The online Master of Science in Data Science with a Machine Learning specialization from Southern Methodist University (SMU) is a 33.5-credit program that can be completed in 20–28 months.
Designed for working professionals, it combines live online classes, self-paced coursework, and on-campus immersion experiences. Students attend weekly sessions in small seminar-style classes that support collaborative learning and faculty interaction.
Accreditation and Outcomes
The program is accredited by the Southern Association of Colleges and Schools Commission on Colleges (SACSCOC). Graduates are prepared for advanced data science roles, including data architect, machine learning engineer, and principal software engineer.
The curriculum develops both technical and strategic decision-making skills with a focus on practical applications.
Curriculum
The core curriculum covers statistical foundations, data wrangling, machine learning, database management, and data visualization. Key courses include Machine Learning I & II, Natural Language Processing, and Applied Statistics.
Statistical Foundations for Data Science
This course introduces statistical concepts through experimental design. Students focus on analyzing and interpreting statistical data, rather than performing calculations. Topics include sampling methods, T-tests, ANOVA, regression, and ethical data practices.
Doing Data Science
Students gain hands-on experience with the full data science workflow using industry-standard tools. Topics include data wrangling, exploratory analysis, machine learning, and time series forecasting. Real-world examples connect theory to application.
Applied Statistics: Inference and Modeling
This course builds on foundational statistics to explore multivariate data analysis and basic machine learning techniques. Topics include regression, discriminant analysis, clustering, and bootstrap methods. Emphasis is placed on interpreting results over computation.
File Organization and Database Management
Students learn to design, implement, and manage relational and NoSQL databases. The course covers query languages, database architecture, and storage techniques. Projects include hands-on work using SQL, MySQL, MongoDB, and Python.
Machine Learning I
Students explore core machine learning techniques, including classification, regression, clustering, and recommender systems. Emphasis is on practical application with tools like Python, R, and scikit-learn. Coursework includes data preparation, model design, and evaluation.
Quantifying the World
This course focuses on large-scale data collection, storage, and initial analysis. Students use APIs and scripting to gather and process data, then apply basic statistics and visualization to validate findings. Tools include Python, R, SQL, and NoSQL.
Machine Learning II
Students study advanced machine learning topics such as deep learning, supervised and unsupervised classification, and complex regression models. The course includes hands-on feature engineering and model validation using Matlab, Python, and R.
Natural Language Processing
This course teaches techniques for extracting insights from unstructured text data. Topics include text classification, tagging, semantic search, and normalization. Students work with real-world data sets using Python, SQL, and NLP libraries.
Specialization
Students choosing the Machine Learning specialization complete targeted coursework in areas such as supervised classification, unsupervised clustering, and deep learning. A capstone project and required immersions reinforce learning through real-world data challenges.
Admission and Prerequisites
Applicants must hold a bachelor’s degree and show proficiency in programming and quantitative skills. No GRE is required, though supplemental materials like math or programming addenda may be submitted. Credit for prior learning is available for qualified boot camp or MicroMasters participants, allowing up to 6 graduate credits to be applied toward the degree.
Online Learning and Support
All classes are delivered through SMU’s online platform, with 24/7 access and mobile compatibility. Students receive academic advising, technical support, and career services throughout the program. Required on-campus immersions in Texas offer hands-on workshops, industry networking, and capstone presentations.
Tuition
The total tuition is approximately $37,855. Students may apply for federal financial aid, and military education benefits are accepted. Employer sponsorship and payment plans are also available.
Other Options at SMU
SMU also offers a Business Analytics specialization within the same program. Both specializations allow students to tailor their education to fit career goals in data science, machine learning, and business intelligence.