What Big Data, Data Science, Deep Learning software goes together? We analyze the associations between top Data Science tools, Commercial vs Free/Open Source, rank tools on R vs Python bias, find tools more associated with Big Data, those more associated with Deep Learning, and uncover strong regional differences. Last week, I reported the results of 2016 KDnuggets Software Poll: R, Python Duel As Top Analytics, Data Science software. This post looks a little deeper and examines the associations between different tools, their relationship to Big Data and Deep Learning, and regional patterns. At the end of the post there is a link to anonymized dataset, so that you can do your own analysis (and let me know about the results in comments below). The question asked in KDnuggets Poll was What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? First, we looked at associations between the top 10 tools. Lift (X & Y) = pct (X & Y) / ( pct (X) * pct (Y) ) where pct(X) is the percent of users who selected X.
Free Data Science Courses | Data Science Academy Free Data Science Courses The Little List of Free #DataScience Courses Free Online Data Science Courses & Data Science Training Click on the free data science courses links below: The Open Source Data Science Masters Harvard CS109 Data Science Introduction to Data Science by Jeff Hammerbacher at UC, Berkeley Introduction to Data Science @coursera Introduction to Data Science @UofWashington Data Science Course @ColumbiaUni notes by @mathbabe An Introduction to Data Science at Syracuse University ( pdf) Applied Data Science: An Introduction @SyracuseUni Data Science and Analytics at UCBerkeley Process Mining: Data Science in Action @TUEindhoven Learning from Data at California Institute of Technology Statistical Thinking and Data Analysis @MIT Data Analysis and Statistical Inference @DukeUni Introduction to Data Mining @MIT Mining Massive Datasets @Stanford Pattern Discovery in Data Mining @UoIllinois Introduction to Data Wrangling at the School of Data Making Sense of Data @Google Openintro to Statistics
Cheat Sheet - 10 Machine Learning Algorithms & R Commands - Bytes Cravings This article lists down 10 popular machine learning algorithms and related R commands (& package information) that could be used to create respective models. The objective is to represent a quick reference page for beginners/intermediate level R programmers who working on machine learning related problems. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos. Following are the different ML algorithms included in this article:Linear regressionLogistic RegressionK-Means ClusteringK-Nearest Neighbors (KNN) ClassificationNaive Bayes ClassificationDecison TreesSupport Vector Machine (SVM)Artifical Neural Network (ANN)AprioriAdaBoost Cheat Sheet – ML Algorithms & R Commands Linear regression: “lm” method from base package could be used for linear regression models. For most of the above formulas including linear regression model, one could use following function to predict: Ajitesh Kumar
TraffickCam | Web App 12 Statistical and Machine Learning Methods that Every Data Scientist Should Know Below is my personal list of statistical and machine learning methods that every data scientist should know in 2016. Statistical Hypothesis Testing (t-test, chi-squared test & ANOVA)Multiple Regression (Linear Models)General Linear Models (GLM: Logistic Regression, Poisson Regression)Random ForestXgboost (eXtreme Gradient Boosted Trees)Deep LearningBayesian Modeling with MCMCword2vecK-means ClusteringGraph Theory & Network Analysis(A1) Latent Dirichlet Allocation & Topic Modeling(A2) Factorization (SVD, NMF) From my experience in the data science industry for 4 years, I think that currently these 12 methods are the most popular, useful and suitable for various problems requiring data science. As far as I've known, there have been not a few lists of "representative methods in data science" ever. In addition to the list itself, I showed R or Python scripts of an experiment on sample datasets for each method, in order to enable readers to try it easily.
Herramientas Digitales para el Periodismo de Datos Sandra Crucianelli, becaria del Knight International Journalism Fellow –ICFJ (International Center For Journalist), creó el primer equipo de periodismo de investigación el cuál hizo seguimiento a los ingresos fiscales asignados a los servicios públicos de la Argentina. Es especialista en periodismo digital de investigación y periodismo de datos. Fundadora de www.SoloLocal.Info, revista digital que produce noticias locales en la ciudad Bahía Blanca, Argentina y ha sido instructora y asesora para el Centro Knight de Periodismo en las Américas en la Universidad de Texas, Austin, desde el 2004. También, Crucianelli, es instructor para el Centro Internacional de Medios de la Universidad Internacional de Florida y autora del libro “Digital Tools for Journalist” (Herramientas Digitales para Periodistas), en español y en portugues.
14 Great Machine Learning, Data Science, R , DataViz Cheat Sheets Data Science Central 14 Great Machine Learning, Data Science, R , DataViz Cheat Sheets by Laetitia Van Cauwenberge Oct 11, 2015 Your best references to do your job or get started in data science. Click here for picture source DSC Resources Additional Reading Follow us on Twitter: @DataScienceCtrl | @AnalyticBridge Vincent Granville My data science cheat sheet is, I believe, the first one to have been published. 5 members like this loading
AGRIMONITOR: Agricultural policy, food security and climate change As a consumer, for sure you know the price of the food that you consume daily. However, do you know why your food has this price and why it is different from the one that is has in other countries? Well, agricultural public policies are the ones that determine, among other factors, the price you have to pay for your food. In fact, did you know that these policies also impact in food security and climate change? Do not miss the chance to learn how to analyze agricultural policies in Latin-America and the Caribbean, to learn their implications in food security and to understand their close connection with the environment and climate change. You will learn all this with ‘AGRIMONITOR’: a database created by IDB, that contains information about 18 countries in Latin-America and the Caribbean. Furthermore, if you obtain 90 points over 100 in the course, you will have the chance to participate in a competition, which will have two winners. AGRIMONITOR is waiting for you!
k-nearest neighbors algorithm - Wikipedia Non-parametric classification method In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges in 1951,[1] and later expanded by Thomas Cover.[2] It is used for classification and regression. In both cases, the input consists of the k closest training examples in a data set. The output depends on whether k-NN is used for classification or regression: In k-NN classification, the output is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). Both for classification and regression, a useful technique can be to assign weights to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. A peculiarity of the k-NN algorithm is that it is sensitive to the local structure of the data.
Observatorio de Transparencia y Anticorrupción <div class='noindex'>Puede estar intentando tener acceso a este sitio desde un explorador protegido en el servidor. Habilite los scripts y vuelva a cargar la página.</div> Activar el modo de accesibilidad Omitir los comandos de cinta Saltar al contenido principal Desactivar animaciones Inicio de sesión <div id="ctl00_PlaceHolderSearchArea_SmallSearchInputBox1_noscript">Parece que el explorador no tiene JavaScript habilitado. index Contenidos del sitio Sede Administrativa Secretaría de Transparencia: Carrera 8 # 12B - 61 Piso 10 Edificio BIC - Bogotá D.C Horario de Atención al Público: lunes a viernes de 8:00 am a 5:30 pm Línea Directa: (57-1) 587 0555 PBX: (57-1) 562 9300 Ext. 3633 Correo electrónico: obstransparencia@presidencia.gov.co Correspondencia: Calle 7 No. 6-54 - Presidencia de la República de Colombia Línea de Atención Gratuita Nacional: 018000-913040 El Observatorio de Transparencia y Anticorrupción no tramita denuncias por casos de corrupción. Entidad
Le « deep learning », une révolution dans l'intelligence artificielle Cette technologie d'apprentissage, basée sur des réseaux de neurones artificiels, a complètement bouleversé le domaine de l'intelligence artificielle en moins de cinq ans. Le Monde.fr | • Mis à jour le | Par Morgane Tual « Je n'ai jamais vu une révolution aussi rapide. On est passé d'un système un peu obscur à un système utilisé par des millions de personnes en seulement deux ans. » Yann LeCun, un des pionniers du « deep learning », n'en revient toujours pas. Après une longue traversée du désert, « l'apprentissage profond », qu'il a contribué à inventer, est désormais la méthode phare de l'intelligence artificielle (IA). Ce système d'apprentissage et de classification, basé sur des « réseaux de neurones artificiels » numériques, est, pêle-mêle, utilisé par Siri, Cortana et Google Now pour comprendre la voix, être capable d'apprendre à reconnaître des visages. Qu'est-ce que c'est ? « Comment reconnaître une image de chat ? Concrètement, ça donne quoi ? Et demain ?