Learn data science fundamentals with practical examples and exercises, covering Python, NumPy, Pandas, R, SQL, and data visualization techniques. Key Features Comprehensive coverage of data science fundamentals Practical examples and exercises for hands-on learning Diverse tools and techniques, including Python, NumPy, Pandas, R, and data visualization Book Description This book, part of the Pocket Primer series, introduces the basic concepts of data science using Python 3 and other applications. It offers a fast-paced introduction to data analytics, statistics, data visualization, linear algebra, and regular expressions. The book features numerous code samples using Python, NumPy, R, SQL, NoSQL, and Pandas. Companion files with source code and color figures are available. Understanding data science is crucial in today's data-driven world. This book provides a comprehensive introduction, covering key areas such as Python 3, data visualization, and statistical concepts. The practical code samples and hands-on approach make it ideal for beginners and those looking to enhance their skills. The journey begins with working with data, followed by an introduction to probability, statistics, and linear algebra. It then delves into Python, NumPy, Pandas, R, regular expressions, and SQL/NoSQL, concluding with data visualization techniques. This structured approach ensures a solid foundation in data science.What you will learn Understand and preprocess various types of data Apply probability and statistical methods Utilize linear algebra in data science applications Implement Python for data manipulation and analysis Use NumPy and Pandas for efficient data handling Visualize data effectively using various tools Who this book is for This book is ideal for beginners and intermediate learners in data science, including students, professionals, and enthusiasts. Basic programming knowledge is beneficial but not mandatory. The book assumes no prior expertise in data science, making it accessible to a broad audience.