Skip to content
  • Print
return to MS in Data Science


Master of Science in Data Science
Dina Dell'Aringa
Administrative Assistant
School of Engineering and Computer Science

How to apply

Request Information

First Semester

This course introduces relational database management systems (RDBMS) and the structured query language (SQL) for manipulating data stored therein. The class is focused on the applied use of SQL by data scientists to extract, manipulate and prepare data for analysis. Although this class is not a database design class, students will be exposed to entity-relationship (ER) models and the benefits of third normal form (3NF) data modeling. The class employs hands-on experiential learning utilizing the modern relational database querying languages and graphical development environments.

Linear algebra is the generalized study of solutions to systems of linear equations. This course will focus on developing a conceptual understanding of computational tools from linear algebra which are frequently employed in the analysis of data. These tools include formulating linear systems as matrix-vector equations, solving systems of simultaneous equations using technology, performing basic computations involving matrix algebra, solving eigenvalue-eigenvector problems using technology, diagonalization, and orthogonal projections. The use of software to perform computations will be emphasized.

This course introduces computational data analysis using multi-paradigm programming languages.  By the end of the course, students will tackle complex data analysis problems.  The course emphasizes the use of programming languages for statistical and machine learning analysis, and predictive modeling.   Graphical analytics tools will also be used.  The course will also cover the various packages for accessing data that come with the various languages, manipulating and preparing data for analysis, conducting statistical and machine learning analyses, and graphically plotting and visualizing data and analytical results.  The course emphasizes hands-on data and analysis using a variety of real-world data sets and analytical objectives. 

A survey of regression, linear models, and experimental design. Topics include simple and multiple linear regression, single- and multi-factor studies, analysis of variance, analysis of covariance, model selection, diagnostics. This class focuses more on the application of regression methods than the underlying theory through the use of modern statistical programming languages.

This course consists of a set of weekly presentations and discussions around key analytic issues and current case studies. These hot topics will be presented by a combination of guest speakers-industry luminaries in the area of analytics-and University of the Pacific faculty members, including the MS analytics program director. Many of these topics will be drawn from relevant real-world contemporary analytic stories that reinforce specific elements of the academic content being taught and can not be predicted in advance. Students will also be introduced to key topics around the use of data and the methods and techniques involved in data science. This will include Ethics, Critical Thinking, Communication Skills, Presentations Skills, and Innovation.

Students learn about research design, qualitative and quantitative research, and sources of data. Topics will include a variety of research topics, including such things as data collection procedures, measurement strategies questionnaire design and content analysis, interviewing techniques, literature surveys; information databases, probability testing, and inferential statistics. Students will prepare and present a research proposal (with emphasis on technical writing/presentation principles) as part of the course.