A foundational course in data structures and algorithms using Python. Covers arrays, linked lists, stacks, queues, heaps, time complexity, Big O notation, binary search, bubble sort, and applied probl
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Intro to Data Structures & Algorithms is a foundational course designed to teach backend engineers the core data structures and algorithmic thinking they need to solve real engineering problems and pass technical interviews — all implemented in Python.
This course begins with a grounding in what data structures and algorithms are, why they matter for backend
Master Intro to Data Structures & Algorithms from fundamentals to advanced concepts
Build real-world projects applying what you've learned
Understand best practices and industry standards
Gain confidence to work on production-level applications
Prepare for technical interviews and career advancement
This chapter introduces data structures and algorithms as a discipline, explaining why DSA knowledge is essential for backend engineers who want to design efficient systems, write performant code, and pass technical interviews.
This chapter provides an overview of the fundamental data structures you'll encounter as a backend engineer — what they are, how they organize data, and the trade-offs between different structures in terms of access, insertion, deletion, and memory usage.
This chapter introduces algorithmic thinking in Python — how to approach problems systematically, break them into steps, and translate solutions into clean, efficient Python code.
This chapter covers time complexity — how to measure and reason about the performance of your code as input sizes grow, enabling you to make informed engineering decisions about algorithm selection and optimization.
This chapter covers Big O notation — the standard mathematical framework for classifying algorithm performance. You'll learn to read, write, and reason about Big O expressions that describe how your code behaves as data grows.
This chapter covers the binary search algorithm — one of the most fundamental and efficient searching techniques in computer science. You'll implement it in Python and understand why it's a cornerstone of backend system optimization.
This chapter explores linear and polynomial time complexity in depth — helping you recognize these performance characteristics in your own code and understand their implications for backend system scalability.
This chapter covers the bubble sort algorithm — a foundational sorting technique that teaches core sorting mechanics and serves as a baseline for understanding more efficient sorting algorithms.
This chapter provides a focused overview of three essential data structures — linked lists, stacks, and queues — explaining their structures, operations, and the backend engineering scenarios where each one is the right choice.
This chapter focuses on applying data structure and algorithm concepts specifically with arrays — solving problems, implementing operations, and understanding the performance characteristics of array-based solutions in Python.
This chapter focuses on applying DSA concepts with linked lists — implementing operations, solving problems, and understanding when linked lists outperform arrays in backend engineering contexts.
This chapter focuses on heaps — a tree-based data structure that enables efficient priority-based operations. You'll learn how heaps work, implement them in Python, and understand their critical role in backend systems like task scheduling and priority queues.
This chapter focuses on stacks — the Last-In-First-Out (LIFO) data structure. You'll implement stack operations in Python and solve problems that demonstrate why stacks are essential for expression evaluation, undo systems, and recursive thinking.
This chapter focuses on queues — the First-In-First-Out (FIFO) data structure. You'll implement queue operations in Python and understand why queues are foundational to request processing, job scheduling, and message-driven backend architectures.
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"I truly appreciate the high-quality material in this course. The structured lessons, hands-on projects, and clear explanations make learning a great experience. I look forward to future additions and updates! Thanks for your polite and friendly attitude."
"The practical examples and hands-on exercises were particularly beneficial. They not only reinforced the theoretical concepts but also allowed me to apply them in real-world scenarios. The inclusion of best practices and common pitfalls added a practical dimension to the learning process."
"The course is an excellent resource for beginners. Your explanations of the basics are clear, making it easy for newcomers to grasp. I particularly enjoyed the task management application; it's a practical example that helps solidify the concepts."
Some programming experience is recommended. Check the course description for specific prerequisites.
This course has 1 hours of content. Complete it at your own pace.
Yes! Upon completion you'll receive a certificate you can share on LinkedIn and your resume.
Yes, all code examples and resources are available for offline access.
Yes — career guidance, resume reviews, and mock interviews are available through the platform.