Data Theory B.S. at University of California Los Angeles

The Data Theory major has the following learning outcomes:

Understanding of mathematical and statistical bases of most common methods of data science
Ability to explain in writing, with examples, how concepts of statistics and mathematics together solve real-world problems involving data
Skillfully manage data
Development, comparison, and testing of data-driven models to solve problems
Understanding and explanation of variability when fitting and interpreting models of real-world systems
Carrying out of reproducible data analysis using accepted practices of research community
Written and verbal communication of findings of analyses
Identification of areas of active research in data science
Insightfully address problems concerning ethics of data use and storage, including data privacy and security
Demonstrated mastery of concepts and skills of machine learning, modeling and supervised learning, dimension reduction and unsupervised learning, and deep learning
Demonstrated familiarity with numerous software tools used in statistical and data science work and research
Demonstrated knowledge of mathematical foundations, including pure and applied linear algebra, basic analysis, probability, and optimization theory
Study and evaluation of proofs of mathematical and statistical results employed in data theory
Work effectively in a team on a data science problem
Demonstrated eligibility for graduate study in applied mathematical science or statistical science