One of the best ways to measure the popularity or market share of software for data science is to count the number of job advertisements that highlight knowledge of each as a requirement. In rough order of the quality of the data, these include: There are many ways to measure popularity or market share, and each has its advantages and disadvantages.

Do your colleagues use it so you can easily share data and programs?.

Does it provide output in the form you prefer (e.g., cut & paste into a word processor vs.

Are its visualization options (e.g., static vs.

Does it fully support the style (programming, menus and dialog boxes, or workflow diagrams) that you like?.

Python, R) that is commonly accessible from many packages?

Does its extensibility use its own unique language or an external one (e.g.

Does the software provide all the methods you need? If not, how extensible is it?.

When choosing a tool for data analysis, now more commonly referred to as analytics or data science, there are many factors to consider: I announce the updates to this article on Twitter: Introduction

Updates: The most recent update was to the Job Advertisement section on.

Such software is also referred to as tools for data science, statistical analysis, machine learning, artificial intelligence, predictive analytics, and business analytics and is also a subset of business intelligence.

This article, formerly known as The Popularity of Data Analysis Software, presents various ways of measuring the popularity or market share of software for advanced analytics.

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