IBM Big Data Hub
JUNE 27, 2018
More than 75% of C-level executives consider it a top priority to better leverage data and analytics in their decision-making. Unfortunately, less than half of individual workers say the same — a disconnect that highlights how hard it can be to make those C-level priorities a reality.
Smarten
JUNE 27, 2018
In this article, we will discuss the Decision Tree analysis method. What is Decision Tree Analysis? There are two basic types of decision tree analysis: Classification and Regression. 1) Classification Trees are used when the target variable is categorical and, as the name implies, are used to classify/divide the data into these predefined categories of a target variable.
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CIO Business Intelligence
JUNE 27, 2018
Improved customer relations, increased employee productivity, new revenue streams — big benefits await those who breathe new life into their business intelligence strategies.
Smarten
JUNE 27, 2018
This article presents a brief explanation of Outliers, and how this type of analysis is used. What is Outlier Analysis? An outlier is an element of a data set that distinctly stands out from the rest of the data. In other words, outliers are those data points that lie outside the overall pattern of distribution as shown in figure below. The easiest way to detect outliers is to create a graph.
Speaker: Timothy Chan, PhD., Head of Data Science
Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.
Smarten
JUNE 27, 2018
This article provides a brief explanation of the SVM Classification method of analytics. What is SVM Classification Analysis? SVM Classifications are based on the idea of finding a hyper plane that best divides a dataset into predefined classes, as shown in the image below. The goal is to choose a hyperplane with the greatest possible margin between the hyper-plane and any point within the training set, giving a greater chance of new data being classified correctly.
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