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Master of Science in Data Science
209.946.2992
datascience@pacific.edu
Dina Dell'Aringa
Administrative Assistant

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First Semester

RELATIONAL DATABASES
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 FOR DATA SCIENCE 1
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.


ANALYTICS COMPUTING 1
This course introduces computational data analysis using multi-paradigm programming languages. This course emphasizes the use of these languages for statistical and machine learning data analysis and predictive modeling. This course also emphasizes using analytics specific libraries, and will introduce the use of graphical analytics tools. Prerequisites: Introduction to Programming Bootcamp module or equivalent; Linear Algebra for Analytics 


ANALYTICS COMPUTING 2 
This course introduces computational data analysis using multi-paradigm programming languages to tackle complex data analysis problems. This course will cover the essentials of programming in these various languages and their various packages for accessing data from various sources, 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. Prerequisite: ANALYTICS COMPUTING 1


STATISTICS FOR DATA SCIENCE 1
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. Prerequisites: Linear Algebra for Data Science (5 week module) A survey of regression, linear models, and experimental design.


WEEKLY HOT TOPICS
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.