Syllabus
Data Analytics - 12 ECTS
- Machine Learning
Machine Learning
Ects : 6
Lecturer :
Total hours : 36
Overview :
The course gives a thorough presentation of the machine learning field and follows this outline:
- general introduction to machine learning and to its focus on predictive performances (running example: k-nearest neighbours algorithm)
- machine learning as automated program building from examples (running example: decision trees)
- machine learning as optimization:
- empirical risk minimization
- links with maximum likelihood estimation
- surrogate losses and extended machine learning settings
- regularisation and kernel methods (support vector machines)
- reliable estimation of performances:
- over fitting
- split samples
- resampling (leave-one-out, cross-validation and bootstrap)
- ROC curve, AUC and other advanced measures
- combining models:
- ensemble techniques
- bagging and random forests
- boosting
- unsupervised learning:
- clustering (hierarchical clustering, k-means and variants, mixture models, density clustering)
- outlier and anomaly detection
Coefficient : 2 6 (M2 Economie Internationale et Développement) 6 (M2 Diagnostic économique international)
Require prerequisites :
- intermediate level in either Python or R. Students are expected to be able to perform standard data management tasks in Python or R, including, but not limited to:
- loading a data set from a CSV file
- recoding and cleaning the data set
- implementing a simple data exploration strategy based on pivot table and on graphical representation
- intermediate level in statistics and probability. Students are expected to be familiar with:
- descriptive statistics
- conditional probabilities and conditional expectations
- core results from statistics: bias and variance concepts, strong law of large numbers, central limit theorem, etc.
Learning outcomes :
After attending the course the students will
- have a good understanding of the algorithmic and statistical foundations of the main machine learning techniques
- be able to select machine learning techniques adapted to a particular task (exploratory analysis with clustering methods, predictive analysis, etc.)
- be able to design a model selection procedure adapted to a particular task
- report the results of a machine learning project with valid estimation of the performances of their model
Assessment :
- quizzes and tests during the course
- machine learning project
Digital Economics - 18 ECTS
Data Analytics - 12 ECTS
Digital Economics - 9 ECTS
Job Market Insertion - 9 ECTS
Academic Training Year 2025 - 2026 - subject to modification
Teaching Modalities
All courses in the D4E track are taught in English and involve lectures with projects, equipping students with the skills to master data analytics techniques.
The program covers data analytics and digital economics. In data analytics, students explore advanced topics like machine learning and neural networks. In digital economics, they examine blockchain economics and smart contracts. All courses are mandatory, ensuring a comprehensive and cohesive learning experience.
A key feature of the program is the “ Business Cases ” seminars, in which industry professionals present real-world challenges faced by their companies.
They explain how data analytics tools are employed to address these challenges and, whenever possible, provide students with the opportunity to work directly with the data, gaining hands-on experience with industry-relevant problems.
The program concludes with an end-of-study internship, starting in March and lasting a minimum of four months.
Internships and Supervised Projects
The supervised projects include the “ Data Science Project, ” which offers students a unique opportunity to tackle real-world problems through hands-on problem-solving. Guided by two faculty members, students leverage advanced data science tools to analyze and address challenges using real-world datasets.
These projects foster collaboration, critical thinking, and the practical application of the skills acquired during the program, effectively bridging the gap between theoretical concepts and practical implementation.
Theinternship is an essential component of the curriculum, enabling students to apply their knowledge in a professional setting. It concludes with the submission of a written report and an oral defense, both of which are evaluated and contribute to the students’ final assessment.
This process ensures that students not only gain practical experience but also develop their ability to critically reflect on their work and communicate their findings effectively.
Research-driven Programs
Training courses are developed in close collaboration with Dauphine's world-class research programs, which ensure high standards and innovation.
Research is organized around 6 disciplines all centered on the sciences of organizations and decision making.
Learn more about research at Dauphine