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Big O notation: Python to Rust

This course is part of Rust for Data Engineering Specialization

Instructor: Noah Gift

Included with

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5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

6 hours to complete

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Some related experience required

6 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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This course is part of the Rust for Data Engineering Specialization

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There are 5 modules in this course

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.

Module details

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.

What's included

3 videos7 readings

3 videosTotal 4 minutes

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 readingsTotal 70 minutes

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

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.

What's included

3 videos6 readings

3 videosTotal 4 minutes

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 readingsTotal 60 minutes

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

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.

What's included

3 videos6 readings

3 videosTotal 3 minutes

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

6 readingsTotal 60 minutes

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

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."

What's included

3 videos6 readings

3 videosTotal 3 minutes

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

6 readingsTotal 60 minutes

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

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.