Prerequisites: completed bachelor degree (required) plus college level courses in statistics, linear algebra, and coding in any language, e.g. python or R (strongly recommended).

A total of 10 courses (30 credits) are required:

DSCI 6000 Applied statistics and data science (3 credits)

DSCI 6100 Programming for data scientists (Python) (3 credits)

DSCI 6200 Data science and machine learning (3 credits)

DSCI 6300 Data visualization (3 credits)

DSCI 6400 Ethics in data science and artificial intelligence (3 credits)

CYBS 6020 Cloud Computing Platforms, Applications, and Data Security (3 credits)

DSCI 6600 Data wrangling with SQL (3 credits)

DSCI 6700 Text mining and unstructured data (3 credits)

DSCI 6800 Artificial Intelligence and machine learning (3 credits)

DSCI 7000 Data Science Capstone (3 credits)

Suggested sequence due to course dependency (assuming full time and 8 week terms)

Term 1 *

Term 2 **

Term 3 ***

Term 4 ****

Term 5 *****

Term 6

DSCI 6000 Applied statistics and data science

DSCI 6100 Programming for data scientists

DSCI 6300 Data visualization

DSCI 6200 Data science and machine learning

DSCI 6400 Data ethics

CYBR 6020 Cloud Computing Platforms, Applications, and Data Security

DSCI 6600 Data wrangling with SQL

DSCI 6700 Text mining and unstructured data

DSCI 6800 AI and machine learning

DSCI 7000 Capstone project

* “DSCI 6000 Applied statistics and data science,” which introduces the classical, Bayesian, and DSCI perspectives to data analytics, is the foundational course for many subsequent courses.

** “DSCI 6300 Data visualization” focuses on pattern recognition and spatial perception, and thus does not require a strong foundation in math. “6100 Programming for data scientists” requires background information of computing. Students can take “Introduction to computing” in the first term if this pre-requisite is not met.

*** “DSCI 6400 Ethics in data science and artificial intelligence” is social-oriented and philosophical, and therefore it does not require a strong foundation in math. “6600 Data science and machine learning” is built on the foundation of “Applied statistics and data science”. It covers both AI-based and non-AI-based methodologies.

**** “CYBR 6020 Cloud Computing Platforms, Applications, and Data Security” entails knowledge and skills of cutting-edge technology, necessitating a deep understanding of data analytics and high-performance computing. Similarly, all other elective courses are built upon robust fundamentals in either data analytics, computing, or both. This course is offered by CPS.

***** In DSCI 6600 SQL is utilized for extracting and managing structured data, while in 6700 text mining and various tools are utilized for parsing unstructured data. It is recommended to acquire skills in handling structured data before delving into the domain of unstructured data. The course "6800 AI and machine learning" specifically emphasizes AI-enabled techniques. Therefore, it is recommended that learners taking the "Data science and machine learning" course initially to gain a comprehensive understanding of all data science methods.

******* The capstone project requires students to integrate knowledge acquired throughout the entire program. It should not be taken with finishing at least 24 credit hours.

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Master of Science in Data Science

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