This is the home page index
Home page index
Algorithm and Data Structure Study Plan for Data Science and Software Engineering
1. Basic Data Structures (2-3 weeks)
- Arrays and Strings
- Linked Lists
- Stacks and Queues
- Hash Tables
- Trees (Binary Trees, Binary Search Trees)
- Heaps
- Graphs (representation and traversal)
2. Basic Algorithms (2-3 weeks)
- Searching (Linear Search, Binary Search)
- Sorting (Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, Quick Sort)
- Recursion
- Breadth-First Search (BFS)
- Depth-First Search (DFS)
3. Advanced Data Structures (2-3 weeks)
- Balanced Trees (AVL Trees, Red-Black Trees)
- Trie
- Disjoint Set (Union-Find)
- Segment Trees
- Fenwick Trees (Binary Indexed Trees)
4. Advanced Algorithms (3-4 weeks)
- Dynamic Programming
- Greedy Algorithms
- Backtracking
- Divide and Conquer
- Graph Algorithms (Dijkstra’s, Bellman-Ford, Floyd-Warshall, Kruskal’s, Prim’s)
5. Algorithm Design Techniques (2-3 weeks)
- Time and Space Complexity Analysis
- Amortized Analysis
- Problem-Solving Strategies
6. Data Science Specific Topics (2-3 weeks)
- Probability and Statistics
- Linear Algebra basics
- Numerical Algorithms
- Machine Learning Algorithms (basics)
7. Software Engineering Specific Topics (2-3 weeks)
- Object-Oriented Design
- System Design basics
- Concurrency and Multithreading basics
8. Practice and Application (Ongoing)
- Coding Challenges (LeetCode, HackerRank, etc.)
- Implementation of data structures and algorithms from scratch
- Real-world problem solving using these concepts
Total estimated time: 15-22 weeks (3-5 months) for focused study
Note: This timeline assumes you’re dedicating significant time each day to studying. Adjust based on your schedule and prior knowledge. Continuous practice and application are key to mastering these concepts.