Curriculum Overview

The Master of Science in Analytics program is a full-time, on-campus program spanning five consecutive quarters, beginning in the fall quarter and ending in the subsequent fall quarter. The first three quarters are spent on campus taking required courses and working on an overarching Practicum project in collaboration with fellow cohort members. During the summer quarter, students spend a minimum of 10 weeks fulfilling an industry internship. There is no tuition charged during this quarter. Finally, students return for their second fall quarter to complete the last of their coursework and engage in their Capstone projects before graduating.

The general timeline for classes and project work is shown below.

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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.

Everything Starts with Data (MSiA 400)

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

Predictive Analytics I (MSiA 401)

Multiple regression, logistic regression, discriminant analysis, generalized linear models, and survival analysis.

Introduction to Databases & Information Retrieval (MSiA 413)

Data models and database design; SQL, distributed databases, and information retrieval.

Introduction to Java & Python Programming (MSiA 422)

Object oriented programming, data structures, and algorithms.

Practicum in Leadership (MSiA 488)

Through reflective experiential exercises with their Industry Practicum groups, students improve their leadership effectiveness and increase emotional intelligence by becoming more aware of who they are and how they understand and interact with others.

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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.

Generating Business Value with Analytics (MSiA 410)

An engaging and practical course on how analytics and strong communication skills can drive business value.

Data Visualization (MSiA 411)

Visualization principles, scorecards, dashboards, interacting with graphics, story telling, and D3.

Predictive Analytics II (MSiA 420)

Non-parametric regression and classification methods, including fundamental concepts, various nonlinear predictive modeling methods and algorithms, and understanding and interpreting results. Introduction to time series forecasting. 

Data Mining (MSiA 421)

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

Practicum in Leadership (MSiA 488)

Through reflective experiential exercises with their Industry Practicum groups, students improve their leadership effectiveness and increase emotional intelligence by becoming more aware of who they are and how they understand and interact with others.

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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.

Analytics Value Chain (MSiA 423)

This course teaches what it takes to move a machine learning-based solution from a concept to a production application as well as A/B testing and design of experiments. 

Data Warehousing and Workflow Management (MSiA 430)

Online Analytical Processing (OLAP), dimensional modeling, and data streaming.

Analytics for Big Data (MSiA 431)

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

Deep Learning (MSiA 432)

Deep learning models (generative and discriminative), CNN, RNN, and backpropagation.

Practicum in Leadership (MSiA 488)

Through reflective experiential exercises with their Industry Practicum groups, students improve their leadership effectiveness and increase emotional intelligence by becoming more aware of who they are and how they understand and interact with others.

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Summer Quarter

Internship

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

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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.

Text Analytics (MSiA 414)

An introduction to a variety of practical Natural Language Processing tasks / techniques with focus on industry topics and state-of-the-art systems.

Two Electives 

Choose from (examples): AR/VR for Virtual Analytics, Reinforcement Learning for Artificial Intelligence, Healthcare Analytics, Predictive Models for Credit Risk Management, Optimization & Heuristics, or Social Networks Analysis.