Network Data Science

Instructor: Ben Pedigo

Johns Hopkins University
NeuroData lab
@bdpedigo (Github)
bpedigo@jhu.edu

About me

• PhD Candidate in NeuroData lab
• My work focuses on using statistical and computational techniques to help understand nanoscale connectomes.
• I am a lead developer of a Python package for network data science called graspologic, which we co-develop with Microsoft Research.
• I did a couple of internships at Microsoft Research, working on techniques for analyzing search and communication networks.

About me

A few of my other interests are birdwatching, bouldering, and biking.

About you?

• Name?
• Year?
• Major?
• Research interests? (if you have any)
• Hobbies?

Goals

Course logistics

• Course meets MTWThF, 1-2:20pm ET, on Zoom
• Last 20-30 minutes of each day will hopefully be for project work time
• I will be available ~2:20-2:45pm ET (or when everyone leaves) - think of this as extra office hours
• Syllabus: https://bdpedigo.github.io/networks-course/syllabus.html (please review)
• Calendar: https://bdpedigo.github.io/networks-course/calendar.html
• Everything will be on the course website above, not going to use Blackboard for much.
• Class communication will be via Discord.

Grading

(straight from the syllabus)

To pass, a student must:

• Attend and engage with every lecture (please let the instructor know if you need to miss a lecture for some reason)
• Submit the pre-project mini-assignment by 11:59pm on Jan 5th
• Submit any other mini-assignments (these may be very minor things like filling out surveys, telling me what your final project will be, etc.)
• Submit a merge-able final project notebook by 12pm (noon) on Jan 20th (Thursday)
• Present their final project on the last day of class, Jan 21st (Friday)

Mini-assignment

• Need to make a pull request to this class's repository on GitHub
• Due Jan 5th at 11:59pm
• Highly encouraged to come to class time, work with other students
• Description of the mini-assignment is here (and on sidebar for course website).

Final project - notebook

• The final project is a short analysis of some dataset in the form of a Jupyter Notebook
• The notebook should describe (using text and figures):
  • The dataset(s) you investigated, and what you wanted to learn from them
  • The methods you used, explained at a level that someone else in the class would definitely understand
  • The results you found, with a few figures
  • A brief discussion of the meaning of these results

Final project - notebook

• You are welcome to collaborate, but
  • Each student must submit their own notebook and write their own code.
  • If there's something you want to collaborate more extensively on, discuss with instructor.
  • Acknowledge any people you collaborated with in the notebook.
• Reference any articles, data sources, etc. you used in your notebook.
• Notebooks are due Thursday, January 20th at 12pm (noon) ET. This will give me time to address any issues with your pull requests and merge them by the time we have presentations the next day.
  • You are encouraged to submit pull requests earlier than this, and I am happy to provide feedback!

Final project - presentation

• Everyone will present their final projects on the last day of class, January 21st.
• Presentations should be <5 minutes (no longer than 5) with 2 minutes for questions.
• Presentation should briefly cover the same main points from the notebook.
  • Some details from the notebook you may not have time to cover in the presentation; that's OK.

Feedback

• I am always open to feedback (positive or negative). You can leave any comments you have here: https://forms.gle/UxFB2sSQiQyrxr5AA
• Can be totally anonymous, if you'd like.