These are my auto-generated cliff notes, produced in part with my CMPIF2100 Lab Transcriber. As per the permission given during the 5/28 study hall, I'm sharing them with the rest of the cohort. I'll do my best to keep this repo updated.
The notes are organized by module:
Canyon Notes/(the entire first half of the semester woven into one deep, illustrated lesson - the deepest tier, big brother to the Cliff Jumper notes)Cliff Jumper Notes/(each module's notes woven into one connected lesson)Cliff Notes Module 01/(course orientation, what data science is, the data-science pipeline, and list comprehensions (part 1))Cliff Notes Module 02/(list comprehensions (part 2))Cliff Notes Module 03/(the programming process and mindset: getting unstuck, problem-solving, and practical Python tips)Cliff Notes Module 04/(statistics foundations: variance, standard deviation, standard error, confidence intervals, and random sampling)Cliff Notes Module 05/(NumPy: 1D and 2D arrays, reshaping and transposing, conditional filtering, summary statistics, and random number generation)Cliff Notes Module 06/(pandas basics: Series, DataFrames, selecting columns, and filtering rows)Cliff Notes Module 07/(pandas wrangling: grouping and aggregation, concatenation, summarizing Series/DataFrames, and missing-value EDA)Cliff Notes Module 08/(visualization foundations: matplotlib and seaborn, bar charts, histograms, boxplots, plotting from pandas, and choosing a plot type)Cliff Notes Module 09/(relationship plots: scatterplots and point density, categorical point plots, faceted histograms, conditional KDE, and violin plots)Cliff Notes Module 10/(many continuous variables at once: pair plots, correlation coefficients and heat maps, seaborn styles and color palettes, and standardization with scikit-learn)
Per-subject documents that focus on a single topic in depth, rather than following the course module by module. See the folder's README for details.
Trainers/
Standalone walkthroughs and setup guides. Each linked folder is its own repository, gathered here for convenience:
Guides/Goodbye AI - On the Road with VS Code.pdf(going AI-free in VS Code, the follow-up to the Coursera-to-VS-Code guide)Guides/UCFCECS_ALL_PYTHON_LOOPS_INTRO/(an intro to every kind of Python loop, from the UCF Assisted Learning Center)Guides/upitt-anaconda-setup-guide/(setting up Anaconda for CMPINF-2100 at the University of Pittsburgh)Guides/CourseraToVSCode/(a guide to using VS Code with Coursera, the prequel to the Goodbye AI guide above)
Small utilities I built for the cohort. Each linked folder is its own repository, gathered here for convenience:
Tools/CMPIF2100_LAB_VER_CHECK/(two lines of script that save your sanity checking package versions)Tools/CMPIF2100-Lab-Transcriber-2.0/(records Windows system audio and transcribes lab sessions live)Tools/VEShell/(Very Easy Shell: an Electron + xterm.js + node-pty terminal wrapper)
A note on these folders: the items under
Guides/andTools/are Git submodules, they point to their own repositories so nothing is duplicated here. Browsing on GitHub just works: click a folder and it takes you to that repo. If yougit clonethis repo and want those folders filled in on your machine, add--recursive(or rungit submodule update --init --recursiveafterward).
Also included is Sandbox.ipynb, an entirely blank-slate Jupyter notebook I use as "scratch paper" while working through assignments. Feel free to use it the same way.
- DatJavaClass (Victor S), author and director. Conceived these notes, established their format and structure, directed their creation, and fact-checked, edited, and quality-controlled every one, with assistance by Claude. Some material may have been derived from assigned material, but has not been copied verbatim. For source materials please contact CMPINF-2100 Faculty and Assistants.
As Always! You're awesome, Stay awesome! and I wish everyone the best of grades!