Principles of Data Science: A beginner’s guide to the math and coding skills you need to be fluent in data and machine learning: A beginner's guide to ... skills for data fluency and machine learning
by: Sinan Ozdemir (Author)
Publisher:Packt Publishing - ebooks Account
Publication Date: 9 Feb. 2024
Language:English
Print Length:419 pages
ISBN-10:1837636303
ISBN-13:9781837636303
Book Description
Transform your data into insights with essential techniques and math to unravel the secrets hidden within your data
Key FeaturesLearn practical data science combined with data theory to gain maximum insight from dataSee how to deploy actionable machine learning pipelines while mitigating biases in data and modelsExplore actionable case studies and see how to put your new skills to use, fast!
Book Description"Principles of Data Science" bridges mathematics, programming, and business analysis, empowering readers to confidently pose and address complex data questions and construct effective machine learning pipelines. It equips you with tools to transform abstract concepts and raw statistics into actionable insights.Beginning with cleaning and preparing data + effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Discover the statistical models that help you take control and navigate even the densest (or the sparsest) datasets and find out how to create powerful visualizations that communicate the stories your data are telling. In this edition, you will also learn advanced transfer learning and pre-trained models for NLP and vision tasks, with a focus on application. Advanced techniques for mitigating algorithmic bias in data and models are covered, along with addressing model and data drift. Finally, you will explore medium-level data governance including data provenance, privacy, and deletion request handling.By the end of the book, you'll learn the fundamentals of computational mathematics and statistics while exploring modern machine learning and large pre-trained models like GPT and BERT.
What you will learnMaster data science's core steps with practical examplesBridge math and programming through advanced stats and MLHarness probability, calculus, and models for data controlExplore transformative modern ML with large language modelsEvaluate ML success with effective metrics and MLOpsCreate visuals that convey actionable insightsQuantify and mitigate biases in data and ML models
Who this book is forIf you are an aspiring novice data scientist ready to learn more, this book is for you. If you have the basic math skills but want to apply them in data science, or you have good programming skills but lack the necessary math, this book will also help you. Some knowledge of Python programming will also help.
Table of contentsData Science TerminologyTypes of DataThe Five Steps of Data ScienceBasic MathematicsImpossible or Improbable? - An Introduction to ProbabilityAdvanced ProbabilityBasic StatisticsAdvanced StatisticsCommunicating Data How to Tell If Your Toaster Is Learning: Machine Learning EssentialsPredictions Don’t Grow on Trees, or do they? - Beyond Statistical ModellingIntroduction to Transfer Learning and Pre-trained modelsTackling Model and Data DriftDealing with Data GovernanceDealing with Data Governance
About the Author
Transform your data into insights with essential techniques and math to unravel the secrets hidden within your data
Key FeaturesLearn practical data science combined with data theory to gain maximum insight from dataSee how to deploy actionable machine learning pipelines while mitigating biases in data and modelsExplore actionable case studies and see how to put your new skills to use, fast!
Book Description"Principles of Data Science" bridges mathematics, programming, and business analysis, empowering readers to confidently pose and address complex data questions and construct effective machine learning pipelines. It equips you with tools to transform abstract concepts and raw statistics into actionable insights.Beginning with cleaning and preparing data + effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Discover the statistical models that help you take control and navigate even the densest (or the sparsest) datasets and find out how to create powerful visualizations that communicate the stories your data are telling. In this edition, you will also learn advanced transfer learning and pre-trained models for NLP and vision tasks, with a focus on application. Advanced techniques for mitigating algorithmic bias in data and models are covered, along with addressing model and data drift. Finally, you will explore medium-level data governance including data provenance, privacy, and deletion request handling.By the end of the book, you'll learn the fundamentals of computational mathematics and statistics while exploring modern machine learning and large pre-trained models like GPT and BERT.
What you will learnMaster data science's core steps with practical examplesBridge math and programming through advanced stats and MLHarness probability, calculus, and models for data controlExplore transformative modern ML with large language modelsEvaluate ML success with effective metrics and MLOpsCreate visuals that convey actionable insightsQuantify and mitigate biases in data and ML models
Who this book is forIf you are an aspiring novice data scientist ready to learn more, this book is for you. If you have the basic math skills but want to apply them in data science, or you have good programming skills but lack the necessary math, this book will also help you. Some knowledge of Python programming will also help.
Table of contentsData Science TerminologyTypes of DataThe Five Steps of Data ScienceBasic MathematicsImpossible or Improbable? - An Introduction to ProbabilityAdvanced ProbabilityBasic StatisticsAdvanced StatisticsCommunicating Data How to Tell If Your Toaster Is Learning: Machine Learning EssentialsPredictions Don’t Grow on Trees, or do they? - Beyond Statistical ModellingIntroduction to Transfer Learning and Pre-trained modelsTackling Model and Data DriftDealing with Data GovernanceDealing with Data Governance
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