Supports de cours -- Data Mining Cette page recense les supports utilisés pour mes enseignements de Machine Learning, Data Mining et de Data Science au sein du Département Informatique et Statistique (DIS) de l'Université Lyon 2, principalement en Master 2 Statistique et Informatique pour la Science des donnéEs (SISE), formation en data science, dans le cadre du traitement statistique des données et de la valorisation des big data. Je suis très attentif à la synergie forte entre l'informatique et les statistiques dans ce diplôme, ce sont là les piliers essentiels du métier de data scientist. Attention, pour la majorité, il s'agit de « slides » imprimés en PDF, donc très peu formalisés, ils mettent avant tout l'accent sur le fil directeur du domaine étudié et recensent les points importants. Cette page est bien entendu ouverte à tous les statisticiens, data miner et data scientist, étudiants ou pas, de l'Université Lyon 2 ou d'ailleurs. Nous vous remercions par avance. Ricco Rakotomalala – Université Lyon 2
Factorial Analysis of Variance Factorial Analysis of Variance (ANOVA) One-way ANOVAs only allow us to examine one source of variance (one factor). There are situations (lots of situation) where we are interested in examine more than one source of variance. We will now examine 2 or more independent variables (or factors) on a single dependent variable. One-way ANOVA = 1 IV Two-way ANOVA = 2 IV (factorial ANOVA) Three-way ANOVA = 3 IV (factorial ANOVA) etc. When we covered research designs, we usually used X (treatment) and O (measure) to illustrate the design. What do the numbers (e.g., 3 X 2) mean? Factors can be assigned or active. Activity Design three research questions that would require a two-way ANOVA to analyze the data. Why not run 2 one-way ANOVAs? Ordinal Interaction (lines are not parallel) Disordinal Interaction (lines cross) but lines do not have to cross to be considered an interaction. The following graphic illustration are from Dr. Effects may be depicted graphically. Null Hypotheses 1. [data] Profile Plots
Top 10 data mining algorithms in plain English Today, I’m going to explain in plain English the top 10 most influential data mining algorithms as voted on by 3 separate panels in this survey paper. Once you know what they are, how they work, what they do and where you can find them, my hope is you’ll have this blog post as a springboard to learn even more about data mining. What are we waiting for? Let’s get started! Update 16-May-2015: Thanks to Yuval Merhav and Oliver Keyes for their suggestions which I’ve incorporated into the post. Update 28-May-2015: Thanks to Dan Steinberg (yes, the CART expert!) What does it do? Wait, what’s a classifier? What’s an example of this? Now: Given these attributes, we want to predict whether the patient will get cancer. And here’s the deal: Using a set of patient attributes and the patient’s corresponding class, C4.5 constructs a decision tree that can predict the class for new patients based on their attributes. Cool, so what’s a decision tree? The bottomline is: Is this supervised or unsupervised? 3.
Réaliser une étude qualitative : définitions et techniques Définition de l'étude qualitative Sa grande sœur, l'enquête quantitative, prend tout le devant de la scène. Pourtant l'étude qualitative est très employée pour : s'approprier un sujet, explorer un univers, saisir ce qui le caractérise, évaluer, pondérer et comprendre des besoins, des comportements, des attitudes de consommation, récolter suffisamment d'informations avant de lancer une opération d'analyse de grande envergure. Elle répond à la question "Pourquoi ?". Ses outils de prédilection : l'entretien individuel et la réunion de groupe. L'étude qualitative permet d'explorer l'univers d'un produit ou d'un service, sa perception par les consommateurs, les valeurs associées. Constitution de l'échantillon Contrairement une recherche quantitative, l'échantillon d'un "quali" n'est pas représentatif d'une population, mais respecte cependant sa structure. Les objectifs d'une enquête qualitative Elle peut être utilisée seule ou en complément d'autres types d'études. Utilisée seule :
Sipina - Arbres de décision Venn Diagram Plotter | Pan-Omics Research Acknowledgment All publications that utilize this software should provide appropriate acknowledgement to PNNL and the OMICS.PNL.GOV website. However, if the software is extended or modified, then any subsequent publications should include a more extensive statement, as shown in the Readme file for the given application or on the website that more fully describes the application. Disclaimer These programs are primarily designed to run on Windows machines. Please use them at your own risk. Portions of this research were supported by the NIH National Center for Research Resources (Grant RR018522), the W.R. We would like your feedback about the usefulness of the tools and information provided by the Resource.
Scheduling in Hadoop Hadoop is a general-purpose system that enables high-performance processing of data over a set of distributed nodes. But within this definition is the fact that Hadoop is a multi-tasking system that can process multiple data sets for multiple jobs for multiple users at the same time. This capability of multi-processing means that Hadoop has the opportunity to more optimally map jobs to resources in a way that optimizes their use. Up until 2008, Hadoop supported a single scheduler that was intermixed with the JobTracker logic. Luckily, a bug report (HADOOP-3412) was submitted for an implementation of a scheduler that was independent of the JobTracker. With this change, Hadoop is now a multi-user data warehouse that supports a variety of different types of processing jobs, with a pluggable scheduler framework providing greater control. Note: This article assumes some knowledge of Hadoop. The core Hadoop architecture Figure 1. Back to top Hadoop schedulers FIFO scheduler Fair scheduler
Créer une base de données sous Excel La Société par Actions Simplifiée Purch (ci-après « PURCH »), dont le siège est situé 8 rue de l'Hôtel de Ville, 92200 Neuilly-sur-Seine, au capital social de 501.411,05 euros, enregistrée au RCS de Nanterre sous le numéro 424 382 026, édite un site d'informations dans le domaine des nouvelles technologies, www.tomsguide.fr (ci-après le « Site »), proposant un accès à différentes informations et espaces de discussions, tel que détaillé ci-après. Les présentes conditions générales d'utilisation (ci-après les "CGU"), soumises au droit français, ont vocation à régir l’accès et l'utilisation du Site par toute personne y accédant (ci-après l’« Utilisateur »), quel que soit le lieu où il se trouve et les modalités de connexion au Site. « Contenu » désigne la structure du Site ainsi que son contenu (notamment textes, images fixes ou animées, vidéos, bases de données, programmes, marques, logo, et tous les autres éléments composant le Site, à l’exception des Contributions). Accès au Forum a. b.
Web Squared Journal Observations About Streaming Data Analytics for Science | The eScience Cloud I recently had the pleasure of attending two excellent workshops on the topic of streaming data analytics and science. A goal of the workshops was to understand the state of the art of “big data” streaming applications in scientific research and, if possible, identify common themes and challenges. Called Stream2015 and Stream2016, these meetings were organized by Geoffrey Fox, Lavanya Ramakrishnan and Shantenu Jha. First it is important to understand what we mean by streaming data analytics and why it has become so important. In some cases, the volume and rate of generation is so large, we cannot keep the data at rest for very long. This article has two parts. There are many factors that determine when a particular technology is appropriate for a particular problem. We can divide the spectrum of streaming data scenarios into three basic categories The data streaming challenges that confront large enterprises when dealing the data from millions of users of Internet enabled devices.
Méthodes de Recherche Quantitatives - M@n@gement Vol21 - 1 Better, faster, stronger, the impact of market oriented coopetition on product commercial performance Paul Chiambaretto, Frédéric Le Roy, Benjamin Mira, Marc Robert. Pages : 574-610 Résumé This paper examines the specific impacts of market-oriented coopetition on product commercial performance. Download PDF (EN) Quantitative Methods - Strategy & Business Policy Vol20 - 3 Does strategy formalization foster innovation? Marc Fréchet, Hervé Goy. Pages : 266-286 Despite abundant research, the relationship between strategy formalization and innovation remains unclear. Entrepreneurship - Innovation & Technology - Quantitative Methods - Strategy & Business Policy Vol20 - 5 The globalization of research highlighted through the research networks of management education institutions: the case of French business schools Sébastien Dubois, Isabelle Walsh. Pages : 435-462 Research has become a key success factor for academic institutions in a growing and increasingly globalized market. Pages : 463-491
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