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Big O notation: Python to Rust

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Big O notation: Python to Rust

Dieser Kurs ist Teil von Spezialisierung „Rust for Data Engineering“

Dozent: Noah Gift

Bei enthalten

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5 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

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6 Stunden zu vervollständigen

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5 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Einige einschlägige Kenntnisse erforderlich

6 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

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Dieser Kurs ist Teil der Spezialisierung Spezialisierung „Rust for Data Engineering“

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In diesem Kurs gibt es 5 Module

Big O notation: Python to Rust is a hands-on algorithmic complexity course for engineers transitioning from Python to Rust who want to reason rigorously about how their code scales. You will learn Big O, Big Theta, and Big Omega notation; analyze the time and space complexity of common operations on Python and Rust data structures (list/Vec, dict/HashMap, set/HashSet, tuple, slice, BTreeMap); and compare measured performance in both languages on identical workloads. The course covers amortized analysis, recursion and master theorem, worst case versus expected case, the cost of allocation and borrowing, and how Rust's ownership model affects real-world constants even when asymptotic complexity is identical. You will profile Python code with cProfile and timeit, profile Rust code with criterion and perf, and translate Python algorithms (search, sort, hashing, graph traversal, dynamic programming) into idiomatic Rust while keeping or improving complexity guarantees. By the end of the course, you will be able to predict performance, choose appropriate data structures, justify rewrites from Python to Rust on quantitative grounds, and communicate trade-offs to a team. Part of the Rust for Data Engineering specialization.

Moduldetails

Set the foundation: define what "complexity" actually means as a claim, and meet the three modes of proof — analytical, empirical, and structural — you'll use to defend complexity claims throughout the course. Learn to recognize falsifiable vs. unfalsifiable performance claims and build the worked-example habit you'll need in later modules.

Das ist alles enthalten

3 Videos7 Lektüren

3 VideosInsgesamt 4 Minuten

1.1.1 What Complexity Means And Three Modes1 Minute
1.2.1 Reading A Complexity Claim1 Minute
1.3.1 Depyler Transpiler Shortcut1 Minute

7 LektürenInsgesamt 70 Minuten

About This Course10 Minuten
Key Terms: What "Complexity" Means + Three Modes of Proof10 Minuten
Reflection: What "Complexity" Means + Three Modes of Proof10 Minuten
Key Terms: Reading a Complexity Claim — Falsifiability10 Minuten
Reflection: Reading a Complexity Claim — Falsifiability10 Minuten
Key Terms: Depyler — the Transpiler Shortcut10 Minuten
Reflection: Depyler — the Transpiler Shortcut10 Minuten

Empirical proof in practice: measure runtime with reproducible benchmarks. Three head-to-head Python→Rust translations — list comprehension to iterator, dict lookup to HashMap, and sorted() to sort_unstable — let you read benchmark output, control for noise, and decide when measured speedups are real and when they're artifacts.

Das ist alles enthalten

3 Videos6 Lektüren

3 VideosInsgesamt 4 Minuten

2.1.1 List Comp To Iterator1 Minute
2.2.1 Dict Lookup To Hashmap1 Minute
2.3.1 Sorted To Sort Unstable1 Minute

6 LektürenInsgesamt 60 Minuten

Key Terms: List Comprehension → Iterator10 Minuten
Reflection: List Comprehension → Iterator10 Minuten
Key Terms: x in dict → HashMap Lookup10 Minuten
Reflection: x in dict → HashMap Lookup10 Minuten
Key Terms: sorted() → sort_unstable10 Minuten
Reflection: sorted() → sort_unstable10 Minuten

Structural proof in practice: use the type system to make incorrect programs impossible to compile. Translating Optional[T] to Option<T>, try/except to Result<T,E>, and ad-hoc state machines to Rust enums turns runtime errors into compile-time errors — a structural guarantee no benchmark can refute.

Das ist alles enthalten

3 Videos6 Lektüren

3 VideosInsgesamt 3 Minuten

3.1.1 Optional To Option1 Minute
3.2.1 Try Except To Result1 Minute
3.3.1 Mutable Default To Ownership1 Minute

6 LektürenInsgesamt 60 Minuten

Key Terms: Optional[T] → Option<T>10 Minuten
Reflection: Optional[T] → Option<T>10 Minuten
Key Terms: try/except → Result<T, E>10 Minuten
Reflection: try/except → Result<T, E>10 Minuten
Key Terms: Mutable Default Arg → Ownership10 Minuten
Reflection: Mutable Default Arg → Ownership10 Minuten

Translation with runtime consequences: each translation in this module replaces a Python construct (generators, subprocess calls, parallel loops) with a Rust equivalent that carries different runtime guarantees — memory profile, error surface, parallelism model — not just "the same thing, but faster."

Das ist alles enthalten

3 Videos6 Lektüren

3 VideosInsgesamt 3 Minuten

4.1.1 Generator To Iterator1 Minute
4.2.1 Subprocess To Command1 Minute
4.3.1 Threading To Rayon1 Minute

6 LektürenInsgesamt 60 Minuten

Key Terms: Generator → Iterator10 Minuten
Reflection: Generator → Iterator10 Minuten
Key Terms: Subprocess → Command10 Minuten
Reflection: Subprocess → Command10 Minuten
Key Terms: Threading → Rayon10 Minuten
Reflection: Threading → Rayon10 Minuten

Capstone: bring all three modes of proof together on a real translation. Two case studies — a three-mode playbook end to end, and a deliberate "when NOT to translate" example — train your judgment about when a Python→Rust port pays off and when it would just add cost without measurable benefit.