Build on your SQL & BI skills / CORE PATH
NumPy & time-aware analysis
Understand array shape, masks and units before optimizing.
Use NumPy for numeric work; preserve exact currency separately.
Understand the idea
Understand array shape, masks and units before optimizing. Use NumPy for numeric work; preserve exact currency separately.
Work through the practice with a tiny fixture, predict the result before running it, and record one boundary case. Use the exit check as evidence before moving on.
What to learn
- Explain axes, broadcasting and boolean masks.
- Parse timestamps as aware UTC values and display Africa/Cairo when needed.
- Measure a transformation before replacing clear code.
Practice on your own data
Compare a list comprehension and an array operation on the same small numeric input.
Practice this lesson in the Python lab →
Results agree, units are documented and a timezone assumption is explicit.
Prerequisites
Video and playlist companions
Choose one explanation for the concept, then attempt the practice. These are topic mappings, not a claim that every linked lesson covers every outcome.
Python for Data Analysis
Loads YouTube on request. Open on YouTube ↗ if the owner restricts embedding.
03 تعليم الآلة , القسم الثالث : بايثون Machine learning , Python
Loads YouTube on request. Open on YouTube ↗ if the owner restricts embedding.
Sources and further reading
- NumPy · quickstart ↗Official documentation
- Python for Data Analysis ↗User-selected YouTube playlist
- 03 تعليم الآلة , القسم الثالث : بايثون Machine learning , Python ↗User-selected YouTube playlist · also in the inherited DE catalog · Inherited from data-engineering-learning-roadmap: Ahmed Shaaban mentorship
Track progress on this topic → · Markdown source ↗