dslp
missing-semester
dslp | missing-semester | |
---|---|---|
4 | 375 | |
389 | 4,704 | |
2.6% | 1.1% | |
1.8 | 6.8 | |
about 3 years ago | 2 months ago | |
CSS | ||
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
dslp
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Data Science Project Documentation
One great resource that I’ve found if you leverage GitHub is this DSLP process. Really succinctly ties everything to the code and it’s pretty quick to pick up in my experience. https://github.com/dslp/dslp
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New DS here. Where can I learn best practices for organizing a project, folder structure, BASH scripting/scheduling, etc?
As an addendum to u/GryffinLoL I’d add this resource if you’re using any kind of VCS tooling. It has some solid suggestions.
- Does anyone know of comprehensive refresher material for a once Senior Data Scientist?
- What is the best structured ds project you have seen?
missing-semester
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Ask HN: I want to learn to use the terminal, where do I start
The missing semester of your cs education
https://missing.csail.mit.edu/
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Please advise, still struggling intensely
You mentioned having issues with accessory concepts so perhaps this might help: https://missing.csail.mit.edu/. There's also a chapter on git
- Curso del IPN
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CS2030S and CS2040S advice
https://missing.csail.mit.edu/ is a good way to pass the Dec-Jan break if you want to prep for CS2030S + some more general stuff.
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I cancelled my Replit subscription
Reflecting a little bit more I don't think it was replit's fault, per-say. But that change should have been made together with a larger adjustment to the program. Like adding a class/unit in the style of [the missing semester](https://missing.csail.mit.edu/) to make sure people came away with a good range of intuitions.
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Advice to a Novice Programmer
From MJD's post: I think CS curricula should have a class that focuses specifically on these issues, on the matter of how do you actually write software?
But they never do.
FWIW, MIT's "The Missing Semester of Your CS Education" attempts to deal with this lack, though, even there, it's an unofficial course taught between terms, during MIT's IAP -- Independent Activities Period[1] -- and not an actual CS course.
[0] https://missing.csail.mit.edu/
[1] https://en.wikipedia.org/wiki/Traditions_and_student_activit...
- School of SRE: Curriculum for onboarding non-traditional hires and new grads
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Advice / Resources from a "Seasoned Beginner"
Link to the "missing semester of your CS degree" course by MIT.
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MIT's Missing Semester Class: Beyond the CS Curriculum
Rightly called The Missing Semester (of Your CS Education), this class from MIT will teach you how to use some of the tools that are fundamental to the software engineering ecosystem. From shell scripting to the fundamentals of information security—spanning around 12 lectures—you can add a bunch of practical skills to your toolbox.
- ¿Recomendaciones sobre que aprender?
What are some alternatives?
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
cs-topics - My personal curriculum covering basic CS topics. This might be useful for self-taught developers... A work in development! This might take a very long time to get finished!
projects - Sample projects using Ploomber.
computer-science - :mortar_board: Path to a free self-taught education in Computer Science!
govcookiecutter - A cookiecutter template for data science projects within His Majesty's Government and wider public sector.
CS50x-2021 - 🎓 HarvardX: CS50 Introduction to Computer Science (CS50x)
awesome-datascience - :memo: An awesome Data Science repository to learn and apply for real world problems.
vimrc - The ultimate Vim configuration (vimrc)
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
javascript - JavaScript Style Guide
materials - Bonus materials, exercises, and example projects for our Python tutorials
flexboxfroggy - A game for learning CSS flexbox 🐸