From concepts to code

• Verfasst von: Tashman, Adam P. [VerfasserIn]
• Titel: From concepts to code
• Titelzusatz: introduction to data science
• Verf.angabe: Adam P. Tashman
• Ausgabe: First edition
• Verlagsort: Boca Raton ; London ; New York
• Verlag: CRC Press, Taylor & Francis Group
• Jahr: 2024
• Umfang: 1 Online-Ressource (xviii, 367 Seiten)
• Fussnoten: Description based on publisher supplied metadata and other sources
• ISBN: 978-1-04-001451-6
• Schlagwörter: (s)Data Science / (s)Data Mining / (s)Datenanalyse / (s)Maschinelles Lernen
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe: Tashman, Adam P.: From concepts to code. - First edition. - Boca Raton : CRC Press, 2024. - xviii, 367 Seiten
• K10plus-PPN: 1885676700
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Acknowledgments -- Preface -- Symbols -- 1. Introduction -- 1.1. What Is Data Science? -- 1.2. Relationships Are of Primary Importance -- 1.3. Modeling and Uncertainty -- 1.4. Pipelines -- 1.4.1. The Data Pipeline -- 1.4.2. The Data Science Pipeline -- 1.5. Representation -- 1.6. For Everyone -- 1.7. Target Audience -- 1.8. How this Book Teaches Coding -- 1.9. Course and Code Package -- 1.10. Why Isn't Data Science Typically Done with Excel? -- 1.11. Goals and Scope -- 1.12. Exercises -- 2. Communicating Effectively and Earning Trust -- 2.1. Master Yourself -- 2.2. Technical Competence -- 2.3. Know Your Audience -- 2.4. Tell Good Stories -- 2.5. State Your Needs -- 2.6. Assume Positive Intent -- 2.7. Help Others -- 2.8. Take Ownership -- 2.9. Chapter Summary -- 2.10. Exercises -- 3. Data Science Project Planning -- 3.1. Defining the Project Objectives -- 3.2. A Questionnaire for Defining the Objectives -- 3.3. Analytical Framing -- 3.4. Planning Data Collection and Usage -- 3.5. Data Quantity and Coverage -- 3.6. Sourcing Data -- 3.7. Chapter Summary -- 3.8. Exercises -- 4. An Overview of Data -- 4.1. Data Types -- 4.2. Statistical Data Types -- 4.3. Datasets and States of Data -- 4.4. Data Sources and Data Veracity -- 4.5. Data Ingestion -- 4.5.1. Data Velocity and Volume -- 4.5.2. Batch versus Streaming -- 4.5.3. Web Scraping and APIs -- 4.6. Data Integration -- 4.7. Levels of Data Processing -- 4.7.1. Trusted Zone -- 4.7.2. Standardizing Data -- 4.7.3. Natural Language Processing -- 4.7.4. Protecting Identity -- 4.7.5. Refined Zone -- 4.8. The Structure of Data at Rest -- 4.8.1. Structured Data -- 4.8.2. Semi-structured Data -- 4.8.3. Unstructured Data -- 4.9. Metadata -- 4.10. Representativeness and Bias -- 4.11. Data Is Never Neutral -- 4.12. Chapter Summary.

Abstract

"The breadth of problems that can be solved with data science is astonishing, and this book provides the required tools and skills to a broad audience. The reader takes a journey into the forms, uses, and abuses of data and models, and learns how to critically examine each step. Python coding and data analysis skills are built from the ground up, with no prior coding experience assumed. The necessary background in computer science, mathematics, and statistics is provided in an approachable manner. Each step of the machine learning lifecycle is discussed, from business objective planning to monitoring a model in production. This end-to-end approach supplies the broad view necessary to sidestep many of the pitfalls that can sink a data science project. Detailed examples are provided from a wide range of applications and fields, from fraud detection in banking to breast cancer classification in healthcare. The reader will learn the techniques to accomplish tasks that include predicting outcomes, explaining observations, and detecting patterns. Improper use of data and models can introduce unwanted effects and dangers to society. A chapter on model risk provides a framework for comprehensively challenging a model and mitigating weaknesses. When data is collected, stored, and used, it may misrepresent reality and introduce bias. Strategies for addressing bias are discussed. The book leverages content developed by the author for a full-year data science course suitable for advanced high school or early undergraduate students. This course is freely available and it includes weekly lesson plans"--