Machine Learning Specialization
Master the machine learning techniques needed to build self-optimizing systems and provide solutions to problems or processes in any organization. Learn more about the Machine Learning Specialization.
Academics Overview
The online master of science in data Science program from Southern Methodist University equips data-driven professionals with the skills required to generate measurable impact in their business or organization. Graduates of this program will master the concepts and tools required to effectively mine, manage and analyze unstructured data and to clearly communicate results and solutions to inform strategy in organizations.
Developed by esteemed practitioners in the data science field, coursework outlines key learning objectives through practical application and provides students the opportunity to incorporate key concepts and tools into their current role. Students build upon what they learn in their courses by working on case studies, participating in compelling workshops and engaging with industry professionals.
Students can customize their curriculum and choose an area of specialization for their elective coursework. Adding a specialization allows students to focus on mastering disciplines aligned with their career interests and to further deepen the specific skill sets needed in their organization.
Areas of Specialization
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 about the Business Analytics Specialization.
Program Requirements
The 33-credit program can be completed in as little as 20 months:
- 20 core credits
- 8 elective credits
- 2 immersion experience credits
- 3 capstone credits
Here are sample course schedules for both 5-Term and 7-Term tracks (PDF, 79kb)
Why DataScience@SMU?
Employers are seeking data-driven candidates with the advanced technical skills and business acumen to make strategic, informed decisions in today’s global business environment.
To meet the demand, DataScience@SMU features an interdisciplinary curriculum that draws from three SMU schools: Dedman College of Humanities and Sciences, Lyle School of Engineering and Meadows School of the Arts. Students apply the tools and methods used by data science professionals to complete project-based assignments and participate in real case studies that solve organizational challenges.
Our students cultivate the skills necessary to meet the current high demand for data science professionals in various industries. The program features corporate networking opportunities and career services that allow students to advance their career while working toward completing their degree.
Students become equipped with the critical tools needed to make well-informed decisions and immediately apply what they learn to their career. Students can choose an optional Machine Learning Specialization or Business Analytics Specialization to gain a more in-depth competency related to their interests and become sought-after professionals in the field of data science.
“The program provides excellent breadth and depth on skills and tools required for a data scientist. So far, I’ve learned a total of 30 different programming languages and data analytics packages. The machine learning, classification, clustering and recommender system skills I’ve acquired have been outstanding.”
Learning Objectives
The online Master of Science in Data Science program matches the academic rigor and standards of SMU’s on-campus programs. Students learn techniques to effectively manage and analyze data and make strategic decisions. SAS, Python and R programming languages are used as a foundation to cultivate technical skills in areas that include statistical analysis, programming, data mining and network security.
The interdisciplinary curriculum is designed for students to develop and strengthen the following skills:
Statistical Analysis – Students will learn experimental design and methods, including developing the statistical techniques to form relevant questions, collect and analyze appropriate data and make informed decisions.
Technical Skills – Students will cultivate technical skills in statistical analysis, programming, data mining, machine learning, database management and data and network security. Both Python and R programming languages are used extensively throughout the program as the foundation of these technical skills’ applications.
Visualization and Communication – Students will not only develop the oral and written communication skills to successfully summarize and present results to non-experts in various industries, but they will also learn several approaches to data visualization, including visual and information design principles and creative coding.
Applied Data Science – Students will take an interdisciplinary approach to the practical application of analytic and mathematical principles, bringing together methods, concepts and practices in the data science field.
