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:
> David AUDENAERT, lecturer in Statistics: david.audenaert@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.
Data Science & 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: Sbébastien Da Veiga / sebastien.da-veiga@ensai.fr
Data Science & Risk Management
In order to assess and measure the risk associated with 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
Data Science & Biostatistics
This specialization at ENSAI prepares students for fascinating careers in biotechnology, pharmaceutical laboratories, and public health.
> Head of the specialization: Matthieu Marbac-Lourdelle / matthieu.marbac-lourdelle@ensai.fr
Data Engineering
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: Cédric Herzet / cedric.herzet@ensai.fr
Data Science & Quantitative Marketing
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 Science, Economic and Health Modeling
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:
> François Portier / francois.portier@ensai.fr