Why do we need data? And how can we take advantage of it? Each minute in 2020, over 250,000 online meetings were held, more than 500 hours of video were uploaded, and USD 1M was spent online [1]. At the end of the year, approximately 64 ZB of data were created [2]. While you were reading this blog so far, more than 21 PB of data have been generated. This is roughly equal to the storage of 21,000 high-end laptops*.

Data science and machine learning solutions Market Size, Status and Forecast 2025," which provides an overview of the various factors enabling …

Azure Machine Learning is an end to end solution that facilitates descriptive, predictive and prescriptive analysis. Working with Azure ML helps data scientists easily publish their code as a web service, to be accessible from different platforms. Data scientists can publish their R code directly from R Studio into Azure ML and create a web service that can be called from any other application. Imagine that we have a simple function in R Studio for calculating the sum of two variables. We are going to create an API (web service) for this function that can be called from other platforms (eg mobile applications, web applications).

Prescriptive analysis is the best way to see how to make a sale or encourage a customer in the future. Recommendation systems are the another name for prescriptive analysis. Customer activity is used to recommend items and improve conversion in the digital store. The history of previous purchases and interests are used to recommend new products. To make the recommendation we employ both descriptive and predictive analysis several times.