Preface

In today’s data-driven economy, the ability to derive actionable insights from complex datasets has become a critical skill across industries. Whether in business, healthcare, finance, retail, or government, data analytics has emerged as the cornerstone of intelligent decision-making. This book, Data Analytics with Python: A Practical Guide, is designed to empower aspiring analysts, students, and professionals with the practical skills and knowledge necessary to navigate the full analytics lifecycle.

From data acquisition and cleaning to visualization, statistical modelling, and integration with artificial intelligence tools, this book offers a comprehensive and hands-on approach to modern data analysis using Python and SQL. Each chapter is carefully crafted to build core competencies while also introducing the latest developments, such as the integration of Generative AI, AutoML platforms, and real-time dashboards.

A distinguishing feature of this book is its balance between foundational concepts and industry applications. Through case studies in customer churn prediction, fraud detection, sales performance analysis, and more, readers will gain real-world exposure to how data is leveraged for business impact. Extensive code examples, visualizations, and project-based exercises ensure that learners not only understand the theory but also acquire the technical fluency to apply it.

Special attention has been given to Generative AI—a transformative technology that is revolutionizing how data analysts work. Tools like OpenAI’s ChatGPT, Microsoft Copilot, and Tableau GPT are enabling analysts to generate code, query data, summarize dashboards, and create visualizations through natural language interaction. This book guides readers on how to effectively integrate these tools into daily workflows, enhancing productivity, accelerating insight generation, and supporting decision intelligence.

Whether you are an undergraduate student, a postgraduate learner, a working professional, or an educator designing analytics curricula, this book aims to serve as both a foundational text and a practical manual. It is our hope that Data Analytics with Python will help you unlock the potential of data and become a confident, agile, and future-ready analyst.

We are deeply grateful to Nitya Publications for their support and to all readers who bring this knowledge to life through application and innovation.