In this competitive world, there is a need to continue supporting core strengths in higher education: build on a reputation for excellence and diversity in learning and teaching, world-leading research and an enviable record of knowledge exchange. The goal of this book is to open the doors of opportunity related to different analytical techniques from a broader array of datasets. It is an attempt to provide a reservoir of updated knowledge on varied tools for academicians, consultants, research scholars, practitioner and students. Data Analytics using Python will enable readers gain sufficient knowledge and experience to perform analysis using different tools and techniques available in numerous libraries according to different requirement of the user for different types of data. In order to provide a more meaningful and easier learning experience, this book has been written with more interesting and relevant real-life examples. The examples taken from a variety of sectors are solved with proper explanation of code and comments are used for better clarity. This easy-to-understand approach would enable readers to develop the required skills and apply techniques to solve all types of problems in Python in an effective manner. The second edition of this book includes new chapters related to web scraping with Beautiful Soup and Selenium, new visualization technologies like dashboard with streamlit and graphical user interface with tkinter, integration of NoSQL databases like Neo4j, MongoDB and more SQL databases, advanced mathematical and statistical techniques like optimization and conjoint analysis, object oriented programming in Python, machine learning techniques like recommendation system, apriori algorithm and association rules and many more.