Data Science Course Schedule
The course schedule below should be used as an example. Contact your admission representative to discuss your specific course schedule.
Year 1
Fall Semester
Principles of Learning and Motivation for Design
EDUC 503 | 3 Units
Understand design and advancement of learning and motivation outcomes in various environments through a systematic examination and application of current research.
Data-Informed Design
EDUC 597 | 2 Units
Explore research design, data collection, assessment and evaluation methods.
Introduction to Computational Thinking and Data Science
DSCI 549 | 4 Units
Introduction to data analysis techniques and associated computing concepts for non-programmers.
Spring Semester
Designing Inclusive and Accessible Instruction
EDUC 591 | 3 units
Examine various research designs and their appropriateness for addressing different research questions, threats to validity and other challenges in research, and basic statistical methods and their use.
Principles of Programming for Data Science
DSCI 510 | 4 Units
Programming in Python for retrieving, searching and analyzing data from the Web. Learning to manipulate large data sets.
Year 2
Fall Semester
AI and Learning Technologies A
EDUC 565A | 3 units
Explore current and emerging generative AI and other educational technologies to enhance learning and instructional design.
Data Science at Scale
DSCI 550 | 4 Units
Big data informatics fundamentals. Data lifecycle; the data scientist; machine learning; data mining; NoSQL databases; tools to store, process, analyze large data sets on clusters.
Spring Semester
AI and Learning Technologies B
EDUC 565B | 3 Units
Design AI-enhanced, multimedia-rich learning experiences.
Data Presentation
EDUC 621 | 2 Units
Analyze and present data using visualizations and other formats. Apply data insights to support decision-making.
Viterbi Elective
4 Units
Elective Course
Year 1
Fall Semester
Principles of Learning and Motivation for Design
EDUC 503 | 3 Units
Understand design and advancement of learning and motivation outcomes in various environments through a systematic examination and application of current research.
Introduction to Computational Thinking and Data Science
DSCI 549 | 4 Units
Introduction to data analysis techniques and associated computing concepts for non-programmers.
Spring Semester
Designing Inclusive and Accessible Instruction
EDUC 591 | 3 units
Examine various research designs and their appropriateness for addressing different research questions, threats to validity and other challenges in research, and basic statistical methods and their use.
Principles of Programming for Data Science
DSCI 510 | 4 Units
Programming in Python for retrieving, searching and analyzing data from the Web. Learning to manipulate large data sets.
Year 2
Fall Semester
AI and Learning Technologies A
EDUC 565A | 3 units
Explore current and emerging generative AI and other educational technologies to enhance learning and instructional design.
Data-Informed Design
EDUC 597 | 2 Units
Explore research design, data collection, assessment and evaluation methods.
Spring Semester
AI and Learning Technologies B
EDUC 565B | 3 Units
Design AI-enhanced, multimedia-rich learning experiences.
Data Presentation
EDUC 621 | 2 Units
Analyze and present data using visualizations and other formats. Apply data insights to support decision-making.
Year 3
Fall Semester
Data Science at Scale
DSCI 550 | 4 Units
Big data informatics fundamentals. Data lifecycle; the data scientist; machine learning; data mining; NoSQL databases; tools to store, process, analyze large data sets on clusters.
Spring Semester
Viterbi Elective
4 Units
Elective Course
Focus Courses – Viterbi Electives
Security and Privacy
DSCI 529 | 4 units
Covers societal implications of information privacy and how to design systems to best preserve privacy. Recommended preparation: General familiarity with the use of common Internet and mobile applications.
Machine Learning for Data Science
DSCI 552 | 4 units
Practical applications of machine learning techniques to real-world problems. Uses in data mining and recommendation systems and for building adaptive user interfaces.
Foundations and Applications of Data Mining
DSCI 553 | 4 units
Data mining and machine learning algorithms for analyzing very large data sets. Emphasis on System Building with Spark. Case studies. Recommended Preparation: Probability on the level of EE 364, linear algebra on the level of EE 141, and essential programming on the level of DSCI 510.
Data Visualizations
DSCI 554 | 4 units
Graphical depictions of data for communication, analysis and decision support. Cognitive processing and perception of visual data and visualizations. Designing effective visualizations. Implementing interactive visualizations.
Data Science Professional Practicum
DSCI 560 | 4 units
Student teams working on external customer data analytic challenges; project/presentation based; real client data and implementable solutions for delivery to actual stakeholders; capstone to degree. Recommended preparation: Knowledge of data management, machine learning, data mining and data visualization.
Probability and Statistics for Data Science
DSCI 564 | 4 units
Fundamental concepts in probability and statistics from a data science perspective; rigorous probabilistic reasoning and problem-solving; statistical methods used in data science. Recommended preparation: Multivariate calculus, linear algebra, linear system theory.
Special Topic
DSCI 599* | 4 units
*with advisor approval
Course content to be selected each semester from recent developments in Data Science.
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Applicants are automatically considered for limited, merit-based scholarships of up to $15,000.
Apply NowQuestions about the program?
Contact:
Clara Colin, MA
Assistant Director, Office of Admission and Scholarships