DataScience@SMU is an online Master of Science in Data Science program designed for current and aspiring data science professionals looking to gain the advanced skills needed to manage, analyze, mine and understand complex data to make strategic decisions in their organizations. Through a combination of interactive coursework, collaborative group activities and online face-to-face classes, students gain the technical, analytical and communication skills needed to make meaningful data-driven decisions across various industries.
A Skills-Based Curriculum
DataScience@SMU’s interdisciplinary curriculum draws from SMU’s Dedman College of Humanities and Sciences, Lyle School of Engineering and Meadows School of the Arts. Classes and coursework focus on statistics while building and expanding upon computer science and data visualization skills. As students progress in the program, they master more advanced concepts and have the opportunity to choose a specialization with a customized curriculum that closely aligns with their goals.
By choosing an area of specialization, students can master necessary skill sets and apply them directly to their career interests.
Machine Learning Specialization
Master the machine learning techniques needed to build self-optimizing systems and provide solutions to problems or improve processes in any organization. Learn more.
Business Analytics Specialization
Master the analytical tools required to synthesize qualitative data and effectively communicate results to key stakeholders to inform strategic decision-making. Learn more.
Analyzes machine learning and the data preparation workflow, including multivariate nonlinear nonparametric regression, supervised classification, unsupervised classification and deep learning. All material covered is reinforced through hands-on experiences using state-of-the-art tools to design and execute data mining processes using Python and R.
Natural Language Processing
Explores natural language processing (NLP) as applied to data mining, text mining and machine learning tasks with unstructured big data. Topics include document clustering and classification, automated tagging and highlighting, semantic search and text normalization to support machine learning applications. Students will gain experience building solutions from real-world data sets, utilizing WordNet and the data of some leading websites.
Examines the practical applications of the use of data sciences in the application of econometrics and quantitative finance. The primary learning framework is based on utilization of real and simulated data sets for business and economic situations. Students become familiar with the use of R in the creation of data analysis combining financial theory and statistical analysis, including portfolio theory, CAPM and econometric modeling.
DataScience@SMU gives you the ability to earn your degree and advance your career without relocating. As a student in the program, you are a full member of the SMU community with access to high-quality curriculum, faculty and resources.
Develop relationships with industry professionals, faculty and peers while working on group projects, networking at in-person immersions and engaging in discussion during live classes.
Stay connected through our virtual campus. Easily access your coursework from any Internet-enabled device and attend live, weekly classes. With a small student-to-faculty ratio, each class fosters rich, engaging discussions.
Receive dedicated student support with tutoring, refresher courses, and academic and career services to ensure your personal and professional goals are met.
DataScience@SMU at a Glance
Designed for working professionals, the program provides flexibility that enables you to maintain current responsibilities while earning your degree in two years or less. GRE waivers are available for students with five or more years of professional experience.
20–28 months to complete the program
33.5 credits of coursework
3 start dates per year
Apply by March 9, 2018 to join the May 2018 cohort.