Ozdemir Principles of Data Science

A beginner's guide to essential math and coding skills for data fluency and machine learning
3. Auflage 2024
ISBN: 978-1-83763-600-6
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 326 Seiten

Principles of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights.
Starting with cleaning and preparation, you'll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you'll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data.
With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You'll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you'll explore medium-level data governance, including data provenance, privacy, and deletion request handling.
By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.

Autoren/Hrsg.

Ozdemir, Sinan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Table of Contents - Data Science Terminology
- Types of Data
- The Five Steps of Data Science
- Basic Mathematics
- Impossible or Improbable – A Gentle Introduction to Probability
- Advanced Probability
- What are the Chances? An Introduction to Statistics
- Advanced Statistics
- Communicating Data
- How to Tell if Your Toaster is Learning – Machine Learning Essentials
- Predictions Don't Grow on Trees, or Do They?
- Introduction to Transfer Learning and Pre-trained Models
- Mitigating Algorithmic Bias and Tackling Model and Data Drift
- AI Governance
- Navigating Real-World Data Science Case Studies in Action

Über Autor(innen)

Ozdemir Sinan :

Sinan is an active lecturer focusing on large language models and a former lecturer of data science at the Johns Hopkins University. He is the author of multiple textbooks on data science and machine learning including "Quick Start Guide to LLMs". Sinan is currently the founder of LoopGenius which uses AI to help people and businesses boost their sales and was previously the founder of the acquired Kylie.ai, an enterprise-grade conversational AI platform with RPA capabilities. He holds a Master's Degree in Pure Mathematics from Johns Hopkins University and is based in San Francisco.