Advanced Analytics in Professional Sports

The keywords Hadoop, Big Data, Machine Learning and AI no longer exclusively belong to the boardrooms of top IT companies. As the Advanced Analytics is becoming more mature, different types of businesses are trying to find ways to make use of it to improve their operations. Professional sports leagues were one of the early adapters […]
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MS Machine Language Server for Enterprise-Grade Analytics

R Server is dead! Long live Machine Learning Server! The introduction of Microsoft R Server with SQL Server 2016 was tremendously exciting for data scientists and analysts working with “Big Data”. When SQL Server 2017 was released, many were shocked to see that R Server was gone. They were, however, relieved to discover that R […]
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10 Rules for Creating Reproducible Results in Data Science

In recent years evidence has been mounting that points to a crisis in the reproducible results of scientific research. Reviews of papers in the fields of psychology and cancer biology found that only 40% and 10%, respectively, of the results, could be reproduced. Nature published the results of a survey of researchers in 2016 that […]
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Machine Learning using Spark and R

R is ubiquitous in the data science community. Its ecosystem of more than 8,000 packages makes it the Swiss Army knife of modeling applications. Similarly, Apache Spark has rapidly become the big data platform of choice for data scientists. Its ability to perform calculations relatively quickly (due to features like in-memory caching) makes it ideal […]
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Assumptions Can Ruin Your K-Means Clusters

Clustering is one of the most powerful and widely used of the machine learning techniques. It’s very seductive. Throw some data into the algorithm and let it discover hitherto unknown relationships and patterns. K-means is the most popular of all the cluster algorithms. It’s easy to understand—and therefore implement—so it’s available in almost all analysis […]
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