Sébastien Thibert

Formation BootCamp >400H, RNCP niv.6 :

• Python
  
• Exploratory Data Analysis (Pandas, Numpy)
  
• Data visualisation (Matplotlib, Seaborn, Bokeh, Plotly)
  
• Machine Learning (Supervised & Unsupervised using Scikit, Optuna)
  
• Deep Learning (Keras, Tensorflow)
  
• Big Data (SQL, PySpark)
  
• Projet (>100H): Computer vision pour la reconnaissance de champignons
  

Voir expérience professionnelle

M2 "Nanotechnologies et nanomatériaux" (spécialisation):
Propriétés et applications des couches minces, des nanotubes de carbone, des nanoparticules synthétisés par voie physique et chimique; synthèse en salle blanche; fondamentaux de la technologie photovoltaïque (1ère, 2ème et 3ème génération, procédés avancés); physique et procédés des semi-conducteurs; guides d’onde; cours et manipulations de microscopie électronique et à champ proche; matériaux pour l’optique; optoélectronique; nano-photonique; Initiation au management de projet; etc.

M1 physique-chimie (tronc commun):
Etudes expérimentales de panneaux solaires; physique du solide et théorie des bandes; physique de l’atmosphère; énergie et environnement; traitement du signal; spectroscopie moléculaire; etc.

Bases de physique: technologie des semi-conducteurs; optique; électronique; cristallographie; mécanique des fluides; électromagnétisme; thermodynamique; etc.
Bases de chimie: chimie organique et inorganique, équilibres en solutions; etc.

-Specialisation Applied Data Science with Python (>50H):
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.
The five courses are: Introduction to Data Science in Python / Applied Plotting, Charting & Data Representation in Applied Machine Learning in Python / Applied Text Mining in Python / Applied Social Network Analysis in Python
(https://www.coursera.org/specializations/data-science-python)

-JMP Statistical Thinking for Industrial Problem Solving (>30H):
A free online statistics course
In virtually every field, deriving insights from data is central to problem solving, innovation and growth. But without an understanding of which approaches to use, and how to interpret and communicate results, the best opportunities will remain undiscovered.
That’s why we created Statistical Thinking for Industrial Problem Solving. This online statistics course is available – for free – to anyone interested in building practical skills in using data to solve problems better.
(https://www.jmp.com/en_gb/online-statistics-course.html)

-Dataquest Data Scientist Path & Python tutorials (>100H):
Learn how to make inferences and predictions from data.
This path covers everything you need to learn to work as a data scientist using Python.
You'll learn the Python fundamentals, dig into data analysis and data viz, query databases with SQL, study statistics, and dig into building machine learning models all over the course of this carefully designed course path.
It's designed so that there are no prerequisites and no prior experience required. Everything you need to learn, you'll learn on this path!
As you learn, you'll apply each concept immediately by writing code right in your browser that's automatically checked by our system to give you near-instant feedback on your progress.
We think the best way to learn is to learn by doing, so you'll be challenged every step of the way to really apply the concepts you're learning, and you'll build a variety of projects using real-world data to solve real data science problems.
By the end of this path, you'll have the skills you need to work as a data scientist, and you'll be comfortable with things like:
How to program in popular data science languages.
How to clean and visualize data.
How to make predictions using statistics and machine learning.
Collaboration tools like git and SQL databases.
(https://www.dataquest.io/path/data-scientist/)

-Méthodologie DMAIC Six Sigma (30H):
Cover the fundamentals for quality engineering and management, including the statistics at a Six-Sigma Green Belt level applied to the DMAIC (Define, Measure, Analyze, Improve, Control) process-improvement cycle. (https://www.edx.org/course/fundamentals-six-sigma-quality-tumx-qemx)

-Gestion de Projet GdP (15H):
Le MOOC Gestion de Projet introduit les apprenants à la gestion de projet :

1. Dans ses fondamentaux : À quoi cela sert-il ? Quels en sont les principaux “points durs” ?
   
2. Dans ses outils : savoir monter un projet, animer une équipe, négocier un objectif, mettre en œuvre la collaboration d’une équipe sur le net...
   À l’issue de cette formation, vous serez capable de concevoir et de piloter un projet. (https://mooc.gestiondeprojet.pm/)
   

-Experimental design and optimization (25H):
In this course, you will learn how to plan efficient experiments - testing with many variables. Our goal is to find the best results using only a few experiments. A key part of the course is how to optimize a system.(https://www.coursera.org/learn/experimentation)

-EPFL Micro and Nanofabrication (MEMS) (20H):
Learn the fundamentals of microfabrication and nanofabrication by using the most effective techniques in a cleanroom environment.(https://www.edx.org/course/micro-nanofabrication-mems-epflx-memsx)

-Statistical Thinking for Data Science and Analytics (30H):
In the four Statistical Thinking modules, we will discuss the data collection, analysis and inference processes, as well as the statistical methods used for association analysis, which are critical to understanding predictive analytics. We will then cover the principles and practices used in exploratory data analysis and visualization, drawing upon several real-world examples from the social sciences, health-care as well as sports.
(https://courses.edx.org/courses/course-v1:ColumbiaX+DS101X+1T2017/course/)