Information Covid Research

Lead a Project

Second-Year Project (M1)

ENSAI calls upon statistical practitioners to tutor its second-year projects. The pedagogical objective of this project is to allow students to apply the skills learned during the first two year of study and give them the opportunity to go further in confronting a real problem.

Student Expectations

Students work in groups of 3 or 4 on topics they have chosen. They approach the subject using concepts, methodologies, and tools which are adapted to it. Each group must write a report and present their findings via an oral defense. Students are expected to work around 4 hours per week on the project. More work would be inappropriate due to their other responsibilities and courses.

Statistical Content of the Project

To address the problem, student must use their knowledge in Time Series, Sampling Theory, Data Analysis, Linear Regression, Categorical Variable Regression, Econometrics, and more. Other tools may be used, but the tutors must take sufficient time to explain them to students.

The Role of the Tutor

The tutor must provide:
– a topic with an unresolved issue;
– one or more data sets of reasonable size with adequate documentation (if necessary);
– a relevant bibliography to help students contextualize the problem and discuss the projects results in the final report.

Project tutors must have good knowledge of the data sets they provide to students and mastery of the basic statistical techniques necessary for their analysis.

Calendar

October – November: submission of project topics (with the data sets) to ENSAI for validation
December: students form groups and choose topics
End of January: in-person meeting between students and tutors to begin the project
February – April: project tutoring (4 meetings minimum) in person or via videoconference
End April / Early May: students turn in written reports
Mid-May: projects are defended in front of an evaluation panel made up of a President, and ENSAI professor, an expert in Communication, and the tutor

Access to Data

The totality of data necessary for the project must be available from the beginning of the project. The size of databases is limited to 100 MB (some flexibility is possible if tutors judge that this size is insufficient for a given project). The use of SAS or R is mandatory.

Confidentiality

Data will be stocked in a secure manner to ensure that only the students in the group and the tutor will have access. All students sign a confidentiality agreement.

Payment

Tutoring a project is paid in a lump sum. Costs associated with travel are borne by ENSAI.

Would you like to offer or tutor one or several projects?

In one page maximum, present:

  • the theme of the study
  • the issue(s) at hand
  • the type of data
  • some bibliographical information

Send your document to:
> Khaled LARBI, lecturer in Statistics: khaled.larbi@ensai.fr

End of Studies Project (M2)

All 6 specialization programs offer the possibility of tutoring projects. The formats and calendars vary from one program to another.

> Please contact directly the Head of the specialization for tutoring a project.

Advanced Statistical Engineering

This specialization offers a large range of employment opportunities. ENSAI students will become skilled modeling experts in many fields of application such as quality/reliability in business, environmental predictions, and signal and image processing.

> Head of the specialization: Salima El Kolei /Salima.el-kolei@ensai.fr

Risk Management and Financial Engineering

In order to asses and measure the risk associated to different financial operations, banks need the support of experts in banking regulations and advanced quantitative techniques. As risk and asset management are at the heart of this ENSAI specialization students will learn how to build quantitative tools to manage financial savings.

> Head of the specialization: Samuel Danthine / samuel.danthine@ensai.fr

Biostatistics

This specialization at ENSAI prepares students for fascinating careers in biotechnology, pharmaceutical laboratories, and public health.

> Head of the specialization: Lionel Truquet / lionel.truquet@ensai.fr

Advanced Tools for Data Science & Machine Learning

This ENSAI specialization combines computer science with statistics. Data Scientist students will become experts in Big Data, learn about systems architecture, networking, and cybersecurity in order to handle large volumes of data.

> Head of the specialization: Mohamed Graiet / mohamed.graiet@ensai.fr

Quantitative Marketing and Revenue Management

ENSAI engineering students in this specialization will gain a strong background in marketing culture (marketing mix, experience marketing, digital marketing, customer relationship management). This will enable students to extract and analyze data in order to understand and explain it, as well as predict purchasing behavior of products and services.

> Head of the specialization: Basile Deloynes / basile.deloynes@ensai.fr

Data Modeling for Spatial Analysis and Health Economics

This ENSAI program offers a strong background in statistical engineering, economics, econometrics applied to territory dynamics and health, and will enable statistical engineers to evaluate both public policies and projects in the private sector.

> Head of the specialization: Samuel Danthine / samuel.danthine@ensai.fr

Master Smart Data (M2)

International Master Statistics for Smart Data offer the possibility of tutoring projects (in english).

Please, contact directly the Head of the Master for tutoring a project:

> Valentin Patilea /valentin.patilea@ensai.fr