Heard of the American game show “Let’s Make a Deal”? For those who don’t know, let me describe the game show in short. As a contestant on the game show, you are […]
While there are disagreements about what exactly constitutes a “data scientist,” there’s little doubt that one of the critical components involves statistical aptitude. And as the data science profession evolves, it’s taking […]
In this example, we will examine ARMA and ARIMA models with Python using the Statsmodels package. This package can be downloaded at http://statsmodels.sourceforge.net/stable/index.html. Autogressive Moving-Average Processes (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA) […]
Introduction IPython is a growing project, with increasingly language-agnostic components. IPython 3.x was the last monumental release of IPython, containing the notebook server, qtconsole, etc. As of IPython 4.0, the language-agnostic parts […]
Who performs analytics? Before we approach a formal definition, it may be useful to consider who performs analytics. Traditionally, analytics has been performed by statisticians, operations research analysts and management scientist. More […]
Uplift modeling, also known as incremental modeling, true lift modeling, or net-lift modeling is a predictive modeling technique that directly models the incremental impact of a treatment (such as a direct marketing […]
I am always at a loss in describing the skills of predictive analytics, for there are many. I am working on another book about analytics that has a different approach than Predictive Analytics using R, though I am using material from three chapters. The new book is an operations research approach to analytics, covering a different set of methods, skill and tools. Combined, the two books are over 1000 pages, so perhaps you can see my dilemma. Hence, this article is going to touch the basic skills required and several useful tool.