Fundamentals for Data Science, Vol. I

Beschreibung

Data science begins with strong foundations in statistics, mathematics, and computational thinking. Fundamentals for Data Science – Volume I provides a comprehensive and accessible introduction to the core principles that underpin data-driven analysis and decision-making.

This volume introduces readers to essential programming tools in R and Python, enabling hands-on engagement with data from the outset. Foundational topics such as probability theory, probability distributions, sampling, and statistical inference are developed with clarity and intuition, ensuring conceptual understanding without excessive mathematical formalism. Key mathematical tools, including linear algebra, are presented in a manner tailored to data science applications.

The book places strong emphasis on exploratory data analysis, regression modeling, causal inference, and business-focused statistical applications, reinforced through real-world case studies and executable code examples. Designed for undergraduate and postgraduate students, as well as professionals transitioning into analytics roles, this volume serves as both a classroom-ready textbook and a practical self-learning resource. It prepares readers with the analytical maturity required to progress confidently to advanced machine learning and modern data science techniques.

Volume I of this two-volume series lays the foundational pillars of data science, combining statistical theory, mathematical essentials, and practical computing skills required for modern data analysis. Designed as a comprehensive entry point, this volume equips readers with the conceptual and computational tools needed to understand, explore, and model data before progressing to advanced machine learning and high-dimensional methods.

The volume begins with hands-on introductions to R and Python, enabling readers with no prior programming experience to immediately engage in data exploration and analysis. Core probabilistic and statistical concepts probability theory, probability distributions, sampling, and parametric inference are developed systematically, ensuring a strong analytical backbone for data-driven reasoning. Essential mathematical tools, particularly linear algebra, are presented in an intuitive manner tailored to data science applications.

Emphasis is placed on exploratory data analysis, regression modeling, causal inference, and business-oriented statistical modeling, supported throughout by real-world case studies and applied examples. Mathematical rigor is balanced with intuition, and every major concept is reinforced using executable R and Python code.

This volume is ideal for undergraduate and postgraduate students, researchers, and practitioners seeking a structured and application-driven introduction to data science and business analytics. It serves as both a classroom-ready textbook and a self-study reference, preparing readers for advanced modeling techniques covered in Volume II.

Autor*in

Bhargab Chattopadhyay

Themen in »Fundamentals for Data Science, Vol. I«

Statistics for Management Business Statistics and Analytics Data Analytics Analytical methods Statistical methods Introduction to Data Analytics Statistics and Probability Data Visualization Predictive Analytics Python and R Programming Machine Learning Business Model Evaluation

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