Is functional programming more effective than object-oriented programming?

Imperative vs functional programming. It’s a debate that goes back to the birth of high-level languages—Fortran vs Lisp. In later years, it was retreaded as object-oriented vs function programming (OOP vs FP)—OOP having become the (massively) dominant software development paradigm. And, I’m a fully paid-up member. I embraced Object Pascal via Delphi 1 on 1995 […]
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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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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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5 Machine Learning Books Worth Reading

Machine learning remains a hot topic as organizations attempt to squeeze insights and competitive advantage from their data sets. I’ve selected five books that may be of interest if you are embarking on a machine learning initiative or career. Obviously, there are much more great books out there and I’d like to hear your recommendations—recommendation […]
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Tackling a new dataset

With all the excitement (hype?) surrounding machine learning, there’s a tendency for decision makers (and many analysts) to believe that insights are just waiting to spring forth from the data when they are finally released by these magical algorithms. When it’s time to sit down and actually work with a real dataset, disillusionment quickly sets […]
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