Complete package for all Data Science models using R. Starting form Preprocessing, Data Manipulation, Feature Engineering, Model Building, and Model Validation.
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Data Science Professional
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R for Data Science SUMMARY
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A repository of functions for community ecology
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Codes and examples based on the book Data Science for Business by Foster Provost and Tom Fawcett
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A R Package that can be readily installed to get Numerical and Factor Summary of Columns in a DataFrame
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Self Learning Data Science & R and Python Programming
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a R package of smote(a tool of preprocessing imbalance data)
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Package to access programmatically datos.gob.es
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An R package for the comparison of MPT analysis approaches
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UCSB Data Science Project
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A Date Manager for Massive Data
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Tree_Distribution_Databaseby MortonArb-ForestEcology
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License: No License (No License)Repository for curating occurrence and predictor data of tree distributions
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Ohio State Intro R
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Imbalanced data refers to classification problems where one class outnumbers other class by a substantial proportion. Extreme imbalance data can be seen in banking or financial data.
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Our attempt to learn Bayesian statistics
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R section for data science
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R exercises (2016)
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Datos y códigos asociados a Encuesta de inserción laboral de investigadores científicos 2016
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data sets from projects
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Coursera JHU Data Science: Data Products, final project
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Various data viz examples
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Data analysis examples using Apple Health data.
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Algoritmos, Modelos Matemáticos e Estatísticos direcionados a Data Science
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Analysis of plus/minus scores for individual players for the past 5 seasons, sorted by team and position
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Exploratory Data Analysis with R - ML Meetup BH
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Smart meter data and code for MSc course
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TFG. Diseño de una herramienta para la caracterización financiera de usuarios de redes sociales. Ciencia de los datos, Fintech, Influencer, Marketing, R, REST API, Twitter
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Foundations of Applied Statistics and Data Science with Applications in Biology
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Husky Data Science
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data-scraping-cleaning-visualizationby advanced-data-science-projects
R 2 Version:Current License: No License (No License)
License: No License (No License)How to scrape online websites using RSelenium and submit forms, also cleaning very dirty PDF's and importing clean data using R to Tableau for visualizations
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Material para el curso
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R package with functions to streamline my data science projects
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It's pan India Data Science Competition by ZS Associates hosted on Hacker Rank. I got a rank in top 40 based on a GBM model.
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Shiny app for webpages on data science (decommissioned)
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An analysis of Texas inmates last words. The data is pulled from the internet using BeautifulSoup and analyzed in R. Specifically we use Latent Semantic Analysis. Initially this was an assignment for a class in data engineering.
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A light weight exploratory data analysis tool using data.table in R.
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Module 5: Introduction to R
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Extracting 🐦 data from opendata.uit.no
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Supplementary material for paper "Inferring social structure from continuous-time interaction data"
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I have been asked by my company to explore the potential of webscraping to extract and analyze data. These are the scripts I have used to establish proof of concept.
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This repo will be used to keep my archives about Data Science Specialization by Data Science Academy
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PredictTestbench: Test Bench for Comparison of Data Prediction Models
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R 2 Version:Current License: No License (No License)
License: No License (No License)The purpose of this project is to demonstrate your ability to collect, work with, and clean a data set. The goal is to prepare tidy data that can be used for later analysis. You will be graded by your peers on a series of yes/no questions related to the project. You will be required to submit: 1) a tidy data set as described below, 2) a link to a Github repository with your script for performing the analysis, and 3) a code book that describes the variables, the data, and any transformations or work that you performed to clean up the data called CodeBook.md. You should also include a README.md in the repo with your scripts. This repo explains how all of the scripts work and how they are connected. One of the most exciting areas in all of data science right now is wearable computing - see for example this article . Companies like Fitbit, Nike, and Jawbone Up are racing to develop the most advanced algorithms to attract new users. The data linked to from the course website represent data collected from the accelerometers from the Samsung Galaxy S smartphone. A full description is available at the site where the data was obtained: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones Here are the data for the project: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip You should create one R script called run_analysis.R that does the following. Merges the training and the test sets to create one data set. Extracts only the measurements on the mean and standard deviation for each measurement. Uses descriptive activity names to name the activities in the data set Appropriately labels the data set with descriptive variable names. From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.
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Data_Science_In_Rby djdhiraj
Complete package for all Data Science models using R. Starting form Preprocessing, Data Manipulation, Feature Engineering, Model Building, and Model Validation.
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HarvardX_DataScienceProfessionalby or73
Data Science Professional
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R-for-Data-Scienceby ChrisCrazi
R for Data Science SUMMARY
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CommEcolFunctionsby afilazzola
A repository of functions for community ecology
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DataScienceForBusinessby RMeissner2018
Codes and examples based on the book Data Science for Business by Foster Provost and Tom Fawcett
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uber-supply-demand-gapby rahulshuklab4u
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DataFrameSummaryby ravindrareddytamma
A R Package that can be readily installed to get Numerical and Factor Summary of Columns in a DataFrame
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Coursera-Data-Scienceby jemc36
Self Learning Data Science & R and Python Programming
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Dislexisby alejandroolmed59
Kotlin 2Updated: 5 y ago License: No License (No License)
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SMOTEby binyi10
a R package of smote(a tool of preprocessing imbalance data)
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omni-tree-coreby omni-tree
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datos.gob.es.rby SevillaR
Package to access programmatically datos.gob.es
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MPTmultiverseby mpt-network
An R package for the comparison of MPT analysis approaches
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Leaflet_Meetup_Feb2018by rladieslx
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prediction-lolby mchiang9
UCSB Data Science Project
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simter-ymdby simter
A Date Manager for Massive Data
Kotlin 2Updated: 3 y ago License: No License (No License)
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Tree_Distribution_Databaseby MortonArb-ForestEcology
Repository for curating occurrence and predictor data of tree distributions
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causalityby adammmorris
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IntroR-at-OSUby everhartlab
Ohio State Intro R
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handle_class_imbalance_databy treselle-systems
Imbalanced data refers to classification problems where one class outnumbers other class by a substantial proportion. Extreme imbalance data can be seen in banking or financial data.
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bayes2017by atkinsjeff
Our attempt to learn Bayesian statistics
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R-Programmingby import-ajith
R section for data science
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BeztPlotby herrBez
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encuesta_2016by anipchile
Datos y códigos asociados a Encuesta de inserción laboral de investigadores científicos 2016
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NeuralNetShinyAppby etsibert
Coursera JHU Data Science: Data Products, final project
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apple-health-examplesby mganjoo
Data analysis examples using Apple Health data.
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data-scienceby robson-fernandes
Algoritmos, Modelos Matemáticos e Estatísticos direcionados a Data Science
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NBA-trendsby WL-Biol185-ShinyProjects
Analysis of plus/minus scores for individual players for the past 5 seasons, sorted by team and position
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mmlbh-edaby davpinto
Exploratory Data Analysis with R - ML Meetup BH
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MSc-courseby gaskyk
Smart meter data and code for MSc course
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tfg_etsitby sanxlop
TFG. Diseño de una herramienta para la caracterización financiera de usuarios de redes sociales. Ciencia de los datos, Fintech, Influencer, Marketing, R, REST API, Twitter
R 2Updated: 7 y ago License: Permissive (MIT)
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asdscourse2019by jdstorey
Foundations of Applied Statistics and Data Science with Applications in Biology
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data-scraping-cleaning-visualizationby advanced-data-science-projects
How to scrape online websites using RSelenium and submit forms, also cleaning very dirty PDF's and importing clean data using R to Tableau for visualizations
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curs-1211by barcelonapm
Material para el curso
Perl 2Updated: 10 y ago License: No License (No License)
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DataScienceRby happyrabbit
R package with functions to streamline my data science projects
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ZS_YoungDataScientistChallange2016by sauravkaushik8
It's pan India Data Science Competition by ZS Associates hosted on Hacker Rank. I got a rank in top 40 based on a GBM model.
R 2Updated: 7 y ago License: Permissive (MIT)
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shiny-bookmarksby tohweizhong
Shiny app for webpages on data science (decommissioned)
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Death-Rowby MattScicluna
An analysis of Texas inmates last words. The data is pulled from the internet using BeautifulSoup and analyzed in R. Specifically we use Latent Semantic Analysis. Initially this was an assignment for a class in data engineering.
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expdataby r2rahul
A light weight exploratory data analysis tool using data.table in R.
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egg-volumesby fjukstad
Extracting 🐦 data from opendata.uit.no
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relational-event-networksby wesleytlee
Supplementary material for paper "Inferring social structure from continuous-time interaction data"
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Webscrapingby BuissonFlorent
I have been asked by my company to explore the potential of webscraping to extract and analyze data. These are the scripts I have used to establish proof of concept.
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data-scientistby RaniereRamos
This repo will be used to keep my archives about Data Science Specialization by Data Science Academy
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PredictTestbenchby neerajdhanraj
PredictTestbench: Test Bench for Comparison of Data Prediction Models
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Getting-and-Cleaning-Data-Course-Project-Samsung-Galaxy-S-Accelerometers-Data-by ShadowzI
The purpose of this project is to demonstrate your ability to collect, work with, and clean a data set. The goal is to prepare tidy data that can be used for later analysis. You will be graded by your peers on a series of yes/no questions related to the project. You will be required to submit: 1) a tidy data set as described below, 2) a link to a Github repository with your script for performing the analysis, and 3) a code book that describes the variables, the data, and any transformations or work that you performed to clean up the data called CodeBook.md. You should also include a README.md in the repo with your scripts. This repo explains how all of the scripts work and how they are connected. One of the most exciting areas in all of data science right now is wearable computing - see for example this article . Companies like Fitbit, Nike, and Jawbone Up are racing to develop the most advanced algorithms to attract new users. The data linked to from the course website represent data collected from the accelerometers from the Samsung Galaxy S smartphone. A full description is available at the site where the data was obtained: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones Here are the data for the project: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip You should create one R script called run_analysis.R that does the following. Merges the training and the test sets to create one data set. Extracts only the measurements on the mean and standard deviation for each measurement. Uses descriptive activity names to name the activities in the data set Appropriately labels the data set with descriptive variable names. From the data set in step 4, creates a second, independent tidy data set with the average of each variable for each activity and each subject.
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