Owing to rise of Data Science, the world has seen a drastic change in the realm of business management in terms of predictive, prescriptive and preventive analytics. However formulating these analytics involves, a well structured approach to ensure proper planning and execution of a Data Science project. This report bridges the gap by presenting a guided framework from understanding the project requirements to deployment of model along with maintenance. The report presents a clear road map for successful project implementation, effective task allocation, and optimal resource management to meet deadlines and quality standards. Furthermore, it highlights the various tools and technologies used for model development, deployment, and real-time monitoring, fostering continuous improvement and system adaptability. Further, the report elaborates on various code management approaches, process and workflow management along with data science life cycle to ensure continuous development and adaptation.