- Data Camp link
- Youtube course https://www.youtube.com/watch?v=yKi9-BfbfzQ&t=6s&ab_channel=freeCodeCampEspa%C3%B1ol
- https://pll.harvard.edu/course/cs50s-introduction-programming-python?delta=0
- Intro to Python (version 3): https://www.codecademy.com/learn/learn-python-3
- If that one at some point asks you to upgrade to Pro version, then this one should be free: Python version 2: https://www.codecademy.com/learn/learn-python
- https://aeturrell.github.io/coding-for-economists/code-preliminaries.html
- Pandas:
- https://aeturrell.github.io/coding-for-economists/data-analysis-quickstart.html
- Interactive Colab book on Pandas, numpy, machine learning: https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/03.01-Introducing-Pandas-Objects.ipynb#scrollTo=a9CSUN-4sRi-
- Basic intro: https://statsthinking21.github.io/statsthinking21-core-site/
- Stats for experiments: https://experimentology.io/
- It's in R, but you can try to work through the sections in Python.
- https://sebastianraschka.com/blog/2020/intro-to-dl-ch01.html
- Basic pipeline: https://machinelearningmastery.com/machine-learning-in-python-step-by-step/
- https://docs.google.com/document/d/1xA8nz5CvB1iQaRbvFv0XRnPbkm7yYQ18lFcKmP-IFmY/edit?usp=sharing
- Python code (in progress): https://github.com/danielmlow/tutorials/blob/main/machine_learning/Machine_Learning_Checklist_Paper.ipynb
A bit more in depth ML: https://sebastianraschka.com/blog/2021/ml-course.html
- General intro: https://medium.com/data-science-at-microsoft/causal-inference-part-1-of-3-understanding-the-fundamentals-816f4723e54a
- https://press.princeton.edu/books/hardcover/9780691199429/data-analysis-for-social-science
- Gelman, A., Hill, J., & Vehtari, A. (2021). Regression and Other Stories. Cambridge University Press.
- Free: Online and implementation in R https://mixtape.scunning.com/
- Free: Book, videos, and slides: https://www.bradyneal.com/causal-inference-course
- Free: More advanced (free, online, videos, book) https://learning.edx.org/course/course-v1:HarvardX+PH559x+2T2023/home
- You can use colab, but eventually you'll want to switch to vscode for faster coding and debugging.
- Make sure you use shortcuts for everything and dont click things (its too slow). Key shortcut: highlight code in a .py script and running in a second panel interactive jupyter notebook window. After installing Jupyter extension in VS code, map "Jupyter: Run Selection/Line in Python Interactive Window" to command enter
Open your keybindings.json (Cmd+Shift+P → "Open Keyboard Shortcuts (JSON)") and ctrl+F (windows) or cmd+F (mac) "cmd+enter" and replace for "" for all commands except for "command": "jupyter.execSelectionInteractive" where you can add it again.
Then replace:
{
"key": "cmd+enter",
"command": "jupyter.execSelectionInteractive",
"when": "editorTextFocus && isWorkspaceTrusted && jupyter.ownsSelection && !findInputFocussed && !isCompositeNotebook && !notebookEditorFocused && !replaceInputFocussed && editorLangId == 'python'"
}
for this:
{
"key": "cmd+enter",
"command": "jupyter.execSelectionInteractive",
"when": "editorTextFocus && isWorkspaceTrusted && editorLangId == 'python'"
}
- https://github.com/danielmlow/tutorials/blob/main/run_python.md
- https://github.com/danielmlow/tutorials/blob/main/virtual_environment.md
-
Tutorial: https://github.com/danielmlow/llm_course/blob/main/openrouter_api.ipynb
-
Workshop video (2hs): https://mindandlife.zoom.us/rec/share/MvBsiFDPoKERnJktnsLYn2UOjGynrFPUlo-tjamaO-HEOj7IeF-I7Gyk6W4lvquL.GsgMEEcFb7Lxf2j8?startTime=1761753753000
- Passcode: 6.#ZxEDK
-
Tutorials from workshop: https://github.com/danielmlow/llm_course
-
Codebook for 49 suicide risk factors: https://github.com/danielmlow/construct-tracker/blob/main/src/construct_tracker/data/lexicons/suicide_risk_lexicon_v1-0/suicide_risk_lexicon_codebook_prototypical_examples.txt
-
Paper explaining method (see Figure 3 on LLMs): https://osf.io/preprints/psyarxiv/9rdux_v4