Data Science Fundamentals Pocket Primer (eBook)

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.

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Learn data science fundamentals with practical examples and exercises, covering Python, NumPy, Pandas, R, SQL, and data visualization techniques.Key FeaturesComprehensive coverage of data science fundamentalsPractical examples and exercises for hands-on learningDiverse tools and techniques, including Python, NumPy, Pandas, R, and data visualizationBook DescriptionThis 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 learnUnderstand and preprocess various types of dataApply probability and statistical methodsUtilize linear algebra in data science applicationsImplement Python for data manipulation and analysisUse NumPy and Pandas for efficient data handlingVisualize data effectively using various toolsWho this book is forThis 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.]]>