Data Science, MS Technology Entrepreneurship Specialization

Entrepreneurship involves creating a new business or business function where one did not exist before. Advances in science and technology spur innovation, giving existing, resource-rich companies a chance to reinvent themselves, often moving into new markets. These advances, many of them emerging from data science, machine learning, and artificial intelligence, provide an opportunity for individuals and firms to build new organizations or startups. The technology entrepreneurship specialization shows students the path to building a successful, innovation-driven startup.

Curriculum

Core Courses (8 units)

Course Title
MSDS 400-DLMath For Data Scientists
MSDS 401-DLApplied Statistics with R
MSDS 402-DLData Science and Research Practice 1
or MSDS 403-DL Data Science and Digital Transformation
MSDS 420-DLDatabase Systems and Data Preparation
MSDS 422-DLPractical Machine Learning
MSDS 460-DLDecision Analytics
MSDS 475-DLProject Management
or MSDS 480-DL Business Leadership and Communications
or MSDS 485-DL Data Governance, Ethics, and Law
MSDS 498-DLCapstone Class
or MSDS 590-DL Thesis Research
1

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 470-DLTechnology Entrepreneurship
MSDS 474-DLAccounting and Finance for Technology Managers
Any two electives
Supervised Learning Methods
Unsupervised Learning Methods
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
Management Consulting
Business Process Analytics
Business Leadership and Communications
Data Governance, Ethics, and Law
Special Topics in Data Science