Join our Meetup group for more events! https://www.meetup.com/data-umbrella
- Transcript: https://github.com/data-umbrella/event-transcripts/blob/main/2023/73-gsoc.md
- Meetup Event: https://www.meetup.com/data-umbrella/events/290346752/
- Video: https://youtu.be/YE-TYJmvbfg
- Transcriber: ? [needs a transcriber]
- GSOC website: https://summerofcode.withgoogle.com/
- Application timeline: https://developers.google.com/open-source/gsoc/timeline
- Oriol GSOC blog: https://oriolabril.github.io/gsoc2019_blog/https://oriolabril.github.io/gsoc2019_blog/
- Meet the PyMC GSOC students: https://www.pymc.io/blog/blog_gsoc_2022.html
- Larry blog: https://larrydong.com/posts/2022-06-18-value-oss/
- VIDEO Software Testing in Open Source and Data Science https://youtu.be/bJGgVoV4GTc
- VIDEO Intro to Unit Testing and Continuous Integration https://youtu.be/vLBr_AfomUY
Google Summer of Code (GSOC) is a program Google Summer of Code is a global, online program focused on bringing new contributors into open source software development. GSoC Contributors work with an open source organization on a 12+ week programming project under the guidance of mentors. GSOC 2023 application begins in January 2023.
In this panel discussion, we speak with several GSOC alumni of the PyMC project to learn of their experiences including the application process, mentorship experience and post-GSOC open source participation.
## Timestamps
00:00 Data Umbrella introduction
04:17 Introduce speakers
07:00 Begin panel discussion
07:05 What is Google Summer of Code?
07:30 Q: Name, GSOC project, year of project, mentor name
07:50 Larry Dong: PyMC GSOC intern 2021, 2022
08:30 Oriol Abril Pla: ArviZ intern 2019
09:07 Tirth Patel: PyMC intern 2020, SciPy 2021
09:58 Q: How did you first learn of GSOC?
10:06 Larry
10:48 Oriol
11:14 Tirth
12:10 Q: How did you first get involved in open source?
12:17 Oriol
12:57 Larry
13:30 Tirth
14:38 Q: How did you choose your project?
14:48 Larry
16:02 Oriol
17:06 Q: Oriol, how did you prepare to apply to GSOC?
18:20 comment: looked at community, documentation, open issues to understand the project
18:58 Q: What are 3 things you have learned from your GSOC experience, how has it contributed to your professional growth?
19:00 Tirth: multicultural communication on GitHub, soft skills, reviews, tests, documentation, working in teams, helps get a job
21:50 Larry: statistics, Git, how to ask for help, career advice
24:20 Oriol: working collaboratively, programming paradigms, test and code driven development, wider view on programming, planning & coordinating, visibility, job opportunities
28:53 taking GSOC learnings to personal coding practices
29:35 Q: Is GSOC a good place to start for beginners to coding?
30:05 Oriol
31:19 Q: How did Larry and Tirth prepare their applications for GSOC?
31:27 Larry
33:32 Tirth (thoughts on applying to multiple GSOC projects); tips on applying to GSOC
37:40 Q: What is the process in applying for GSOC, with mentors?
41:10 Larry: getting feedback on proposal
42:03 Q: How did Larry balance GSOC application with graduate school?
45:07 Q: Does GSOC provide the project, or do you make suggestions for project ideas?
48:17 Q: Oriol, what is your experience as a GSOC intern and as a mentor for GSOC?
52:30 Q: Is GSOC for solo or teams?
53:50 Q: What are some challenges you faced during GSOC and how did you solve them?
54:10 Larry
55:38 Blog write-ups during GSOC
56:28 Tirth (tips on navigating a codebase)
59:30 Oriol (planning work)
01:02:30 Oriol: final tips
01:03:28 Larry: final tips
01:04:15 Tirth: final tips
Tirth Patel is a maintainer of SciPy and a contributor to a few other open-source projects like NumPy, PyMC, and scikit-learn. He participated in Google Summer of Code with PyMC in 2020 and with SciPy in 2021. He works primarily with Python and C, and he also codes with C++ and Julia. He is currently a graduate computer science student at Arizona State University.
- GitHub: https://www.github.com/tirthasheshpatel
- LinkedIn: https://www.linkedin.com/in/tirthasheshpatel/
Larry Dong is a PhD student in biostatistics at the Dalla Lana School of Public Health at the University of Toronto in Toronto, Canada. His academic interests revolve around dynamic treatment regimes and Bayesian methods. He began his PhD during the pandemic which has allowed him to be immersed in open-source, particularly in the PyMC community. His first GSoC project entailed implementing a Dirichlet Process submodule for PyMC and he returned for another GSoC to continue this project and to learn more about Aesara and AePPL.
- Website: http://larrydong.com/
- GitHub: https://github.com/larryshamalama
- LinkedIn: https://www.linkedin.com/in/larry-dong/
- Twitter: https://twitter.com/larryshamalama
- Mastodon: https://fosstodon.org/@larryshamalama
Oriol has a background in engineering physics and astrophysics and currently works as a computational statistician. He is a core contributor and council member of ArviZ and PyMC projects. He has also worked on statistical research while at Helsinki University and Universitat Pompeu Fabra (Barcelona), especially in the fields of inference diagnostics, prior elicitation and data visualization. Oriol dedicates a lot of his time to community management and documentation because he believes they are as important as the code. He has helped organize and mentored in multiple Data Umbrella sprints. He has also mentored many new ArviZ and PyMC team members whose backgrounds ranged from computational scientist to technical writer.
- Website: https://oriolabrilpla.cat/en/
- GitHub: https://github.com/OriolAbril
- Twitter: https://twitter.com/OriolAbril
- Mastodon: https://toot.cat/@oriolabril
- LinkedIn: https://www.linkedin.com/in/oriol-abril-pla-1b9123180/
