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CS-401: Applied data analysis

This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the data science world (pandas, scikit-learn, Spark, etc.) ...

Enseignant·e·s (2)

Maria Brbic

Maria Brbic is an assistant professor in computer science at EPFL. Prior to joining EPFL, Maria was a postdoctoral researcher in Computer Science at Stanford University working with Jure Leskovec. She received her PhD degree from University of Zagreb in 2019, while also researching at Stanford University and University of Tokyo. Her research was awarded with the Fulbright Scholarship, L’Oreal UNESCO for Women in Science Scholarship, Branimir Jernej award for outstanding publication in biology and biomedicine, and Josip Loncar Silver Plaque award for the best doctoral dissertation. She has been named a Rising Star in EECS by MIT in 2021. Her research is focused on developing new machine learning methods and applying her methods to advance biomedical research.

Robert West

Robert West is a tenure-track assistant professor of computer science at EPFL, where he heads the Data Science Lab. In his research, he develops and applies techniques in machine learning, computational social science, natural language processing, social network analysis, and data mining. Bob also collaborates closely with the Wikimedia Foundation, in his role as a Wikimedia Research Fellow. Bob’s work has won several awards, including best/outstanding paper awards at ICWSM’21, ICWSM’19, and WWW’13, a best-paper runner-up award at WWW’16, a Google Faculty Research Award, a Facebook Research Award, a Hewlett-Packard Graduate Fellowship, and a Facebook Graduate Fellowship. He is actively involved in the research community, e.g., as an Associate Editor of ICWSM and EPJ Data Science and as a co-founder of the Wiki Workshop (held at WWW and ICWSM) and the Applied Machine Learning Days. Bob received his PhD in Computer Science from Stanford University, his MSc from McGill University, Canada, and his undergraduate degree from Technische Universität München, Germany.[Last updated: 25 Aug 2021]

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Concepts associés (32)

Propensity score matching

In the statistical analysis of observational data, propensity score matching (PSM) is a statistical matching technique that attempts to estimate the effect of a treatment, policy, or other intervention by accounting for the covariates that predict receiving the treatment. PSM attempts to reduce the bias due to confounding variables that could be found in an estimate of the treatment effect obtained from simply comparing outcomes among units that received the treatment versus those that did not. Paul R.

Méthode expérimentale

Les méthodes expérimentales scientifiques consistent à tester la validité d'une hypothèse, en reproduisant un phénomène (souvent en laboratoire) et en faisant varier un paramètre. Le paramètre que l'on fait varier est impliqué dans l'hypothèse. Le résultat de l'expérience valide ou non l'hypothèse. La démarche expérimentale est appliquée dans les recherches dans des sciences telles que, par exemple, la biologie, la physique, la chimie, l'informatique, la psychologie, ou encore l'archéologie.

Average treatment effect

The average treatment effect (ATE) is a measure used to compare treatments (or interventions) in randomized experiments, evaluation of policy interventions, and medical trials. The ATE measures the difference in mean (average) outcomes between units assigned to the treatment and units assigned to the control. In a randomized trial (i.e., an experimental study), the average treatment effect can be estimated from a sample using a comparison in mean outcomes for treated and untreated units.

Random assignment

Random assignment or random placement is an experimental technique for assigning human participants or animal subjects to different groups in an experiment (e.g., a treatment group versus a control group) using randomization, such as by a chance procedure (e.g., flipping a coin) or a random number generator. This ensures that each participant or subject has an equal chance of being placed in any group. Random assignment of participants helps to ensure that any differences between and within the groups are not systematic at the outset of the experiment.

Épuration des eaux

thumb|Station d'épuration des eaux à Aguas Corrientes, en Uruguay. L’épuration des eaux est un ensemble de techniques qui consistent à purifier l'eau soit pour réutiliser ou recycler les eaux usées dans le milieu naturel, soit pour transformer les eaux naturelles en eau potable. La fin du marque l'essor des réseaux d'égouttage et d'assainissement en France (courant hygiéniste, rénovation de Paris du baron Haussman). Il s'agit d'éloigner les eaux usées des habitations et des lieux de vie.

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Séances de cours associées (32)

Analyse causale des données d'observation

Couvre l'analyse causale des données d'observation, des pièges, des outils permettant de tirer des conclusions valables et d'aborder les variables confusionnelles.

Études d'observation : pièges et solutions

Explore les défis des études observationnelles, en soulignant l'importance de la randomisation et de l'analyse de sensibilité pour tirer des conclusions valables à partir de «données trouvées».

Études d'observation : pièges et conclusions valides

Souligne les pièges des études d'observation et l'importance des expériences randomisées.

Études d'observation : pièges et solutions

Couvre les pièges des études d'observation, les solutions pour éviter les biais, et l'importance des conclusions valables des « données trouvées ».

Études d'observation : pièges et solutions

Explore les études d'observation, les pièges, les solutions et l'importance de conclusions valides dans les projets de recherche.

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Concepts associés (32)

Propensity score matching

Méthode expérimentale

Average treatment effect

Random assignment

Épuration des eaux

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