Jan 03, 2019 · Data Science Process (a.k.a the O.S.E.M.N. framework) I will walk you through this process using OSEMN framework, which covers every step of the data science project lifecycle from end to end. 1. Obtain Data. The very first step of a data science project is straightforward. We obtain the data that we need from available data sources. As we know that data analysis is a sub-component of data analytics so data analysis life cycle also comes into analytics part, it consists data gathering, data scrubbing, analysis of data and interprets the data precisely so that you can understand what your data want to say.

This lifecycle is designed for data-science projects that are intended to ship as part of intelligent applications. These applications deploy machine learning or artificial intelligence models for predictive analytics. Exploratory data-science projects and improvised analytics projects can also benefit from the use of this process. The Analysis. At this point, the data is reduced (hopefully) to hundreds of thousands, or perhaps a few million rows. The reduction occurs because of aggregations, sampling or both. Examples of reduction include user feedback analysis, since is not necessary to have every single “like” event on a piece of content,... The Modern Analytics Lifecycle, when paired with a self-service analytics platform, can transform companies from status-quo to data-driven organizations that empower their business analysts and data scientists to push previously untouched boundaries.