Python Data Structures and Algorithms: Complete Guide

Understand the algorithms, performance, and implementation of important data structures. Use advanced Python techniques.

What you'll learn
- Develop problem-solving and debugging skills essential for coding interviews.
- Apply algorithmic thinking to real-world tasks, writing efficient and readable Python code.
- Strengthen confidence in data-driven programming and technical interview readiness.
- Implement core data structures like arrays, stacks, queues, and linked lists from scratch in Python.
- Understand algorithm efficiency and analyze code performance using Big-O notation.
- Build and compare sorting and searching algorithms including merge, quick, and binary search.
- Design and implement trees, heaps, hash tables, and other advanced data structures.
- Use Python’s built-in tools effectively, such as heapq, deque, and sorted().

Requirements
- Basic knowledge of Python syntax and programming concepts is required.
- Completion of Learn Python Programming Masterclass (or equivalent experience) recommended.
- A computer with Python 3 installed and a code editor such as PyCharm, VS Code, or IntelliJ IDEA.
- No prior experience with data structures or algorithms is required — everything is taught from first principles.

Description
Master the foundations that power real-world Python.

Write sorting routines and build important data structures from scratch, then discover the Pythonic way to use them in practice. Write unit tests to validate your code. Master Python's special "dunder" methods.

Why this course?

If you’ve already learned the Python language (for example through Tim Buchalka’s Learn Python Programming Masterclass, rated 4.6/5 from over 100,000 reviews and taken by more than 430,000 learners), this new Python Data Structures and Algorithms course is the next logical step. It takes you fromI can write PythontoI can design efficient Python, using the data structures and algorithms employers expect.

You’ll be learning withTim BuchalkaandJP (Jean-Paul) Roberts, both highly experienced instructors. Tim is a Udemy Instructor Partner with over1.7 million studentsand more than460,000 reviewsacross his courses. JP brings his industry insight as a co-instructor, ensuring a practical, robust, and engaging learning experience. Their combined expertise means you can feel confident in the quality of this new course, even before reviews come in.

What makes this course different?

Build first, then go Pythonic.Each topic follows a clear pattern: theory, your own code, then Python’s built-in tools like sorted(), heapq, deque, and queue.

Hands-on, job-relevant coverage.Arrays, linked lists, stacks, queues, hash tables, sets, trees, heaps, searching, and sorting are all taught with Big-O analysis and unit tests. You’ll always know your code is correct.

Up-to-date Python.The course covers modern improvements in CPython, including Python 3.11’s Powersort for list.sort(), with comparisons to earlier approaches.

What learners say about Tim and JP’s teaching:

Note: These quotes reflect students’ experiences from the Python Masterclass. As this Python Data Structures and Algorithms course is newly published, reviews for this course will appear soon.

“Not slow, just the right speed if you already know programming… Excellent trainer thus far.”– Linda

“Great, thorough explanations. Very complete. Thank you!”– Anthony

“Exceptionally good… Really well done!!!”– Rakshan

Is this course for you?

Yes:If you already know basic Python and want to think like a software engineer—choose the right structure, reason about performance, and write clean, correct code.

Yes:If you’re preparing for coding interviews or want efficient, practical patterns for real projects.

No:If you’re brand-new to Python. Start with the Masterclass, then return here.

What you’ll learn

Foundations and Big-O:What data structures and algorithms are, time and space complexity, and trade-offs.

Arrays and lists:Memory model, resizing, slicing, iteration, and dunder methods for Pythonic sequences.

Linked lists:Singly/doubly linked lists, insert/delete, reverse, iterate, indexing, and slicing.

Stacks, queues, deques:Manual implementations, plus Python’s deque and queue.

Hash tables and sets:Open addressing, chaining, dict and set, and specialized collections.

Trees:Binary search trees (insert/search/delete), traversal strategies, fully implement a red-black tree.

Heaps and priority queues:Build heaps, Heapsort, and Python’s heapq.

Searching:Linear vs. binary search (iterative and recursive).

Sorting:Bubble, selection, insertion, merge, quick, counting, radix, and Powersort in Python.

Testing and correctness:Write comprehensive unit tests with unittest, inspired by CPython’s testing style.

Abstract Base Classes and Protocols:Use both and understand the differences between them.

Recursion:When to use it and, importantly, when it's not appropriate.

Your learning experience

Code-along videosthat take you from fundamentals to advanced structures.

Challenges and solutionsso you can cement your understanding.

Production patternsthat map directly to Python’s standard library, ready for work environments.

FAQ

Will I both implement algorithms and use Python’s built-ins?Yes. You’ll start by building your own, then master robust standard-library tools for production.

Does the course cover modern CPython sorting?Up-to-date Python.The course covers modern improvements in CPython, including Python'sassignment expressions(the walrus operator) and Powersort for list.sort(), with comparisons to earlier approaches.

Is there a refund policy?Yes. Udemy offers a 30-day refund window on eligible courses.

Enroll now

Join the next step in your Python journey and learn the data structures and algorithms that make great Python code possible from first principles to production-grade patterns with Tim Buchalka and JP Roberts guiding your way.

Who this course is for:
- Python developers ready to move beyond the basics and deepen their understanding of algorithms.
- Students preparing for technical interviews or coding challenges involving algorithmic problem-solving.
- Self-taught programmers seeking a computer-science foundation in data structures and efficiency.
- Professionals wanting to improve code performance, readability, and scalability in real projects.
- Learners who completed Tim Buchalka’s Python Masterclass and want the next step in their journey.
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