COURSEWORK

Data Science

DSC 106 - Introduction to Data Visualization

Data visualization helps explore and interpret data through interaction. This course introduces the principles, techniques and algorithms for creating effective visualizations. The course draws on knowledge from several disciplines including computer graphics, human-computer interaction, cognitive psychology, design, and statistical graphics and synthesizes relevant ideas. Students will design visualization systems using D3 or other web-based software and evaluate their effectiveness.

Data visualization helps explore and interpret data through interaction. This course introduces the principles, techniques and algorithms for creating effective visualizations. The course draws on knowledge from several disciplines including computer graphics, human-computer interaction, cognitive psychology, design, and statistical graphics and synthesizes relevant ideas. Students will design visualization systems using D3 or other web-based software and evaluate their effectiveness.

DSC 100 - Introduction to Data Management

This course is an introduction to storage and management of large-scale data using classical relational (SQL) systems, with an eye toward applications in Data Science. The course covers topics including the SQL data model and query language, relational data modeling and schema design, elements of cost-based query optimizations, relational data base architecture, and database-backed applications.

This course is an introduction to storage and management of large-scale data using classical relational (SQL) systems, with an eye toward applications in Data Science. The course covers topics including the SQL data model and query language, relational data modeling and schema design, elements of cost-based query optimizations, relational data base architecture, and database-backed applications.

DSC 80 - The Practice and Application of Data Science

The marriage of data, computation, and inferential thinking, or “data science,” is redefining how people and organizations solve challenging problems and understand the world. This course bridges lower- and upper-division data science courses as well as methods courses in other fields. Students master the data science life-cycle and learn many of the fundamental principles and techniques of data science spanning algorithms, statistics, machine learning, visualization, and data systems.

The marriage of data, computation, and inferential thinking, or “data science,” is redefining how people and organizations solve challenging problems and understand the world. This course bridges lower- and upper-division data science courses as well as methods courses in other fields. Students master the data science life-cycle and learn many of the fundamental principles and techniques of data science spanning algorithms, statistics, machine learning, visualization, and data systems.

DSC 30 - Data Structures and Algorithms for Data Science

Builds on topics covered in DSC 20 and provides practical experience in composing larger computational systems through several significant programming projects using Java. 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.

Builds on topics covered in DSC 20 and provides practical experience in composing larger computational systems through several significant programming projects using Java. 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.

DSC 20 - Programming and Basic Data Structures for Data Science

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. 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. 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.

DSC 10 - Principles of Data Science

This introductory course develops computational thinking and tools necessary to answer questions that arise from large-scale datasets. This course emphasizes an end-to-end approach to data science, introducing programming techniques in Python that cover data processing, modeling, and analysis.

This introductory course develops computational thinking and tools necessary to answer questions that arise from large-scale datasets. This course emphasizes an end-to-end approach to data science, introducing programming techniques in Python that cover data processing, modeling, and analysis.

Computer Science

CSE 158 - Recommender Systems and Web Mining

Current methods for data mining and predictive analytics. Emphasis is on studying real-world data sets, building working systems, and putting current ideas from machine learning research into practice.

Current methods for data mining and predictive analytics. Emphasis is on studying real-world data sets, building working systems, and putting current ideas from machine learning research into practice.

CSE 151 - Introduction to Artificial Intelligence: Statistical Approaches

Reasoning with probabilities, reasoning and learning with Bayesian networks, decision making under uncertainty, sequential decision making, statistical learning methods, and reinforcement learning.

Reasoning with probabilities, reasoning and learning with Bayesian networks, decision making under uncertainty, sequential decision making, statistical learning methods, and reinforcement learning.

CSE 21 - Mathematics for Algorithms and Systems

This course will provide an introduction to the discrete mathematical tools needed to analyze algorithms and systems. Enumerative combinatorics: basic counting principles, inclusion-exclusion, and generating functions. Matrix notation. Applied discrete probability. Finite automata.

This course will provide an introduction to the discrete mathematical tools needed to analyze algorithms and systems. Enumerative combinatorics: basic counting principles, inclusion-exclusion, and generating functions. Matrix notation. Applied discrete probability. Finite automata.

CSE 20 - Discrete Mathematics

Basic discrete mathematical structures: sets, relations, functions, sequences, equivalence relations, partial orders, and number systems. Methods of reasoning and proofs: prepositional logic, predicate logic, induction, recursion, and pigeonhole principle. Infinite sets and diagonalization. Basic counting techniques; permutation and combinations. Applications will be given to digital logic design, elementary number theory, design of programs, and proofs of program correctness.

Basic discrete mathematical structures: sets, relations, functions, sequences, equivalence relations, partial orders, and number systems. Methods of reasoning and proofs: prepositional logic, predicate logic, induction, recursion, and pigeonhole principle. Infinite sets and diagonalization. Basic counting techniques; permutation and combinations. Applications will be given to digital logic design, elementary number theory, design of programs, and proofs of program correctness.

CSE 15L - Software Tools and Techniques Laboratory

Hands-on exploration of software development tools and techniques. Investigation of the scientific process as applied to software development and debugging. Emphasis is on weekly hands-on laboratory experiences, development of laboratory notebooking techniques as applied to software design.

Hands-on exploration of software development tools and techniques. Investigation of the scientific process as applied to software development and debugging. Emphasis is on weekly hands-on laboratory experiences, development of laboratory notebooking techniques as applied to software design.

CSE 12 - Basic Data Structures and Object-Oriented Design

Use and implementation of basic data structures including linked lists, stacks, and queues. Use of advanced structures such as binary trees and hash tables. Object-oriented design including interfaces, polymorphism, encapsulation, abstract data types, pre-/post-conditions. Recursion. Uses Java and Java Collections.

Use and implementation of basic data structures including linked lists, stacks, and queues. Use of advanced structures such as binary trees and hash tables. Object-oriented design including interfaces, polymorphism, encapsulation, abstract data types, pre-/post-conditions. Recursion. Uses Java and Java Collections.

CSE 11 - Introduction to Computer Science and Object-Oriented Programming: Java

An accelerated introduction to computer science and programming using the Java language. Basic UNIX. Modularity and abstraction. Documentation, testing and verification techniques. Basic object-oriented programming, including inheritance and dynamic binding. Exception handling. Event-driven programming. Experience with AWT library or other similar library.

An accelerated introduction to computer science and programming using the Java language. Basic UNIX. Modularity and abstraction. Documentation, testing and verification techniques. Basic object-oriented programming, including inheritance and dynamic binding. Exception handling. Event-driven programming. Experience with AWT library or other similar library.

Mathematics

MATH 183 - Statistical Methods

Introduction to probability. Discrete and continuous random variables–binomial, Poisson and Gaussian distributions. Central limit theorem. Data analysis and inferential statistics: graphical techniques, confidence intervals, hypothesis tests, curve fitting.

Introduction to probability. Discrete and continuous random variables–binomial, Poisson and Gaussian distributions. Central limit theorem. Data analysis and inferential statistics: graphical techniques, confidence intervals, hypothesis tests, curve fitting.

MATH 20C - Calculus and Analytic Geometry for Science and Engineering

Vector geometry, vector functions and their derivatives. Partial differentiation. Maxima and minima. Double integration.

Vector geometry, vector functions and their derivatives. Partial differentiation. Maxima and minima. Double integration.

MATH 18 - Linear Algebra

Matrix algebra, Gaussian elimination, determinants. Linear and affine subspaces, bases of Euclidean spaces. Eigenvalues and eigenvectors, quadratic forms, orthogonal matrices, diagonalization of symmetric matrices. Applications. Computing symbolic and graphical solutions using Matlab.

Matrix algebra, Gaussian elimination, determinants. Linear and affine subspaces, bases of Euclidean spaces. Eigenvalues and eigenvectors, quadratic forms, orthogonal matrices, diagonalization of symmetric matrices. Applications. Computing symbolic and graphical solutions using Matlab.