Experiment 06
Python DSA Learning Log
I’m learning data structures and algorithms in Python on the way to data science, and I’m doing it in public: practice files by level, interactive note pages, and a summary report that points at what to clean up next.
- Period
- Mar to May 2026
- Stack
- Python, HTML/JS note pages
- Scope
- 26 Python files, 9 note pages, 16 themes
- Status
- Learning log, not a library
QQuestion
Can I learn DSA in public and keep myself honest?
Writing code is one thing; being able to explain it is another. The notes pages force the second: each topic gets a dry-run table or a graph builder you can click through.
MMethod
Three levels of practice, nine note pages, one summary report
- Practice. Basics, intermediate and advanced folders of Python files.
- Notes. Nine interactive pages, such as a clickable dry-run table and a graph builder.
- Review. A summary report lists learning coverage, topic balance, the most ready files and the best next cleanup.
RResult
16 themes covered so far
Linked lists, queues, stacks, heaps, dictionaries and sets; binary search, two pointers, recursion and matrix traversal; strings, trees, graphs and prime numbers; plus object-oriented classes and hash-based grouping.
CCaveat
A learning log, not a library
- The files are practice, at different levels of polish. The summary report itself marks which are ready and which need cleanup.
- Nothing here is production code. It is evidence of habit: showing up, writing it down, and fixing what is weak.
Contact
Got a messy dataset or a science question?
I read everything and answer when I can. The quickest way is email, and LinkedIn works too.