Data Science, MS Analytics and Modeling Specialization
In the world of data science, the analysts and modelers specialize in testing real-world predictions about data. Data analysts and modelers conduct research and take complex factors into account to build predictive models and create forecasts upon which data-driven decisions can be made. With a focus on traditional methods of applied statistics, this specialization prepares data scientists to utilize algorithms for predictive modeling and analytics, developing models for marketing, finance, and other business applications.
Curriculum
Core Courses (8 units)
Course | Title |
---|---|
MSDS 400-DL | Math For Data Scientists |
MSDS 401-DL | Applied Statistics with R |
MSDS 402-DL | Data Science and Research Practice 1 |
or MSDS 403-DL | Data Science and Digital Transformation |
MSDS 420-DL | Database Systems and Data Preparation |
MSDS 422-DL | Practical Machine Learning |
MSDS 460-DL | Decision Analytics |
MSDS 475-DL | Project Management |
or MSDS 480-DL | Business Leadership and Communications |
or MSDS 485-DL | Data Governance, Ethics, and Law |
MSDS 498-DL | Capstone Class |
or MSDS 590-DL | Thesis Research |
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Which course should students take?
- Students without a background in data science should select MSDS 402-DL Data Science and Research Practice.
- Students with a background in data science should select MSDS 403-DL Data Science and Digital Transformation. Students who have at least two years’ experience in the field and have or had a title, such as data scientist, data analyst, statistician, data engineer, business analyst, etc. should select this course.
Specialization Courses (4 units)
Course | Title |
---|---|
MSDS 410-DL | Supervised Learning Methods |
MSDS 411-DL | Unsupervised Learning Methods |
Any two electives | |
Times Series Analysis and Forecasting | |
Python for Data Analysis | |
Data Engineering with Go | |
Foundations for Data Engineering | |
Analytics Application Engineering | |
Analytics Systems Engineering | |
Real-Time Interactive Processing and Analytics | |
Real-Time Stream Processing and Analytics | |
Marketing Analytics | |
Financial Machine Learning | |
Web and Network Data Science | |
Natural Language Processing | |
Applied Probability and Simulation Modeling | |
Data Visualization | |
Sports Performance Analytics | |
Sports Management Analytics | |
Artificial Intelligence and Deep Learning | |
Knowledge Engineering | |
Computer Vision | |
Intelligent Systems and Robotics | |
Technology Entrepreneurship | |
Management Consulting | |
Accounting and Finance for Technology Managers | |
Business Process Analytics | |
Data Governance, Ethics, and Law | |
Special Topics in Data Science | |
Independent Study |
About the Final Project
As their final course in the program, students take either a master's thesis project in an independent study format or a classroom final project class in which students integrate the knowledge they have gained in the core curriculum in a team project approved by the instructor. In both cases, students are guided by faculty in exploring the body of knowledge of data science. The master’s thesis or capstone class project count as one unit of credit.
Course | Title |
---|---|
Choose one | |
Capstone Class | |
Thesis Research |