Mgt 103 ucsd midterm 22/25/2024 ![]() Restricted to students within the DS25 major. DSC 40A-B connect to DSC 10, 20, and 30 by providing the theoretical foundation for the methods that underlie data science. Whereas other courses in the curriculum, such as DSC 20 and DSC 30, may touch on these topics briefly, this course aims to develop a deeper, theoretical understanding. Topics include time complexity analysis, the analysis of recursive algorithms, graph theory, and graph search algorithms. DSC 40B, the second course in the sequence, covers the fundamentals of computer science with applications to data science. The sequence DSC 40A-B introduces the theoretical foundations of data science. Theoretical Foundations of Data Science II (4) All other students will be allowed as space permits.ĭSC 40B. Prerequisites: DSC 10, MATH 20C or MATH 31BH, and MATH 18 or MATH 20F or MATH 31AH. Students practice creative problem-solving while learning how to rigorously justify and communicate mathematical ideas. Topics include empirical risk minimization, optimization, regression, classification, and discrete probability. DSC 40A, the first course in the sequence, exposes students to the mathematical theory underlying fundamental topics in machine learning. Theoretical Foundations of Data Science I (4) All other students will be allowed as space permits.ĭSC 40A. ![]() ![]() Students will study advanced programming techniques including encapsulation, abstract data types, interfaces, algorithms and complexity, and data structures such as stacks, queues, priority queues, heaps, linked lists, binary trees, binary search trees, and hash tables. Data Structures and Algorithms for Data Science (4)īuilds on topics covered in DSC 20 and provides practical experience in composing larger computational systems through several significant programming projects using Java. All other students will be allowed as space permits.ĭSC 30. ![]() Course will be taught in Python and will cover topics including recursion, higher-order functions, function composition, object-oriented programming, interpreters, classes, and simple data structures such as arrays, lists, and linked lists. Provides an understanding of the structures that underlie the programs, algorithms, and languages used in data science by expanding the repertoire of computational concepts introduced in DSC 10 and exposing students to techniques of abstraction. Programming and Basic Data Structures for Data Science (4) Through homework assignments and projects, students are given an opportunity to develop their analytical skills while working with real-world datasets from a variety of domains. It introduces the Python programming language as a tool for tabular data manipulation, visualization, and simulation. This first course in data science introduces students to data exploration, statistical inference, and prediction. All courses, faculty listings, and curricular and degree requirements described herein are subject to change or deletion without notice.įor course descriptions not found in the UC San Diego General Catalog 2023–24, please contact the department for more information.
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