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Data Structures and Algorithms with C++

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  1. Introduction to Data Structures and Algorithms
    Overview of Data Structures and Algorithms with C++
  2. Importance of Data Structures and Algorithms in Programming with C++
  3. How to Choose the Right Data Structure or Algorithm for a given Problem with C++
  4. Basic C++ Concepts Review
    Variables and Data Types in C++
  5. Control Flow Statements in C++
  6. Classes and Objects in C++
  7. Methods and Constructors in C++
  8. Basic Input and Output in C++
  9. Data Structures
    Arrays in C++
  10. Stacks and Queues in C++
  11. Linked Lists in C++
  12. Hash Tables in C++
  13. Trees in C++
  14. Types of Trees
    Binary Search Trees in C++
  15. Cartesian Trees in C++
  16. B-Trees in C++
  17. Red-Black Trees in C++
  18. Splay Trees in C++
  19. AVL Trees in C++
  20. KD Trees in C++
  21. Min Heap in C++
  22. Max Heap in C++
  23. Trie Trees in C++
  24. Suffix Trees in C++
  25. Sorting Algorithms
    Quicksort in C++
  26. Mergesort in C++
  27. Timsort in C++
  28. Heapsort in C++
  29. Bubble Sort in C++
  30. Insertion Sort in C++
  31. Selection Sort in C++
  32. Tree Sort in C++
  33. Shell Sort in C++
  34. Bucket Sort in C++
  35. Radix Sort in C++
  36. Counting Sort in C++
  37. Cubesort in C++
  38. Searching Algorithms
    Linear Search in C++
  39. Binary Search in C++
  40. Graphing Algorithms
    Breadth First Search (BFS) in C++
  41. Depth First Search (DFS) in C++
  42. Dijkstra’s Algorithm in C++
  43. Algorithm Design Techniques
    Greedy Algorithms in C++
  44. Dynamic Programming in C++
  45. Divide and Conquer in C++
  46. Backtracking in C++
  47. Randomized Algorithms in C++
  48. Conclusion
    Recap of DSA with C++

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This course provided an in-depth look into the core concepts of data structures and algorithms, including various data structures such as arrays, stacks, queues, linked lists, skip lists, hash tables, binary search trees, Cartesian trees, B-trees, red-black trees, splay trees, AVL trees, and KD trees. In addition, learners were exposed to a variety of sorting and searching algorithms, as well as algorithm design techniques such as greedy algorithms, dynamic programming, divide and conquer, backtracking, and randomized algorithms.

Moreover, learners had the opportunity to understand the trade-offs between different data structures and algorithms through the study of Time and Space Complexity analysis, as well as apply their knowledge and practice their skills through a number of hands-on exercises and examples. After completing this course, learners gained the essential knowledge and abilities necessary to become proficient in data structures and algorithms.

This knowledge and understanding of data structures and algorithms can help learners to become better and more efficient developers, especially in the field of computer science and data science. We thank you for taking this course and encourage you to keep exploring and honing your data structures and algorithms skills, as well as continuing to expand your knowledge and grow as a programmer.