Description

The course "Introduction to Data Science" provides students with a comprehensive introduction to the field of data science, covering key concepts, methodologies, and techniques used for extracting insights and knowledge from large and complex datasets. Through a combination of theory and practical exercises, students will gain hands-on experience with data manipulation, exploratory data analysis, statistical modeling, machine learning, and data visualization, equipping them with the necessary skills to tackle real-world data-driven problems. 2. Objectifs

Compétences visées

Upon completing this course, students will have acquired the following skills:
• Proficiency in data manipulation, preprocessing, and exploratory data analysis techniques
• Understanding of statistical modeling concepts and their application to real-world problems
• Knowledge of fundamental machine learning algorithms and their implementation
• Familiarity with deep learning principles and techniques
• Competence in using data visualization tools and creating effective visual representations
• Ability to apply data science techniques to solve real-world problems
• Awareness of ethical considerations and data privacy issues in data science

Bibliographie

VanderPlas, J. (2016). Python Data Science Handbook. O'Reilly Media.
• Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning:
Data Mining, Inference, and Prediction. Springer.
• Grus, J. (2019). Data Science from Scratch: First Principles with Python. O'Reilly Media.
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical
Learning. Springer.
• Raschka, S., & Mirjalili, V. (2019). Python Machine Learning. Packt Publishing.
• Chollet, F. (2017). Deep Learning with Python. Manning Publications.
• Few, S. (2012). Show Me the Numbers: Designing Tables and Graphs to Enlighten.
Analytics Press.

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

2.0