Data Science Tutorials and Insights

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Is Google Tensorflow Object Detection API the easiest way to implement image recognition?

Google Tensorflow Object Detection API
Is Google Tensorflow Object Detection API the easiest way to implement image recognition?

New Jupyter Client Released

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

How to Set Up Hadoop in Pseudo Distributed Mode on Single Cluster

This step-by-step tutorial will teach you how to set up Apache Hadoop in Pseudo-Distributed Mode on Single cluster.
How to Set Up Hadoop in Pseudo Distributed Mode on Single Cluster

Getting Started with Cassandra and Spark

This tutorial is going to go through the steps required to install Cassandra and Spark on a Debian system and how to get them to play nice via Scala.
Getting Started with Cassandra and Spark

Extending Apache Pig with Python UDFs

Apache Pig is a popular system for executing complex Hadoop map-reduce based data-flows. Pig is especially great because it is extensible. By the end of this tutorial, you will be able to write PigLatin scripts that execute Python code as a part of a larger map-reduce workflow.
Extending Apache Pig with Python UDFs

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

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

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

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

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

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.

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

Spark & Python: Working with RDDs (I)

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

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

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.

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: 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 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: 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

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