Our program takes a broad approach to analytics, complementing each course with case studies, team projects, and guest speakers.Our program takes a broad approach to analytics, complementing each course with case studies, team projects, and guest speakers.

Curriculum

Students in the Master of Science in Analytics program move through the coursework together in a cohort. The schedule of classes and project work is shown below.

You may also wish to:
View a filterable list of all courses
View a high-level overview of this program


Legend

Purple highlight = Professional Practicum
(spans first three quarters concurrent with main curriculum)

Yellow highlight = Capstone Project concurrent with main curriculum

Fall Quarter (first)

Industry Practicum (MSiA 489)

Under the guidance of business and technical advisers, students work in small teams to integrate coursework into an industry-supplied project.

Analytics for Competitive Advantage (MSiA 400)

A gateway course covering basic analytics concepts through projects and success stories.

Databases Retrieval (MSiA 413)

Data models and database design.

Predictive Analytics I (MSiA 401)

Multiple regression, logistic regression, and discriminant analysis.

Optimization and Heuristics (MSiA 440)

Integer programming, nonlinear programming, local search, genetic algorithms, simulated annealing, and metaheuristics.


Winter Quarter

Industry Practicum (MSiA 489)

Under the guidance of business and technical advisers, students work in small teams to integrate coursework into an industry supplied projects.

Analytical Consulting Project Leadership (MSiA 410)

Project management, business, and interpersonal communication.

Data Mining (MSiA 421)

Clustering (k-means, partitioning), association rules, factor analysis, scale development, survival analysis, principal components analysis, and dimension reduction.

Predictive Analytics II (MSiA 420)

Non-parametric regression and classification, time series, and quality control methods.

Introduction to Java & Python Programming (MSiA 422)

Object oriented programming, Java, data structures, and basic algorithms.


Spring Quarter

Industry Practicum (MSiA 489)

Under the guidance of business and technical advisers, students work in small teams to integrate coursework into an industry supplied projects.

Data Visualization (MSiA 411)

Purposes of visualization, statistical graphics, visualization for Exploratory Data Analysis, interacting with graphics, and large dataset applications.

Analytics for Big Data (MSiA 431)

With emphasis on Hadoop, unstructured data concepts (key-value), MapReduce technology, and analytics for big data.

Data Warehousing and Workflow Management (MSiA 430)

ETL (extract, transform, load), OLAP, workflow; Business Intelligence: dashboards, scorecards, and performance evaluations.

Elective 1

Choose from (examples): Social Networks Analysis or Deep Learning.


Summer Quarter

Internship

Students spend a minimum of 10 weeks in the employment of an industry collaborator.


Fall Quarter (Second)

Capstone Design Project (MSiA 499)

In this culminating project, students draw on the breadth and depth of the curriculum to address an industry supplied problem.

Leadership for Analytical Organizations and Functions (MSiA 412)

Organizational challenges with analytics, IT and business users; change management, and ROI.

Elective 2

Choose from (examples): Healthcare Analytics, Predictive Models for Credit Risk Management, or Text Analytics.