Data Structures & Algorithms (Python): Practice Exams

Data Structures & Algorithms (Python): Practice Exams – taught by Himanshu Kaushik – is a comprehensive Udemy course that helps you master interview‑ready coding skills.
Search for a free data structures course, data structures udemy course, or learn algorithms online and you’ll find this updated 2026 offering.
The program focuses on Python‑based data structures, algorithmic analysis, and realistic practice exams that mirror FAANG technical screens.
By completing the assessments you earn a Udemy certificate and gain the ability to write high‑performance, production‑grade code.

What You'll Learn

• Build efficient Python solutions by calculating Big O time and space complexity for worst‑case, average‑case, and best‑case scenarios.
• Master selection of optimal data structures—Hash Maps, Linked Lists, Stacks, and Queues—to eliminate engineering bottlenecks.
• Learn traversal techniques for hierarchical data using Trees (BST, AVL) and graph algorithms such as BFS and DFS.
• Understand dynamic‑programming strategies, including memoization and tabulation, to convert brute‑force recursion into scalable solutions.
• Create robust sorting implementations and analyze their performance characteristics within real‑world contexts.
• Implement 200 realistic interview questions that simulate FAANG coding screens, reinforcing data structures and algorithm concepts.
• Apply Python best practices for clean, readable code that passes both unit tests and whiteboard interviews.
• Analyze trade‑offs between memory usage and speed when choosing between array‑based and pointer‑based structures.

Course Details

• Instructor: Himanshu Kaushik
• Rating: 4.7 stars
• Level: Intermediate
• Language: English (US)
• Enrolled students: 13,348
• Certificate: Yes, upon completion
• Includes: Lifetime access, mobile‑friendly video streaming, downloadable practice test PDFs

What This Course Covers

Big O Analysis & Complexity

• Define worst‑case, average‑case, and best‑case scenarios for common algorithms.
• Calculate time and space complexity using formal Big O notation.
• Compare algorithmic efficiency through practical coding examples.
• Identify hidden costs in recursive and iterative implementations.

Core Data Structures

• Implement Hash Maps, Linked Lists, Stacks, and Queues in Python.
• Evaluate each structure’s suitability for specific problem domains.
• Solve engineering bottlenecks by selecting the most appropriate underlying container.
• Write unit tests that verify correct behavior under edge‑case inputs.

Tree & Graph Traversal

• Construct Binary Search Trees (BST) and self‑balancing AVL trees.
• Perform depth‑first search (DFS) and breadth‑first search (BFS) on graph representations.
• Apply traversal techniques to real‑world scenarios such as routing and hierarchical data queries.
• Optimize traversal algorithms for memory consumption and execution speed.

Dynamic Programming & Sorting

• Translate brute‑force recursion into memoized and tabulated dynamic‑programming solutions.
• Implement classic sorting algorithms (Merge Sort, Quick Sort, Heap Sort) and assess their Big O performance.
• Combine sorting with data‑structure selection to achieve optimal end‑to‑end runtimes.
• Debug common pitfalls like off‑by‑one errors and stack overflow in recursive calls.

Practice Exams & Real‑World Scenarios

• Complete four full‑length practice exams containing 200 curated questions.
• Tackle scenario‑based challenges such as undo/redo stacks for graphic editors.
• Solve shortest‑path problems for logistics routing using BFS and Dijkstra’s concepts.
• Analyze high‑frequency trading matching engine requirements through algorithmic design.

Who Should Take This Course

• Aspiring software engineers preparing for technical coding interviews at top‑tier tech firms.
• Backend developers seeking deeper mastery of Python data structures and algorithmic efficiency.
• Data scientists who need to optimize data‑processing pipelines with scalable algorithms.
• Intermediate programmers aiming to transition into senior engineering roles.
• Students targeting certification or portfolio projects that demonstrate algorithmic competence.

Prerequisites

• Basic proficiency in Python programming, including functions and control flow.
• Familiarity with fundamental programming concepts such as variables, loops, and conditional statements.
• Recommended: prior exposure to elementary data structures (arrays, lists) to accelerate learning.

Why Enroll in This Course

This course delivers interview‑focused practice without hidden fees, and a free coupon provides 100 % off for a limited time.
The curriculum mirrors real FAANG questions, ensuring you practice the exact problems recruiters use.
Learning outcomes translate directly into on‑the‑job productivity, helping you write faster, cleaner Python code.
Because the material is self‑paced, you can start immediately and progress at a comfortable speed.

Course Highlights

• Lifetime access to all video lectures, practice exams, and downloadable resources.
• Self‑paced learning lets you study whenever and wherever you choose, on desktop or mobile.
• Certificate of completion validates your expertise for employers and professional networks.
• 200 realistic practice questions simulate the difficulty of top‑tech technical screens.
• Detailed solution walkthroughs explain not