Artificial Intelligence & Data Science
Data Structures & Algorithms (DSA) with AI-Driven Optimization Course
4 Months
Advanced
Certified Course
Contact Us
+918160529351
info.nodetolearn@gmail.com
Surat, Gujarat
Course Overview
Most students can write code. But writing code that actually runs fast, uses memory wisely, and handles large inputs without breaking? That's what separates a good developer from a great one. This DSA course teaches you exactly that — how data is stored, how algorithms work, and how to solve problems efficiently. These are the skills that top tech companies test in every interview.
At NodeToLearn, you learn through 1-on-1 mentorship, not in a batch of 30 students. You get a dedicated workstation, and every concept — from arrays and linked lists to trees, graphs, and dynamic programming — is practiced hands-on, not just memorized from slides. You also learn how to use AI-based code tools to review and optimize your solutions, which is increasingly expected in real software teams.
The course covers the full range: Big-O notation to measure how fast your code runs, classic data structures like stacks, queues, and binary search trees, sorting and searching algorithms, and advanced techniques like dynamic programming and greedy algorithms. You'll also build a small portfolio of algorithm visualizations and do mock whiteboard interview rounds before you finish.
This course is ideal if you're preparing for campus placements, upskilling for backend roles, or getting serious about competitive programming. No prior DSA knowledge is required — just a basic understanding of any one programming language (C++, Java, or Python works fine).
At NodeToLearn, you learn through 1-on-1 mentorship, not in a batch of 30 students. You get a dedicated workstation, and every concept — from arrays and linked lists to trees, graphs, and dynamic programming — is practiced hands-on, not just memorized from slides. You also learn how to use AI-based code tools to review and optimize your solutions, which is increasingly expected in real software teams.
The course covers the full range: Big-O notation to measure how fast your code runs, classic data structures like stacks, queues, and binary search trees, sorting and searching algorithms, and advanced techniques like dynamic programming and greedy algorithms. You'll also build a small portfolio of algorithm visualizations and do mock whiteboard interview rounds before you finish.
This course is ideal if you're preparing for campus placements, upskilling for backend roles, or getting serious about competitive programming. No prior DSA knowledge is required — just a basic understanding of any one programming language (C++, Java, or Python works fine).
Who is this for?
This course is a strong fit for BCA, MCA, B.Tech (IT/CS) students who are 6–18 months away from placements and want to build solid DSA foundations for technical interviews. It's also useful for working developers — especially those in backend or full-stack roles — who never formally studied algorithms and want to fill that gap. Competitive programmers who want structured guidance beyond random online problem-solving will benefit too. You do need basic familiarity with at least one programming language (C++, Java, or Python) before joining — this is not a beginner programming course.
Career Outcomes
- Strong DSA skills open doors to roles like Software Engineer (backend or product)
- Backend Performance Engineer
- and Systems Engineer at product-based companies. It's also the foundation for Competitive Programming and roles in algorithm-heavy domains like fintech
- logistics
- or data infrastructure. Developers with solid DSA and AI tooling knowledge are increasingly sought for technical consulting work as well.
Course Curriculum
Module 1: How to Measure Algorithm Efficiency + Arrays
- Setting up your coding environment: compiler, debugger, and memory profiling tools
- Building a simple companion webpage to display algorithm steps visually using HTML5
- Big-O, Big-Omega, and Big-Theta notation: how to calculate best, worst, and average-case performance of any algorithm
- Array techniques: two-pointer approach, sliding window, and how memory is laid out in contiguous blocks
Module 2: Linked Lists, Stacks, and Queues
- Singly and doubly linked lists: building node chains, inserting, deleting, and reversing them in code
- Stacks (LIFO): implementing with linked nodes, understanding how function call stacks work internally
- Queues (FIFO): circular queues, priority queues, and double-ended queues (deque) — built and tested practically
- Debugging common pointer bugs: null references, boundary conditions, and memory leaks in linear structures
Module 3: Trees and Graphs
- Binary Search Trees (BST): inserting, searching, and deleting nodes; understanding tree height and balance
- Tree traversals: Pre-order, In-order, and Post-order — both recursive and iterative versions
- Graphs: representing them using Adjacency Matrix and Adjacency List, and when to use which
- Graph traversal algorithms: Breadth-First Search (BFS) and Depth-First Search (DFS) with real problem examples
Module 4: Searching, Sorting, and Greedy Algorithms
- Binary Search: O(log n) search logic, upper and lower bound problems, and common variations
- Sorting algorithms: Quick Sort (pivot selection), Merge Sort (split and merge), and comparing their time and space costs
- Greedy algorithms: solving problems by making the locally best choice — Huffman coding, interval scheduling, and activity selection
- Deploying your algorithm visualizer project live on a web host as part of your portfolio
Module 5: Dynamic Programming, AI Code Tools, and Interview Prep
- Dynamic Programming (DP): solving overlapping subproblems using Memoization (top-down) and Tabulation (bottom-up) with real problems like Knapsack and Longest Common Subsequence
- AI-assisted code review: using tools like GitHub Copilot or similar AI profilers to spot inefficiencies, suggest optimizations, and predict edge-case failures
- Using AI to analyze time and space complexity of your own code and compare alternate solutions
- Mock whiteboard interview rounds and final portfolio: an organized GitHub repository of solved problems and a live-hosted algorithm dashboard