Post-Baccalaureate Certificate in Machine Learning for Data Science
Certificate Level: Graduate
Admission Requirements: Bachelor's degree
Certificate Type: Post-Baccalaureate
Number of Credits to Completion: 12.0
Instructional Delivery: Online, Campus
Calendar Type: Quarter
Expected Time to Completion: 1 year
Financial Aid Eligibility: Not aid eligible
Classification of Instructional Program (CIP) Code: 30.7001
Standard Occupational Classification (SOC) Code: 15-1132
About the Program
The Machine Learning for Data Science certificate program provides students with knowledge of advanced approaches to data analysis. Courses in this certificate equip students with an understanding of theoretical foundations as well as practical techniques associated with machine learning and related topics like recommender systems, deep learning, and natural language processing.
Admission Requirements
The post-baccalaureate certificate in Machine Learning for Data Science accepts applicants who hold bachelor's degrees from an accredited university and offers them an opportunity to learn a variety of foundational and applied data science topics. Please visit the School of Computer and Information Sciences website to learn more about admission requirements.
Additional Information
For more information about this program, please visit the School of Computer and Information Sciences website.
Program Requirements
| Required Core Courses | ||
| DSCI 631 | Applied Machine Learning for Data Science | 3.0 |
| DSCI 641 | Recommender Systems | 3.0 |
| Elective Courses | 6.0 | |
| Choose 2 courses from the following: | ||
| Programming Foundations | ||
or CS 501 | Introduction to Programming | |
| Data Structures and Algorithms | ||
or CS 521 | Data Structures and Algorithms I | |
| Introduction to Computer Vision | ||
| Machine Learning | ||
| Applications of Machine Learning | ||
| Deep Learning | ||
| Data Analysis at Scale | ||
| Quantitative Foundations of Data Science | ||
| Data Analysis and Interpretation | ||
| Natural Language Processing with Deep Learning | ||
| Modeling Natural Language | ||
| Social Network Analytics | ||
| Intelligent Search and Language Models | ||
| Total Credits | 12.0 | |
Sample Plan of Study
| First Year | ||
|---|---|---|
| Fall | Credits | |
| DSCI 631 | Applied Machine Learning for Data Science | 3.0 |
| Elective | 3.0 | |
| Credits | 6 | |
| Winter | ||
| DSCI 641 | Recommender Systems | 3.0 |
| Elective | 3.0 | |
| Credits | 6 | |
| Total Credits | 12 | |