![]() ![]() This may be satisfied concurrently to Data 100, though course staff highly recommend completing linear algebra prior to enrolling in Data 100. ![]() Math: Linear Algebra ( MATH 54, STAT 89A, or EE 16A): We will need some basic concepts like linear operators, eigenvectors, derivatives, and integrals to enable statistical inference and derive new prediction algorithms. These courses provide additional background in programming (e.g., for-loops, lambdas, debugging, and complexity) that will enable Data 100 to focus more on the concepts in Data Science and less on the details of programming in python. Data 8 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, CS61A, Computational Structures in Data Science, CS88, or Introduction to Computer Programming for Scientists and Engineers, ENGIN 7. Prerequisites - instructors require the following (or equivalent):įoundations of Data Science: Data 8 covers much of the material in Data 100 but at an introductory level. The course was opened up broadly in Fall 2017. In the pilot offering of the class, enrollment was limited to 98 students. ![]() It bridges between Foundations of Data Science (Data 8) and upper division computer science and statistics courses as well as methods courses in other fields. It also serves as an upper division core class for the data science major. The class is open to students of all majors and levels who meet the prerequisites. 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. Students in Data 100 explore the data science lifecycle, including question formulation, data collection and cleaning, exploratory data analysis and visualization, statistical inference and prediction, and decision-making. The class focuses on quantitative critical thinking and key principles and techniques needed to carry out this cycle. ![]()
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