Master in Applied Mathematics and Statistics

Focus on Year 1: Applied mathematics and statistics

This first-year Master’s program offers a wide range of basic and more specialized courses in applied mathematics. This allows students to build a personalized curriculum adapted to their academic and professional projects in the following areas:

• Statistics, finance, and actuarial science
• Modeling, probability, and artificial Intelligence
• Optimization
• Signal, computing, and machine learning
• Numerical analysis and EDP

To complete the Master’s degree, first-year students can enter the following second-year programs:

• Master year 2 - Statistics, Finance and Actuarial Sciences
• Master year 2 - Mathematical Modelling
• Master year 2 - Statistics, Finance, and Actuarial Science
• Master year 2 - Probability and Finance

Focus on Year 2 Statistics, Finance and Actuarial Science specialization

Stochastic models are intensively used in financial institutions and insurance companies. In addition to model specifications, accurate statistical methods are crucial for estimation, calibration, and forecasting. This trend has recently been accentuated by the rise of data science and machine learning.

The Statistics, Finance and Actuarial Science Master’s program aims to train high-level specialists in the fields of random modeling and risk management to meet the needs of all types of financial institutions. To achieve this ambitious goal, a strong level in probability and statistics is required. The curriculum is organized around core courses in time series, machine learning, risk management, actuarial science, and mathematical finance.

Focus on Year 2 Data Science specialization

Exploiting today's immense volume of data requires sophisticated mathematical techniques, which form the basis of Data Science. This transition from data to knowledge brings many challenges that require an interdisciplinary approach. Data Science relies heavily on the statistical processing of information: mathematical statistics, numerical statistics, statistical learning and machine learning.

A wide range of mathematical and numerical statistics and learning methods are used from analyzing exploratory data to sophisticated inference techniques (hierarchical graphical models) and classification or regression (deep learning, support vector machine). In order to be developped on a massive scale, these methods require the mastery of data distribution mechanisms and large-scale calculations. Applied mathematics (functional analysis, numerical analysis, convex and non-convex optimization) also plays an essential role.

Year 2: Other specializations

• Mathematical Modelling
• Probability and Finance

Career

Students from the Statistics, finance, and actuarial science specialization will be equipped to pursue a career requiring random modelling and risk management for all types of financial institutions including banks, insurance companies, asset managers and hedge funds.

Students from Data Science specialization are eagerly awaited on the global job market.