Book contents
- A Hands-On Introduction to Data Science
- A Hands-On Introduction to Data Science
- Copyright page
- Contents
- Preface
- About the Author
- Part I Conceptual Introductions
- 1 Introduction
- 2 Data
- 3 Techniques
- Part II Tools for Data Science
- Part III Machine Learning for Data Science
- Part IV Applications, Evaluations, and Methods
- Book part
- Index
- References
2 - Data
from Part I - Conceptual Introductions
Published online by Cambridge University Press: 07 February 2026
Book contents
- A Hands-On Introduction to Data Science
- A Hands-On Introduction to Data Science
- Copyright page
- Contents
- Preface
- About the Author
- Part I Conceptual Introductions
- 1 Introduction
- 2 Data
- 3 Techniques
- Part II Tools for Data Science
- Part III Machine Learning for Data Science
- Part IV Applications, Evaluations, and Methods
- Book part
- Index
- References
Summary
This chapter explores the fundamentals of data in data science, covering data types (structured vs. unstructured), collection sources (open data, social media APIs, multimodal data, synthetic data), and storage formats (CSV, TSV, XML, RSS, JSON). It emphasizes the critical importance of data pre-processing, including data cleaning (handling missing values, smoothing noisy data, data munging), integration, transformation, reduction, and discretization. Through hands-on examples, the chapter demonstrates how to systematically prepare "dirty" real-world data for analysis by addressing inconsistencies, outliers, and missing information. The chapter highlights that data preparation is often half the battle in data science, requiring both technical skills and careful attention to data quality and bias.
Information
- Type
- Chapter
- Information
- A Hands-On Introduction to Data Science with Python , pp. 43 - 74Publisher: Cambridge University PressPrint publication year: 2026
References
Further Reading and Resources
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