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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.
This is the textbook for Data 100, the Principles and Techniques of Data Science course at UC Berkeley. Data 100 is the upper-division, semester-long data science course that follows Data 8, the Foundations of Data Science.
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.
Get an overview of data science with a nearly code- and math-free introduction to prediction, causality, visualization, data wrangling, privacy, and ethics. Download Full Syllabus. Download October 2024 Syllabus. Download February 2025 Syllabus.