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Displaying Principles of Data Science ( PDFDrive.com ).pdf.
Download a free PDF. If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost. Simply click on the link to claim your free PDF. https://packt.link/free-ebook/9781785887918. About. Principles of Data Science by Packt. Readme. Activity. Custom properties. 72 stars. 9 watching. 45 forks.
Understand five most important steps of data science; Use your data intelligently and learn how to handle it with care; Bridge the gap between mathematics and programming; Drive actionable results and clean your data using statistical models, calculus, and probability; Build and evaluate baseline machine learning models
Through data science, one can identify relevant issues, collect data from various data sources, integrate the data, conclude solutions, and communicate the results to improve and enhance organizations’ decisions and deliver value to users and organizations.
Master the fundamentals steps of data science through practical examples. Bridge the gap between math and programming using advanced statistics and ML. Harness probability, calculus, and models for effective data control. Explore transformative modern ML with large language models.
Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you'll feel confident about asking—and...
Data Science Principles is a Harvard Online course that gives you an overview of data science with a code- and math-free introduction to prediction, causality, data wrangling, privacy, and ethics.
Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you'll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas.
Learn practical data science combined with data theory to gain maximum insights from data. Discover methods for deploying actionable machine learning pipelines while mitigating biases in data and models. Explore actionable case studies to put your new skills to use immediately.
Download book PDF. Download book EPUB. Overview. Editors: Hamid R. Arabnia, Kevin Daimi, Robert Stahlbock, Cristina Soviany, Leonard Heilig, Kai Brüssau. Introduces various techniques, methods, and algorithms adopted by Data Science experts. Provides a detailed explanation of data science perceptions, reinforced by practical examples.