Coordinator: Logan Axon
Gonzaga’s online Master of Science in Data Science is a multi-disciplinary degree centered on computer programming and mathematics giving you a deep understanding of all aspects of data including machine learning, data management and data collections.
While focus deeply rests on the technical side of data science, students will tackle the ethical implications surrounding data including privacy, discrimination, appropriate use and governance.
Students in the program will learn to use your knowledge to become a creator and designer of analytical tools and models, not just an end user.
Data scientists are needed in nearly every industry as more organizations rely on data to make strategic decisions.
The Bureau of Labor Statistics shows jobs for Data Scientists are seeing 35% growth with an average salary of $103,500 a year.
Some other potential career tracks: data engineer, data analytics, machine learning engineer and software engineering.
M.S. in Data Science: 30 credits
Semester I, Fall (7 credits) |
|
Fall A Session |
|
| DATA 522 Foundations of Data Science |
4 credits |
Fall B Session |
|
| DATA 525 Statistical Computing |
3 credits |
Semester 2, Spring (6 credits) |
|
Spring A Session |
|
| DATA 532 Data and Algorithm Ethics | 3 credits |
Spring B Session |
|
| DATA 581 Data Analytics and Communication | 3 credits |
Semester 3, Fall (7 credits) |
|
Fall A Session |
|
| DATA 526 Data Mining & Statistical Learning | 3 credits |
Fall B Session |
|
| DATA 561 Machine Learning | 4 credits |
Semester 3, Spring (10 credits) |
|
Spring A Session |
|
| DATA 582 Data Intensive Systems | 3 credits |
| DATA 583 Scientific Physics I Laboratory | 3 credits |
| course work continues through full semester | |
Spring B Session |
|
| DATA 562 Machine Learning II | 4 credits |
This course presents relevant techniques and tools for solving real-world data science problems. Students will learn to apply industry standard libraries and services to build and host data science pipelines in the cloud. Data processing steps in the pipeline include fetching data from SQL
databases and the web (via application programming interfaces and web scraping), cleaning and exploring data, applying statistical analyses, training and deploying machine learning models, and presenting insights with interactive dashboarding.
This course covers programming used in statistical computing. Topics include statistical programming, graphics, Monte Carlo methods, simulation studies, optimization, smoothing, cross-validation, and bootstrapping. The class will be taught entirely in the R programming language.
This course covers an introduction to data mining/statistical learning. Topics include statistical foundations of learning and mining methods, dimension reduction, classification, regression, and clustering. Emphasis will be on comprehending models, intuition, and assumptions. The class will be taught entirely in the R programming language.
An introduction to the many ethical concerns surrounding data selection, collection, storage, retrieval, sale, and use of data sets and the algorithms that use them. Special focus on data bias, ownership, informed consent, data privacy, and security, as well as on algorithm fairness and jobs for those cataloging and tagging data sets, especially regarding workers from the third world or "AI Colonialism".
Equivalent:
CPSC 532 - OK if taken since Fall 2024
This course provides an overview of one or more of the major application areas of data science approaches. Students learn and apply data-science techniques discussed in DATA 522 to real-world problems, such as those related to e-commerce, digital marketing, or healthcare.
This course provides a technical introduction to neural networks and their use in building models for classification and regression. Different types of neural network architectures are introduced including recurrent neural networks, convolution neural networks, transformers and attention, deep learning, and relevant application areas such as image classification, natural language processing, and generative modeling.
This course covers current topics in modern machine learning approaches including reinforcement learning, bayesian networks and causality analysis, explainability in AI, and neurosymbolic techniques.
In this course, students will learn a variety of techniques and tools for effectively communicating data analysis questions, results, and insights to a range of audiences. The course will cover techniques related to data storytelling, data visualization, interactive dashboarding, digital portfolio design and development, technical report writing, and technical presentation skills for data science. Students will also learn to effectively use modern tools related to data storytelling and visualization, interactive dashboarding, and project hosting.
This course covers tools and techniques used in applying statistical and machine learning approaches to real-world data sets. Through hands-on assignments and projects, students learn relevant architectures, programming models, and tools related to data modeling and storage, extract-transform-load (ETL) processes, data warehousing, and data pipeline creation and management. The course also explores scalable, distributed, and cloud-based approaches used in data-intensive applications for accessing, filtering, clustering, and classifying data.
This course provides an overview of how to design a data science system and deploy the system into a production environment. Students complete a semester-long project that involves researching a data science problem, proposing a solution to the problem, implementing the solution, and deploying the solution as a hosted web application. Emphasis is placed on working with web-based application programming interfaces, gathering and processing data, researching and implementing common machine algorithms for data mining and classification, and securely deploying models in the cloud.