Ce cours permet d’apprendre la statistique à l’aide du logiciel libre R. Le recours aux mathématiques est minimal. L’objectif est de savoir analyser des données, de comprendre ce que l’on fait, et de pouvoir communiquer ses résultats.

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Aug 21st 2017

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The purpose of this course is to review the material covered in the Fundamentals of Engineering (FE) exam to enable the student to pass it. It will be presented in modules corresponding to the FE topics, particularly those in Civil and Mechanical Engineering.

Each module will review main concepts, illustrate them with examples, and provide extensive practice problems.

Syllabus

Week 1: ABOUT THIS COURSE

Week 2: Mathematics

Week 3: Probability and Statistics

Week 4: Statics

Week 5: Mechanics of Materials

Week 6: Fluid Mechanics

Week 7: Hydraulics and Hydrologic Systems

Week 8: Structural Analysis

or

Sep 11th 2017

Ce cours permet d’apprendre la statistique à l’aide du logiciel libre R. Le recours aux mathématiques est minimal. L’objectif est de savoir analyser des données, de comprendre ce que l’on fait, et de pouvoir communiquer ses résultats.

Aug 28th 2017

Este curso provee al estudiante con conceptos y herramientas matemáticas para modelar problemas en física, que al aplicar podrá enfrentar con éxito los cursos de física universitarios. Así pues, la filosofía de este curso consiste en cubrir temas conceptuales relativos a la Física y desarrollar tu capacidad de aprender y aplicarlos en tu vida profesional.

Aug 21st 2017

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance.

Aug 21st 2017

This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.

Aug 21st 2017

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.

Aug 21st 2017

A data product is the production output from a statistical analysis. Data products automate complex analysis tasks or use technology to expand the utility of a data informed model, algorithm or inference. This course covers the basics of creating data products using Shiny, R packages, and interactive graphics. The course will focus on the statistical fundamentals of creating a data product that can be used to tell a story about data to a mass audience.

Aug 21st 2017

Ce cours d'introduction aux probabilités a la même contenu que le cours de tronc commun de première année de l'École polytechnique donné par Sylvie Méléard.

Aug 21st 2017

This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power.

Aug 21st 2017

Ce cours d'introduction aux probabilités a la même contenu que le cours de tronc commun de première année de l'École polytechnique donné par Sylvie Méléard.

Aug 21st 2017

By now you have definitely heard about data science and big data. In this one-week class, we will provide a crash course in what these terms mean and how they play a role in successful organizations. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. We've designed this course to be as convenient as possible without sacrificing any of the essentials.

Aug 21st 2017

Analytic Combinatorics teaches a calculus that enables precise quantitative predictions of large combinatorial structures. This course introduces the symbolic method to derive functional relations among ordinary, exponential, and multivariate generating functions, and methods in complex analysis for deriving accurate asymptotics from the GF equations.

Aug 21st 2017

This course is an introduction to the finite element method as applicable to a range of problems in physics and engineering sciences. The treatment is mathematical, but only for the purpose of clarifying the formulation. The emphasis is on coding up the formulations in a modern, open-source environment that can be expanded to other applications, subsequently.

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