Algorithms and Data Structures: How AI Is Changing Learning in 2026

## Introduction Algorithms and data structures are the foundation upon which all modern development rests. Without an understanding of Big O, graphs, or dynamic programming, it's impossible to pass an interview at a top IT company or create efficient code. But traditional courses are often overloaded with theory and disconnected from practice. In 2026, artificial intelligence comes to the rescue: learning with AI on ASI Biont makes studying algorithms not only accessible but also maximally personalized. In this article, we'll explore how AI assistants help master complex topics—from simple sorting to NP-complete problems—and why the "Algorithms and Data Structures" course from ASI Biont has become the best choice for interview preparation. ## Why Are Algorithms and Data Structures Important? Every developer faces the need to optimize code. Knowledge of data structures (arrays, trees, hash tables) and algorithms (search, sorting, traversal) allows you to: - Write fast and scalable applications. - Confidently pass technical interviews (especially at FAANG and similar companies). - Solve real-world problems—from routing to big data analysis. Without this foundation, you risk creating code that "works" but crashes under load. The "Algorithms and Data Structures" course on ASI Biont fills these gaps. ## How Does AI Help in Learning Algorithms? Traditional courses often suffer from uniformity: all students follow the same template, regardless of their level of preparation. AI changes the game. On ASI Biont, learning with AI is structured as follows: - **Generation of personalized lessons.** AI adjusts the complexity of the material to your current level: if you already know the basics, it immediately moves to complex topics—for example, dynamic programming or graphs. - **Instant feedback.** After each block, you receive code examples and explanations tailored to your learning style. - **Focus on practice.** AI generates tasks that simulate real interviews: from finding the shortest path in a graph to evaluating time complexity via Big O. This approach saves hours of searching for materials online. Instead of reading dozens of articles, you get concentrated knowledge—with examples in Python, Java, or C++. ## Main Topics of the Course: From Big O to NP-Complete Problems The "Algorithms and Data Structures" course on ASI Biont covers all key sections. Here's what you'll study: ### 1. Complexity Analysis (Big O Notation) Understanding how an algorithm behaves as input data grows is the first step to writing efficient code. You'll learn to: - Distinguish between O(1), O(n), O(n²), and other classes. - Optimize solutions, avoiding quadratic complexity. ### 2. Basic Data Structures - **Arrays and lists**—the absolute basics. - **Stacks and queues**—for LIFO/FIFO problems. - **Hash tables**—for fast lookup (O(1) on average). ### 3. Trees and Graphs - **Binary search trees**—how to implement and balance them. - **Graph traversal** (BFS, DFS)—for pathfinding and network analysis. - **Dijkstra's algorithm**—finding shortest paths. ### 4. Dynamic Programming This is one of the most challenging topics in interviews. AI explains it through real-world cases: the knapsack problem, computing Fibonacci numbers with memoization, finding the longest common subsequence. ### 5. NP-Complete Problems You'll learn how to recognize NP-complete problems (e.g., the traveling salesman problem) and use approximation algorithms when an exact solution is impossible. ## Advantages of the Course on ASI Biont Why choose this particular course? Here are the key differences: | Feature | Traditional Courses | Course on ASI Biont | |----------------|-------------------|-------------------| | **Cost** | $100–500+ | Completely free | | **Adaptation to student** | Fixed program | AI generates content for you | | **Format** | Video or text | Text lessons with AI | | **Practice** | Textbook problems | Realistic interview cases | The course is completely free—no

Algorithms and Data Structures: How AI Is Changing Learning in 2026

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