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Exploring geographical data using SparkR and ggplot2

The present analysis will use the power of SparkR to analyse large datasets in order to explore the 2013 American Community Survey dataset, more concretely its geographical features.
Exploring geographical data using SparkR and ggplot2

Data Science with Python & R: Exploratory Data Analysis

In this article, we will take a exploratory look at the crucial steps in Python's and R's data analytics process.
Data Science with Python & R: Exploratory Data Analysis

Data Science with Python & R: Data Frames I

These series of tutorials on Data Science will try to compare how different concepts in the discipline can be implemented into the two dominant ecosystems nowadays: R and Python.
Data Science with Python & R: Data Frames I

Google Summer of Code-2017-Biodiversity data cleaning

Methods for cleaning Biodiversity data in R
 Google Summer of Code-2017-Biodiversity data cleaning

Data Science with Python & R: Data Frames II

This is the continued tutorial for learning data science with Python & R. In this part, you will be learning about data selection and function mapping.
Data Science with Python & R: Data Frames II

Building Web Data Products with R & Shiny

In this tutorial, we will introduce Shiny, a web development framework and application server for the R language. In simple terms, Shiny can make data analysis into interactive web apps.
Building Web Data Products with R & Shiny

Data Science with Python & R: Sentiment Classification Using Linear Methods

In this tutorial, you'll learn how to create sentiment classification using linear methods with Python and R
Data Science with Python & R: Sentiment Classification Using Linear Methods

Data Science with Python & R: Dimensionality Reduction and Clustering

An important step in data analysis is data exploration and representation. In this tutorial we will see how by combining a technique called Principal Component Analysis (PCA) together with Cluster, we can represent in a two-dimensional space data defined in a higher dimensional one while, at the same time, be able to group this data in similar groups or clusters and find hidden relationships in our data.
Data Science with Python & R: Dimensionality Reduction and Clustering

Spark & R: Loading Data into SparkSQL Data Frames

In this second Spark & R tutorial, we will read data into a SparkSQL data frame as well as have a quick look at the schema.
Spark & R: Loading Data into SparkSQL Data Frames

Spark & R: Downloading data and Starting with SparkR using Jupyter notebooks

In this tutorial we will use the 2013 American Community Survey dataset and start up a SparkR cluster using IPython/Jupyter notebooks.
Spark & R: Downloading data and Starting with SparkR using Jupyter notebooks

Linear Models with SparkR 1.5: Uses and Present Limitations

In this analysis we will use SparkR machine learning capabilities in order to try to predict property value in relation to other variables present in the 2013 American Community Survey dataset.
Linear Models with SparkR 1.5: Uses and Present Limitations

Spark & R: data frame operations with SparkR

In this third tutorial (see the previous one) we will introduce more advanced concepts about SparkSQL with R that you can find in the SparkR documentation, applied to the 2013 American Community Survey housing data. These concepts are related with data frame manipulation, including data slicing, summary statistics, and aggregations.
Spark & R: data frame operations with SparkR

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