Curriculum
The MS-DS is an online data science master's degree designed to prepare the next generation of data scientists to successfully work and collaborate with others across a variety of scientific, business, and other fields.
Academic Overview
Data science is a multidisciplinary field that focuses on the extraction of knowledge and insight from large datasets. Data scientists are tasked with using a range of skills in applied mathematics and statistics, computer science, and applications.
The MS-DS provides learners with a strong foundation in acquiring, cleaning, and managing data. You will learn to analyze large datasets using data mining and machine learning techniques. You will also design, conduct, and run statistical experiments and models; draw rational conclusions from data using probability theory and statistics; and more.
As a graduate of the MS-DS program, you will be well-prepared to apply data science skills to a specific domain area. You will be able to clearly communicate the results of a data science analysis to a non-technical audience; structure effective meetings and projects using collaboration skills; and act ethically in the role of professional data scientist.
Coursework
You will complete 21 credits of core coursework in statistics, computer science, and foundational concepts as well as 9 credits of elective coursework. You may complete courses in any order, but we suggest following one of our recommended learner journeys below.
Statistics Learner Journey
If you are skilled in statistics, we recommend you complete your courses in the following order:
3 credits
- ​DTSA 5001 Probability Theory: Applications for Data Science
- DTSA 5002 Statistical Inference for Estimation in Data Science
- DTSA 5003 Hypothesis Testing for Data Science
4 credits
- DTSA 5301 Data Science as a Field
- DTSA 5302 Cybersecurity for Data Science
- DTSA 5303 Ethical Issues in Data Science
- DTSA 5304 Fundamentals of Data Visualization
14 core credits | 9 elective credits​
Complete your remaining courses in any order:
- Data Science Foundations: Data Structures & Algorithms Courses (3 credits)
- Statistical Modeling for Data Science Courses (3 credits)
- Data Mining: Foundations & Practice Courses (3 credits)
- Machine Learning Courses (3 credits)
- Databases Courses (2 credits)
- Data Science Elective Courses (9 credits)
Computer Science Learner Journey
If you are skilled in computer science, we recommend you complete your courses in the following order:
3 credits
- DTSA 5501 Algorithms for Searching, Sorting & Indexing
- DTSA 5502 Trees and Graphs: Basics
- DTSA 5503 Dynamic Programming, Greedy Algorithms
4 credits
- DTSA 5301 Data Science as a Field
- DTSA 5302 Cybersecurity for Data Science
- DTSA 5303 Ethical Issues in Data Science
- DTSA 5304 Fundamentals of Data Visualization
14 core credits | 9 elective credits
Complete your remaining courses in any order:
- Data Science Foundations: Statistical Inference Courses (3 credits)
- Statistical Modeling for Data Science Courses (3 credits)
- Data Mining Foundations & Practice Courses (3 credits)
- Machine Learning Courses (3 credits)
- Databases Courses (2 credits)
- Data Science Elective Courses (9 credits)
Looking for something different? Design your own learner journey! Start with whatever interests you most — perhaps a core course on data mining or an elective on high-performance computing. Then, complete all three courses within one of our pathway specializations with a cumulative 3.0 GPA or better when you're ready to earn admission to the program. Credits you earn before admission will apply toward the degree. Note that you must complete all courses within 8 years.
Find Data Science Courses
Get started today!
No application required. Click "Enroll Now" below to complete the registration form, pay tuition and start learning right away. Consider starting with a pathway course. Pathways are a series of three 1-credit courses with a focus on either statistics or computer science.