![]() ![]() Data8 provides basic exposure to Python programming and working with tabular data as well as visualization, statistics, and machine learning.Ĭomputing: The Structure and Interpretation of Computer Programs CS 61A or Computational Structures in Data Science CS 88. While we are working to make this class widely accessible we currently require the following (or equivalent) prerequisites:įoundations of Data Science: Data 8 covers much of the material in Data 100 but at an introductory level. Prepare students for advanced Berkeley courses in data-management ( CS 186), machine learning ( CS 189), and statistics ( Stat 154), by providing the necessary foundation and contextĮnable students to start careers as data scientists by providing experience working with real-world data, tools, and techniquesĮmpower students to apply computational and inferential thinking to tackle real-world problems These include languages for transforming, querying, and analyzing data algorithms for machine learning methods including regression, classification, and clustering principles behind creating informative data visualizations statistical concepts of measurement error and prediction and techniques for scalable data processing. Through a strong emphasis on data-centric computing, quantitative critical thinking, and exploratory data analysis this class covers key principles and techniques of data science. In this class, we explore key areas of data science including question formulation, data collection and cleaning, visualization, statistical inference, predictive modeling, and decision-making. This intermediate-level class bridges between Data 8 and upper-division computer science and statistics courses as well as methods courses in other fields. UC Berkeley Spring 2024 Frequently Asked Questions OfferingsĬombining data, computation, and inferential thinking, data science is redefining how people and organizations solve challenging problems and understand their world. ![]() Principles and Techniques of Data Science ![]()
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