Functional Programming for Data Scientists
summary
Argues that functional programming yields concrete wins in data work: shows numpy and pandas behavior (immutability, inplace=False, vectorized ops), demonstrates map/filter/reduce style across tiny scripts and a Hadoop-style wordcount pipeline, notes Python 3 map laziness (iterator) and benefits for concurrency and testability, sketches a managed notebook environment tailored for education (demoed), emphasizes avoiding side effects with pure functions and gives reproducible examples of composition and transformation.
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chapters (8)
- 0:00Functional programming intro
- 2:26Why functional programming matters in data science
- 7:04FP defined and its history
- 13:22Immutability in Python
- 18:24Pure functions and side effects
- 22:00Imperative vs declarative
- 25:12Map filter reduce
- 30:45Interactive quiz
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