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.
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.
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.
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.
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.
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.