exams, tests, quizzes, etc.

Instead, the grades will be determined by class participation and the quality of the aforementioned research deliverables.

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CS 395 Spring 2020 - Research Methods in Data and Web Science

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