# Graduate

### Learning Outcomes

- The ability to manage large data sets in preparation for data science analysis
- A working knowledge of traditional statistical techniques and the ability to apply these methods to a wide array of real world problems.
- The ability to perform a data science analysis from beginning to end while adhering to the principles of reproducible research.
- The ability to program in both the R and Python programming languages.
- Complete a project demonstrating competence in the field of data science.
- Non-thesis track: Students will be required to complete a real world data science project prior to graduating from this program, either through our consulting course, an internship, an independent study, or other appropriate project
- Thesis track: Students will be required to undertake a research project culminating in a thesis

### Curriculum (Effective Fall 2022)

**Non-thesis Track**

#### Statistics Requirements

#### Computer Science Requirements

#### Statistics and Computer Science Eletives

- Statistics Elective: STAT4XX
- Computer Science Elective: COMP4XX
- Statistics or Computer Science Elective: STAT4XX/COMP4XX

#### Data Science Core

- Introduction to Data Science: DSCI401 (4 credits)
- One of the following two courses:
- Predictive Analytics: STAT488
- Machine Learning: COMP479

- Data Science Consulting (capstone): DSCI470 (2 credits)

**Note:** 30 total credit hours

**Thesis Track**

#### Statistics Requirements

#### Computer Science Requirements

#### Data Science Core

- Introduction to Data Science: DSCI401 (4 credits)
- One of the following two courses:
- Predictive Analytics: STAT488
- Machine Learning: COMP479

#### Research

- Introduction to Data Science Research: DSCI494 (2 credits)
- Data Science Research: DSCI499 (8 credits)
- Data Science Thesis: DSCI595 (1 credit)

**Note:** 30 total credit hours

### Learning Outcomes

- The ability to manage large data sets in preparation for data science analysis
- A working knowledge of traditional statistical techniques and the ability to apply these methods to a wide array of real world problems.
- The ability to perform a data science analysis from beginning to end while adhering to the principles of reproducible research.
- The ability to program in both the R and Python programming languages.
- Complete a project demonstrating competence in the field of data science.
- Non-thesis track: Students will be required to complete a real world data science project prior to graduating from this program, either through our consulting course, an internship, an independent study, or other appropriate project
- Thesis track: Students will be required to undertake a research project culminating in a thesis

### Curriculum (Effective Fall 2022)

**Non-thesis Track**

#### Statistics Requirements

#### Computer Science Requirements

#### Statistics and Computer Science Eletives

- Statistics Elective: STAT4XX
- Computer Science Elective: COMP4XX
- Statistics or Computer Science Elective: STAT4XX/COMP4XX

#### Data Science Core

- Introduction to Data Science: DSCI401 (4 credits)
- One of the following two courses:
- Predictive Analytics: STAT488
- Machine Learning: COMP479

- Data Science Consulting (capstone): DSCI470 (2 credits)

**Note:** 30 total credit hours

**Thesis Track**

#### Statistics Requirements

#### Computer Science Requirements

#### Data Science Core

- Introduction to Data Science: DSCI401 (4 credits)
- One of the following two courses:
- Predictive Analytics: STAT488
- Machine Learning: COMP479

#### Research

- Introduction to Data Science Research: DSCI494 (2 credits)
- Data Science Research: DSCI499 (8 credits)
- Data Science Thesis: DSCI595 (1 credit)

**Note:** 30 total credit hours