Data Science Tutorials and Insights

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How I learned R

How I learned R. A little about me and the techniques I used to learn this robust data science language.
How I learned R

UC Berkeley Machine Learning Crash Course: Part 1

Learn all the basics of machine learning — regression, cost functions, and gradient descent. This is the first article in Machine Learning at Berkeley's Crash Course series.
UC Berkeley Machine Learning Crash Course: Part 1

New Jupyter Client Released

A new implementation of the Jupyter notebook with realtime synchronization, written using React.js.
New Jupyter Client Released

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: 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

Spark & Python: MLlib Decision Trees

In this tutorial, you'll learn how to use Spark's machine learning library MLlib to build a Decision Tree classifier for network attack detection and use the complete datasets to test Spark capabilities with large datasets.

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

Spark & Python: Working with RDDs (II)

This is a Spark and Python tutorial that teaches you how to work with RDDs (Part II).

Spark & Python: MLlib Basic Statistics & Exploratory Data Analysis

In this Spark and Python tutorial, you'll learn more about MLlib basic statistics and exploratory data analysis.

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

Spark & Python: MLlib Logistic Regression

In this tutorial, you will learn how to use Spark's machine learning library MLlib to build a Logistic Regression classifier for network attack detection.

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: 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 & Python: Working with RDDs (I)

This tutorial introduces two different ways of getting data into the basic Spark data structure, RDD.

Building Data Products with Python: A Wine Review Website using Django and Bootstrap

With this tutorial, we start a series of tutorials about how to build data products with Python. As a leitmotif we want to build a web-based wine reviews and recommendations website using Python technologies such as Django and Pandas. We have chosen to build a wine reviews and recommendations website, but the concepts and the technology stack...
Building Data Products with Python: A Wine Review Website using Django and Bootstrap

Building a Movie Recommendation Service with Apache Spark & Flask - Part 2

This Apache Spark tutorial goes into detail on how to use Spark machine learning models, or even another kind of data analytics objects, within a web service. By using the Python language, we make this task very easy, thanks to Spark own Python capabilities and to Python-based frameworks such as Flask.
Building a Movie Recommendation Service with Apache Spark & Flask - Part 2

Building a Movie Recommendation Service with Apache Spark & Flask - Part 1

Spark & Python: SQL & DataFrames

This tutorial will introduce you to Spark capabilities. By using SQL language and data frames, you can perform exploratory data analysis easily.

Adding Flow Control to Apache Pig using Python

So you like Pig but its cramping your style? Are you not sure what Pig is about? Are you keen to write some code to write code for you? If yes, then this is for you.
Adding Flow Control to Apache Pig using Python

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

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