The Beginner's Guide to Data Science

This book discusses the principles and practical applications of data science, covering topics such as data wrangling, statistics, machine learning, data visualization, natural language processing, and time series analysis. It provides an extensive treatment and analysis of real-world questions, focusing on determining and assessing answers quickly and accurately.

Format: Hardback
Length: 248 pages
Publication date: 16 November 2022
Publisher: Springer International Publishing AG

This comprehensive book delves into the principles and practical applications of data science, covering a wide range of key topics. It begins by discussing data wrangling, a crucial step in preparing data for analysis. The book then explores statistics, which provides the foundation for understanding and interpreting data. Machine learning, a powerful tool for analyzing and predicting patterns, is extensively discussed, along with data visualization, which enables data insights to be presented effectively. Natural language processing and time series analysis are also covered, as they play pivotal roles in understanding and analyzing textual and temporal data.

In addition to these core topics, the book delves into the implementation of recommendation engines and the selection of appropriate metrics for distance-based analysis. Detailed investigations of techniques used in these areas are provided, along with comprehensive code examples, figures, and tables to aid in understanding and applying the concepts.

The authors of this book have a deep understanding of data science and its real-world applications. They offer an extensive treatment and analysis of real-world questions, focusing on the task of determining and assessing answers as expeditiously and precisely as possible. The book is organized into 11 chapters, each serving as an independent treatment of crucial data science topics.

The first chapter introduces data gathering and acquisition techniques, including data creation and web scraping. It discusses the importance of managing, transforming, and organizing data to prepare it for analysis. The second chapter covers the fundamentals of descriptive statistics, which summarize and aggregate data into meaningful measurements. Inferential statistics, which allows for the inference of trends about the larger population based on a sample, is explored in the third chapter. Metrics, which measure quantities such as distance, similarity, or error, are discussed in the fourth chapter, and their usefulness in comparing and evaluating data is emphasized.

The fifth chapter focuses on recommendation, engines, which are algorithms designed to predict or recommend products to users. The book discusses the various types of recommendation engines and the techniques used to train them. The sixth chapter explores the selection of appropriate metrics for distance-based analysis, such as Euclide, Cosine, and Jaccard similarity. The seventh chapter discusses the implementation of recommendation engines using popular libraries such as Scikit-Learn and TensorFlow.

The eighth chapter discusses the evaluation of recommendation engines, including evaluation metrics such as precision, recall, and F1 score. The ninth chapter explores the challenges related to uncovering actionable. The actionable insights in “big data,” leveraging database and data collection tools such as web scraping and text identification. The tenth chapter discusses the use of machine learning techniques such as clustering, dimensionality reduction, and anomaly detection in “big data” analysis. The eleventh chapter discusses the application of data science in healthcare, finance, and marketing.

In conclusion, this book is a valuable resource for anyone interested in data science, providing a comprehensive treatment and analysis of key topics. It is organized in a clear and concise manner, making it accessible to beginners and experts alike. The extensive use of code examples, figures, and tables helps to clarify and illuminate essential data science concepts, and the real-world examples and case studies provide practical insights into the applications of data science in various industries. Whether you are a data analyst, scientist, or engineer, this book will help you gain a deeper understanding of data science and its potential to drive innovation and growth in today's data-driven world.

Dimension: 279 x 210 (mm)
ISBN-13: 9783031078644
Edition number: 1st ed. 2022