Training – Algorithmics (Complete Pack)

Training Objective
Provide participants with a solid foundation in algorithmics, enabling them to design efficient algorithms, analyze computational complexity, and solve problems systematically using appropriate methods and data structures.

📍 General Organization
⏳ Duration: 80–120 hours (depending on pace and practice)
👨‍🏫 Teaching Methods: Theoretical lessons, coding exercises, workshops, case studies, and final project
🎯 Target Audience: Computer science students, junior developers, engineers, data science learners, and professionals seeking to improve problem-solving skills

🗂️ Detailed Program

🔹 Module 1: Introduction to Algorithmics

• Definition of an algorithm

• Properties: finiteness, clarity, input/output, efficiency

• Algorithm vs program

• Introduction to complexity

🔹 Module 2: Control Structures

• Sequential, conditional, and iterative structures

• Basics of pseudocode

• Example: factorial, simple algorithms

🔹 Module 3: Basic Data Structures

• Variables, constants, and data types

• Arrays (1D and 2D)

• Basic operations: search, insert, delete

🔹 Module 4: Algorithm Design Techniques

• Divide and conquer (binary search, merge sort)

• Recursion (factorial, Fibonacci)

• Greedy methods (coin change, scheduling)

• Dynamic programming (knapsack, shortest paths)

🔹 Module 5: Complexity Analysis

• Time complexity: O(n), O(log n), O(n²), etc.

• Space complexity

• Comparing algorithms

🔹 Module 6: Sorting Algorithms

• Basic sorts: selection, insertion, bubble sort

• Efficient sorts: quick sort, merge sort

• Complexity and performance comparison

🔹 Module 7: Searching Algorithms

• Linear search

• Binary search

• Practical applications

🔹 Module 8: Advanced Data Structures

• Linked lists

• Stacks (LIFO) and queues (FIFO)

• Trees (binary, binary search trees)

• Graphs (directed, undirected, weighted)

🔹 Module 9: Graph Algorithms

• Breadth-first search (BFS)

• Depth-first search (DFS)

• Shortest path algorithms: Dijkstra, Bellman-Ford

• Minimum spanning tree: Kruskal, Prim

🔹 Module 10: Final Project

• Real-world computational case study

• Design and implement an efficient algorithm

• Analyze complexity and efficiency

• Final presentation and evaluation before a jury

Methodology
✅ Interactive lectures with theory + examples
✅ Hands-on coding practice in pseudocode and Python
✅ Workshops and problem-solving sessions
✅ Case studies and group work
✅ Personalized feedback and final project defense

Expected Outcomes
By the end of this training, participants will be able to:
✔️ Understand core algorithmic principles
✔️ Design and implement efficient algorithms
✔️ Analyze algorithm complexity in time and memory
✔️ Apply sorting, searching, and graph algorithms effectively
✔️ Select the right data structures for different problems
✔️ Use algorithmics knowledge in software engineering, AI, and data science