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Principles of Data Science - Third Edition
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Principles of Data Science - Third Edition

by Sinan Ozdemir

Data Science

Book Details

Book Title

Principles of Data Science - Third Edition

Author

Sinan Ozdemir

Publisher

Packt Publishing Pvt Ltd

Publication Date

2024

ISBN

9781837636303

Number of Pages

330

Language

English

Format

PDF

File Size

4.3MB

Subject

Data science

Table of Contents

  • Contributor
  • About the author
  • About the reviewer
  • Preface
  • Chapter 1: Data Science Terminology
  • What is data science?
  • The data science Venn diagram
  • Some more terminology
  • Data science case studies
  • Summary
  • Chapter 2: Types of Data
  • Structured versus unstructured data
  • The four levels of data
  • Summary
  • Questions and answers
  • Chapter 3: The Five Steps of Data Science
  • Introduction to data science
  • Exploring the data
  • Summary
  • Chapter 4: Basic Mathematics
  • Basic symbols and terminology
  • Linear algebra
  • Summary
  • Chapter 5: Impossible or Improbable – A Gentle Introduction to Probability
  • Basic definitions
  • Bayesian versus frequentist
  • How to utilize the rules of probability
  • Introduction to binary classifiers
  • Summary
  • Chapter 6: Advanced Probability
  • Bayesian ideas revisited
  • Random variables
  • Summary
  • Chapter 7: What Are the Chances? An Introduction to Statistics
  • What are statistics?
  • How do we obtain and sample data?
  • How do we measure statistics?
  • The empirical rule
  • Summary
  • Chapter 8: Advanced Statistics
  • Understanding point estimates
  • Sampling distributions
  • Confidence intervals
  • Hypothesis tests
  • Summary
  • Chapter 9: Communicating Data
  • Why does communication matter?
  • Identifying effective visualizations
  • When graphs and statistics lie
  • Verbal communication
  • Summary
  • Chapter 10: How to Tell if Your Toaster is Learning – Machine Learning Essentials
  • Introducing ML
  • Types of ML
  • Predicting continuous variables with linear regression
  • Summary
  • Chapter 11: Predictions Don’t Grow on Trees, or Do They?
  • Performing naïve Bayes classification
  • Understanding decision trees
  • Diving deep into UL
  • Feature extraction and PCA
  • Summary
  • Chapter 12: Introduction to Transfer Learning and Pre-Trained Models
  • Understanding pre-trained models
  • Different types of TL
  • TL with BERT and GPT
  • Summary
  • Chapter 13: Mitigating Algorithmic Bias and Tackling Model and Data Drift
  • Understanding algorithmic bias
  • Sources of algorithmic bias
  • Measuring bias
  • Consequences of unaddressed bias and the importance of fairness
  • Mitigating algorithmic bias
  • Bias in LLMs
  • Emerging techniques in bias and fairness in ML
  • Understanding model drift and decay
  • Mitigating drift
  • Summary
  • Chapter 14: AI Governance
  • Mastering data governance
  • Navigating the intricacy and the anatomy of ML governance
  • A guide to architectural governance
  • Summary
  • Chapter 15: Navigating Real-World Data Science Case Studies in Action
  • Introduction to the COMPAS dataset case study
  • Text embeddings using pretrained models and OpenAI
  • Summary
  • Index
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