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.