The practise of using statistical methods, regression models, machine learning, and deep learning algorithms to provide sophisticated insights and create predictive applications is known as data science. By automating human effort, all software programmes aim to boost productivity and efficiency. In the past, these jobs had to be repetitive in nature and governed by a set of deterministic rules. An accounting system that can automatically generate a balance sheet using sales and costs is an example. Applications for data science aim to automate work that is not governed by deterministic rules and requires human judgement. Data science is a potent discipline that may provide businesses a lot of benefits. There are specialised domain-specific strategies that can be used, and it can be applied to many other domains.But data science problems are open-ended and require experimentation and an active spirit of enquiry. Statistics is a tool in the hands of mankind to translate complex facts into simple and understandable statement of facts. Both these approaches are used in this book with examples to explain the concepts. This book comprises previous question papers problems at appropriate places and also previous GATE questions at the end of each chapter for the benefit of the students.