Foundations of Data Science with Python

The book Foundations of Data Science with Python provides an introduction to data science, covering topics such as data manipulation, visualization, probability, statistics, and dimensionality reduction. It uses a computational-first approach and teaches readers how to use Python and data-science libraries to perform statistical tests using real data sets. The book is targeted toward engineers and scientists, but is also accessible to anyone with basic calculus and computer programming knowledge. It offers a modern, computational approach to working with data and includes an accompanying website with interactive tools to help the reader learn the material.

Format: Hardback
Length: 496 pages
Publication date: 22 February 2024
Publisher: Taylor & Francis Ltd

Introduction:

The Foundations of Data Science with Python is a comprehensive guide that introduces readers to the fundamental principles and techniques of data science. This book is designed for engineers and scientists, but its content is accessible to anyone with a basic understanding of calculus and computer programming. The book adopts a computational-first approach to data science, emphasizing the use of Python and the associated data science libraries to visualize, transform, and model data. It also teaches readers how to conduct statistical tests using real data sets, employing a simple and general approach known as resampling.

Key Features:

1. Modern, Computational Approach: The book takes a modern, computational approach to working with data. It emphasizes the use of Python and the associated data science libraries, such as NumPy, Pandas, and Matplotlib, which are widely used in the field. This approach allows readers to efficiently analyze and manipulate large datasets, perform statistical tests, and build machine learning models.
2. Real Data Sets: The book uses real data sets to conduct statistical tests that address a diverse set of contemporary issues. These issues range from the effects of socioeconomic factors on the spread of the COVID-19 virus to the impact of state laws on firearms mortality. By using real data, readers can gain a deeper understanding of the practical applications of data science and its potential to address real-world problems.
3. Fundamental Tools: The book covers the fundamentals of several important tools in the Python data science ecosystem. These tools include NumPy, Pandas, Matplotlib, SciPy, and Seaborn, which are essential for data manipulation, visualization, statistical analysis, and machine learning. Readers will learn how to use these tools to clean, preprocess, analyze, and interpret data, and build models that can make predictions and draw insights from the data.
4. Accessible to Practicing Engineers and Scientists: The book is intended to be accessible to practicing engineers and scientists who need to gain foundational knowledge of data science. It provides a clear and concise explanation of complex concepts, and the examples and exercises are designed to help readers apply the knowledge gained to real-world scenarios.
5. Undergraduate Textbook: The book can be used as an undergraduate textbook for an Introduction to Data Science course. It covers the essential topics in data science, including data manipulation, visualization, probability, statistics, and dimensionality reduction. The book's computational-first approach and real-data examples make it an effective tool for teaching students the basics of data science.
6. Contemporary Approach: The book provides a more contemporary approach to data science than traditional textbooks. It emphasizes the use of modern tools and techniques, such as machine learning, deep learning, and natural language processing, which are increasingly important in today's data-driven world.

In conclusion, the Foundations of Data Science with Python is a comprehensive and accessible guide that provides readers with the fundamental knowledge and skills needed to work with data in a modern, computational environment. Whether you are a student, practicing engineer, or scientist, this book will help you gain a deeper understanding of data science and its applications to real-world problems.

Weight: 1080g
Dimension: 254 x 178 (mm)
ISBN-13: 9781032346748