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Spark ⭐ 37,661

Apache Spark - A unified analytics engine for large-scale data processing

Catboost ⭐ 7,564

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

H2o 3 ⭐ 6,618

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

ADAM is a genomics analysis platform with specialized file formats built using Apache Avro, Apache Spark, and Apache Parquet. Apache 2 licensed.

Bigsnpr ⭐ 168

R package for the analysis of massive SNP arrays.

Bigstatsr ⭐ 165

R package for statistical tools with big matrices stored on disk.

Datasciencevm ⭐ 161

Tools and Docs on the Azure Data Science Virtual Machine (http://aka.ms/dsvm)

Griffon Vm ⭐ 129

Griffon Data Science Virtual Machine

Chinesenames ⭐ 120

🀄 Chinese Name Database (1930-2008).

Spark Website ⭐ 109

Apache Spark Website

Bigdataclass ⭐ 109

Two-day workshop that covers how to use R to interact databases and Spark

Spark R Notebooks ⭐ 109

R on Apache Spark (SparkR) tutorials for Big Data analysis and Machine Learning as IPython / Jupyter notebooks

Biglasso ⭐ 107

biglasso: Extending Lasso Model Fitting to Big Data in R

Meetups Archivos ⭐ 62

Ppts, códigos y videos de las meetups, data science days, videollamadas y workshops. Data Science Research es una organización sin fines de lucro que busca difundir, descentralizar y difundir los conocimientos en Ciencia de Datos e Inteligencia Artificial en el Perú, dando oportunidades a nuevos talentos mediante MeetUps, Workshops y Semilleros de Conocimiento e Investigación.

Fixed Rank Kriging with big data

An R package for large scale estimation with stochastic gradient descent

Spark Docker ⭐ 53

Official Dockerfile for Apache Spark

Scalable R for Machine Learning

Subsemble ⭐ 39

subsemble R package for ensemble learning on subsets of data

Ggplot2.sparkr ⭐ 26

Rebooting ggplot2 for scalable big data visualization

Awesome Sparklyr ⭐ 22

An awesome sparklyr related package collection

Spark.sas7bdat ⭐ 21

Read in SAS data in parallel into Apache Spark

Data Science Beginners Guide ⭐ 17

📊A Beginner's Guide To Data Science. 📈 📉Start your self-learning journey into the world of data right now.

Remoteparts ⭐ 17

remotePARTS is a set of tools for running Partitioned spatio-temporal auto regression analyses on remotely-sensed data sets.

cMonkey integrated biclustering algorithm - official

Econowcast ⭐ 16

Experimental tools (R) for Big Data econometrics nowcasting and early estimates

Etlutils ⭐ 16

Utilities for easily loading big data from relational databases directly into ffdf objects in R.

Computing With Data ⭐ 15

Code samples for my book "Computing with Data: An Introduction to the Data Industry"

Filearray ⭐ 15

Out-of-memory Arrays in R

Recursive Partitioning for Structural Equation Models

Taller_sparkr ⭐ 12

Taller SparkR para las Jornadas de Usuarios de R

Utdeventdata ⭐ 11

An R package to retrieve political event data from the UTD API server

Learnrvideos ⭐ 10

📼 Videos for learning about R

Sparklyr extension package providing geospatial analytics capabilities

AgroMo is an Integrated Assessment and Modelling software that integrates 4M a CERES based crop model, the Biome-BGCMuSo biogeochemical and a simple agro-economical model in order to support decision makers at multiple scales.

Drillworkshop ⭐ 6

Learn how to quickly explore your data with Apache Drill

PLS package for big data

Save and load your R sessions faster using parallel compression

Microsoft Big Data Scientist And Ai ⭐ 5

Microsoft Big Data, Data Scientist, and AI

Digital Foundry Demand Forcasting ⭐ 5

In tune with conventional big data and data science practitioners’ line of thought, currently causal analysis was the only approach considered for our demand forecasting effort which was applicable across the product portfolio. Experience dictates that not all data are same. Each group of data has different data patterns based on how they were sold and supported over the product life cycle. One-methodology-fits-all is very pleasing from an implementation of view. On a practical ground, one must

Data Science Learning ⭐ 5

Data Science Learning

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