eep learning and Machine learning are becoming more and more important in today’s Enterprises. During the process of building the analytical model using Deep Learning or Machine Learning the data set is collected from various sources such as a file, database, sensors and much more.
Much of the work around data and analytics is on delivering value from it. This includes dashboards, reports, and other data visualizations used in decision making; models that data scientists create to predict outcomes; or applications that incorporate data, analytics, and models.
What has sometimes been undervalued is all the underlying data operations work, or dataops, that it takes before the data is ready for people to analyze and format into applications to present to end users.
We use dataOps methodology as our guideline in order to work with data and prepare it at its best. Dataops is a relatively new umbrella term for the collection of data management practices with the goal of making users of the data—including executives, data scientists, as well as applications—successful in delivering business value from the data.
In general and in most cases, the collected data cannot be used directly for performing analysis process. Therefore, to solve this problem, Data Preparation is done. It includes two techniques that are listed below