Ensai is a multidisciplinary school primarily focused on mathematics, computer science, and economics. It provides a rich and diverse education in its various fields and recruits different profiles of students, including those from preparatory classes for major schools (MP track and more recently PC and PSI tracks) or after D2 degrees or B/L preparatory classes.

We will briefly detail the courses and various study opportunities in the upcoming paragraphs, but we highly recommend that you visit our school’s website right here.

First year

In the first year, students at Ensai acquire fundamentals across various fields. This means that first-year internships enable them to apply their new knowledge over a period of 1 to 3 months during the summer. Here is the document that details the courses in the first year for each semester. The main points of interest are:

  • Mathematics and statistics

This involves various aspects such as an introduction to inferential statistics and statistical testing, an in-depth approach to measurement theories and general probability theories, as well as work on mathematical optimization. Most of the aforementioned points also involve computer applications.

  • Economics

Fundamentals of microeconomics and its mathematical modeling are covered in the first year, including the study of consumers and producers, Pareto optima, and others. Subsequently, macroeconomic dynamics are studied along with all the major economic aggregates such as inflation, unemployment, or the evolution of GDP.

  • Computer science

All first-year students at Ensai study R, SAS, and Python programming. Various methods are explored in depth: factor analysis methods (PCA, MCA…), clustering, object-oriented programming, and general statistical studies using R and SAS.

In addition to this, there are three group projects: a statistical project, an economics project, and a computer science project that allow students to concretely apply the knowledge acquired in the first year. Thus, a first-year student is capable of conducting an in-depth statistical study on a database, modeling phenomena, and confronting them with economic reality.

Second year

The second year is dedicated to deepening the statistical methods already learned. Additionally, advanced statistical concepts are studied with practical applications. During this year, projects are also carried out to apply the new methods. Here is a document that details all the second-year courses. At the end of their second year, students must complete an internship lasting 2 to 3 months during the summer to apply the new methods learned during the year.

  • Mathematics and statistics

During this year, methods of linear regression, survey theory, and time series analysis are studied. Additionally, second-year students have the option to study Markov chains or delve deeper into time series analysis, but they also have the opportunity to explore Bayesian statistics.

  • Economics

Econometrics is studied along with a deeper exploration of microeconomic modeling. Additionally, students have the opportunity to take courses in microeconometrics, applied macroeconometrics, as well as risk economics, and digital economics.

  • Computer science

In the second year at Ensai, the major project is in computer science. Additional computer science courses are provided. Furthermore, students can choose between studying object-oriented programming in Java or C++ programming for computational statistics. The use of tools for Big Data is also covered in a course. After studying unsupervised learning in the first year, students are introduced to supervised learning techniques (k-nearest neighbors, CART trees, discriminant analysis, etc.).

The second year is a pivotal time for students as they must choose among various courses in computer science, mathematics, and economics. For instance, they can study risk economics, demography, software design, advanced programming with R, martingales, and Lévy processes, among others.

For engineering students, this year is particularly important to prepare for their third year of specialization. Similar to the first year, a statistical project is conducted in addition to the computer science project. This structure ensures that students not only deepen their technical knowledge but also continue to develop a practical understanding of how to apply these techniques in real-world scenarios.

Third year

The third year at Ensai is a specialization year for engineering students. Each student can choose from six tracks. At the end of the year, they must complete an end-of-study internship lasting between 5 and 6 months, depending on the specialization they have chosen. The tracks are:

  • Data Science & Statistical Engineering: Ensai trains experts in modeling, covering various fields ranging from quality-reliability for industry to environmental forecasting, including image and signal processing.
  • Data Science & Risk Management: To properly assess and measure the risk associated with different operations, banks require experts who master both banking regulations and advanced quantitative techniques. Developing effective quantitative tools for managing financial savings is also essential: risk management and asset management are at the heart of this specialization.
  • Data Science & Health and Biostatistics: This specialization in biostatistics opens career paths in major pharmaceutical laboratories, biotechnology, and public health.
  • Data Science & Data Engineering: Students gain a strong computing culture combined with their initial statistical training. Experts in Big Data environments, they have sufficient knowledge in systems architecture, networks, and computer security to handle very large volumes of data.
  • Data Science & Marketing: Engineers in this specialization benefit from a high-value-added marketing culture (marketing mix, experiential marketing, digital marketing, customer relationship management) which enables them to extract and analyze data to understand and explain, but most importantly, to predict purchasing behaviors for products and services.
  • Data Science & Economic Modeling and Health: This track provides a background in statistical engineering, economics, and applied econometrics focusing on the dynamics of territories and health, enabling the evaluation of public policies as well as private programs.

Civil servant students (or attachés) have the option to conclude their studies in the second year but are strongly encouraged to pursue the master’s degree “Data Science for Public Decision Making,” which is the choice of the majority of them.

In addition to all the courses offered in mathematics, economics, and computer science, students at Ensai have the option during their studies to take “humanities courses.” These courses aim to expose students to subjects beyond numbers and to develop other skills. Mandatory English courses complete the curriculum, along with elective enrichment courses ranging from learning a new language (Italian, Spanish, German, Chinese, Russian, etc.) to courses in art, social sciences, and geopolitics. This holistic approach ensures that students not only excel technically but also develop a well-rounded perspective necessary for understanding complex global issues.