Statistics and Data Science major
Master 2
3rd Semester
Shared courses
Organisation: Lectures (12h)
Coordinator: Bianca Habermann, Alphée Michelot, Elisabeth Rémy
Evaluation: project and continuous monitoring
Following [PRO2] seminars, the students will attend all the Centuri seminars of this semester. For two of them, they will be asked to broaden their knowledges on the subject and present an oral and written synthese.
Choose 2 courses between 3 courses:
- Developmental Biology
- Neurosciences
- Immunology
COURSE 1: Developmental Biology
Organisation: Lectures (6h), TD (6h) TP( 6h)
Lecturers : Thomas Lecuit , Florence Hubert
Evaluation: project
Fundamentals of morphogenesis: molecular, cellular and biophysical basis of tissue forms in animals and plants. The mechanical and biochemical basis of morphogenesis will be addressed to understand the origin of cell and tissue organization. This course is an introduction to the modeling of the emergence of spatial and temporal patterns in morphogenesis and to the description and understanding of Turing instabilities.
COURSE 2: Neurosciences
Organisation: Lectures (6h), TD (6h) TP( 6h)
Lecturers : Claudio Riviera
Evaluation: final exam
This course covers basic principle in neuroscience. During the lectures special emphasis will be placed on the mechanism of synapse formation and plasticity, functional network maturation, pathophysiology of the brain and role of glia-neuron interaction in network dynamics. Also an introduction to computational methods for analysis and modelling of neurobiological data will be presented. During the TD the students will be challenged in the form of a project where acquired knowledge in mathematical and computational biology will be used to solve specific problems in neuroscience.
COURSE 3: Immunology
Organisation: Lectures (6h), TD (6h) TP( 6h)
Lecturers : Guillaume Voisine
Evaluation: final exam
The course will focus on the different aspects of immunology as approached by physicists. In particular, the following will be studied: Discrimination of antigens by T cells, communication between cells with cytokines and finally a part on differentiation.
Organisation: project during the semester
Coordinator: Florence Hubert, Laurence Röder
Evaluation: project
At the end of the courses [PRO1], and [BIO1], students will choose a scientific article at the interface of several disciplines on which they will work in groups. They will have to present in a memory and an oral presentation, to explain the biological context and the related basic concepts, to explain the methods used to interpret the biological data, to synthesize the results obtained in the article.
Organisation: Lectures (6h), TD (6h) TP (6h)
Lecturer: Pierre Pudlo
Evaluation: continuous monitoring and project.
This course is an introduction to inferential statistics. It will be illustrated with biogical examples. The course will be composed by three parts
- Multiple tests
- Classification
- Time series analysis
Organisation: Lectures (10h), TD (8h), TP (12h)
Coordinator: Anais Baudot
Lecturer: Anais Baudot, Elisabeth Rémy, Aitoir Gonzalez, Anthony Batista
This module will introduce the bases of biological network analysis, from graph theory and algorithms to dynamical modeling. A large part of the module is dedicated to hands-on tutorials, which introduces R and Python packages, as well as softwares widely used in Network Biology, such as Cytoscape and GinSim.
Organisation: project
Coordinator: Florence Hubert, Laurence Röder
Evaluation: project
Following the module [PROJ1], the students will do a short internship in laboratory. They will have to propose a modelling or data processing problem at the math-info-bio interface. They will be asked to synthesize their results in a dissertation and an oral presentation.
Statistics and Data Science courses
Organisation: Lectures (6h), TD (6h), TP (6h)
Lecturers: Guillemette Chapuisat, Assia Benabdallah
Evaluation: final exam and projects
We will study optimization tools in finite and infinite dimension (optimal control theory) as well as their numerical implementation using Python. We will apply these different tools to a chemotherapy optimization problem for an in vitro model of heterogeneous tumor growth, i.e. we will look for the best way to administer a chemotherapy to optimize the effect on a cancer cell culture mixing sensitive and resistant cells to chemotherapy. We will base our work on experiments conducted at the Faculty of Pharmacy of Timone.
Big Data
Organisation: Lectures (12h), TD (12h)
Lecturers: Pierre Pudlo
Evaluation: continuous exam
The purpose of this course is to present some fundamental algorithms in data processing linked to the so-called "big data", based on re-sampling or random permutations of the data. At the end of the course, the student must have understood and know how to implement the bootstrap procedure, including in complicated situations: regression, survival analysis, post-selection model inference. He will also learn how to implement a multiple test procedure, and how to use permutation methods in this setting. The outline of the course will thus be:
- Introduction
- Bootstrap
- Selection of variables / models
- Survival analysis
- Multiple tests
Organisation: Lectures (12h), TD (12h)
Lecturer: Frédérique Richard
Evaluation: continuous exam
A representation of a data vector is said to be parsimonious if it is possible to find a generating system or even a base in which the vector can be described or approximated by a linear combination of a small number of elements. In this course, we will begin by presenting the classical bases and transformations in which certain types of data are naturally parsimonious. We will show the utility of parsimonious decomposition in the case of classical problems in signal processing (denoising, compression ...). Finally, we will present and study several algorithms of such parsimonious decomposition.
The outline of the course is thus the following:
- Classic basics and landmarks for parsimonious representation of signals
- Applications to concrete problems: denoising, compression, parsimonious regression
- Algorithms for the parsimonious representation of signals