Tag Archives: R Programming Online Training

Analyze Smartphone Sensor Data with R and the BreakoutDetection Package

Analyze-Smartphone-Sensor-Data-with-R-and-the-BreakoutDetection-Package
 

Quite interetsing. Juggling with sensor data is starkly different from economics data, document processing or social networking, but very worthwhile. In this blog, we will take a practical approach to analyze smartphone sensor data with R. We are going to use the accelerometer smartphone data that Datarella presented in its Data Fiction competition. The dataset signifies the stimulation along the three axes of the smartphone:

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Using R Programming to Simulate the Incredible Pong Arcade Game

Unleashed in the market in 1972, Pong is one of the first computer games ever developed. Loosely inspired by tennis, Pong captured the worldwide gaming market soon after its launch. Instantaneously, it became a trending fad. Gaming enthusiasts became intrigued, they desired to delve deeper into the computer coding and system mechanisms mostly to understand the essence of arcade game development.

 
Using R Programming to Simulate the Incredible Pong Arcade Game
 

Today, R-Programming is extensively used to develop numerous board games. But the question to ponder on is – can we create traditional arcade games with R programming?

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How to Assess Clustering Tendency: Unsupervised Machine Learning

The meaning of clustering algorithms include partitioning methods (PAM, K-means, FANNY, CLARA etc) along with hierarchical clustering which are used to split the dataset into two groups or clusters of similar objects.

 
How To Assess Clustering Tendency: Unsupervised Machine Learning
 

A natural question that comes, before applying any clustering method on the dataset is:

 

Does the dataset comprise of any inherent clusters?

 

A big problem associated to this, in case of unsupervised machine learning is that clustering methods often return clusters even though the data does not include any clusters. Put in other words, if one blindly applies a clustering analysis on a dataset, it will divide the data into several clusters because that is precisely what they are supposed to do.

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What Sets Apart Data Science from Big Data and Data Analytics

Today is a time when omnipresent has a whole new definition. We no longer think about the almighty, omnipotent and omnipresent God when we speak about being everywhere. Nowadays we mostly mean data when we hear the term “present everywhere”. The amount of digital data that populates the earth today is growing at a tremendous rate, doubling over every two years and transforming the way we live.

What Sets Apart Data Science from Big Data and Data Analytics

As per IBM, an astounding amount of 2.5 Billion gigabytes of data is generated every day since the year 2012. Another revelation made by an article published in the Forbes magazine stated that data is growing faster than ever before today, and by the year 2020 almost 1.7 megabytes of new information will be created every second by every human being on this earth. And that is why it is imperative to know the fundamental basics of this field as clearly this is where our future lies.

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