Implementing an End to End Solution for Data Science Project Cycle-A Complete Roadmap for Data Aspirants

A huge amount of meaningful information and knowledge can be mined from the raw data using data science. The capability to transform data into meaningful insights is the primary key to gain competitive edge for any organization. Data Science is considered as the most attractive job for 21st century. The job of a data scientist begins with collection of raw data and munging on it iteratively till meaningful insights are obtained. Though there are a large number of technical papers available on data science, yet it has been observed that there is a lack of enough literature for a data science aspirant in developing an end to end solution for a data science project. The aim of this paper is to provide a standardized way to enable a budding data scientist to unravel meaningful insights and data driven evidences that can benefit organizations in a significant way and provide a competitive edge. The phases of a data science project cycle i.e. data extraction, data pre-processing, building predictive models and exposing these models as APIs to real time integration have been discussed in this paper. This paper also emphasizes on a few challenges faced by a data scientist and the best practices to deal with them. Thus, this paper shall help a beginner to kick start his journey as budding data scientist.