Description

This course provides an in-depth study of advanced algorithms and data structures, building on the formalism of Abstract Data Types (ADTs).  It covers fundamental and recursive collections such as stacks, queues, lists, dictionaries, trees, and graphs, along with their concrete implementations using arrays and dynamic data structures. Particular emphasis is placed on hierarchical and dynamic structures, including binary trees and B-trees, as well as graph representations and associated algorithms such as traversal, shortest path (Dijkstra), and A*. The course also introduces advanced techniques such as AVL trees and dynamic programming, illustrated through classical optimization problems like the knapsack problem

Bibliographie

Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to
Algorithms. MIT Press.
• Goodrich, M. T., Tamassia, R., & Goldwasser, M. H. (2014). Data Structures and
Algorithms in Python. Wiley.
• Ramakrishnan, R., & Gehrke, J. (2003). Database Management Systems. McGraw-Hill.
• Loshin, D. (2019). Data Preparation for Data Mining. Morgan Kaufmann.
• McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy,
and IPython. O'Reilly Media.
• Healy, K. (2018). Data Visualization: A Practical Introduction. Princeton University Press.

Contacts

• Cecilia Merk

MCC

Les épreuves indiquées respectent et appliquent le règlement de votre formation, disponible dans l'onglet Documents de la description de la formation.

Régime d'évaluation

ECI (Évaluation continue intégrale)

Coefficient

3.0

Évaluation initiale / Session principale - Épreuves