Data Science Southern Methodist University

Overview

The Data Science programme at Southern Methodist University provides a comprehensive foundation in the analytical and computational techniques required to interpret complex global information. This undergraduate major focuses on the intersection of statistical methods, computer science, and mathematical optimization to prepare students for a data-driven world.

Students enrolled in this degree are required to pursue a second major in another discipline, reflecting the highly interdisciplinary nature of the field. This structure ensures that graduates can apply their technical expertise to diverse areas such as engineering, humanities, social sciences, or management science. A minimum of 36 credit hours is dedicated specifically to the major requirements.

Why Data Science at Southern Methodist University?

Southern Methodist University offers a unique academic environment where rigorous theoretical learning is integrated with hands-on experience. The institution emphasises a singular approach to education that encourages students to forge their own paths through dual-major requirements and flexible elective choices across multiple departments.

The programme benefits from the resources of the Dedman College of Humanities and Sciences and the Lyle School of Engineering. Students have access to diverse facilities and a community dedicated to innovative problem-solving. This collaborative atmosphere ensures that graduates are in high demand across various professional sectors and industries.

Tuition Fee Breakdown

• International fee: USD 30940 per semester
• National fee: USD 30940 per semester
• Local fee: USD 30940 per semester

Visit the Fees and Funding section for a breakdown in your local currency.

Syllabus

Modules may include:

Mathematics

• Calculus I
• Calculus II
• Consolidated Calculus
• Linear Algebra

Data Science Core

• Practical Data Science
• Statistical Methods
• Computing Concepts
• Computer Science for Data Science
• Computer Science

Co-Curricular Ethics

• Computing Technology: Historical and Ethical Perspectives
• Cultural and Ethical Implications of Technology
• Technology, Society, and Value

Advanced Statistics

• Economic and Business Forecasting
• Statistical Design and Analysis of Experiments
• Applied Regression Analysis and Data Visualization
• Survey Sampling
• Nonparametric Statistics

Machine Learning

• Machine Learning in Python
• Adaptive Algorithms for Machine Learning
• Data Mining
• Mathematics of Machine Learning

Advanced Methodology

• Statistical Methods for Engineers and Applied Scientists
• File Organization and Database Management
• Information Retrieval and Web Search
• Cloud Computing
• Information Engineering
• Discrete Event Simulation
• Information System Architecture
• Information System Design Strategies
• Digital History with Data Science
• Mathematical Modeling and Applications
• Applied Time Series Analysis

Advanced Electives

• Database Concepts
• Information Assurance and Security
• Algorithms
• Artificial Intelligence
• Mobile Applications for Sensing and Learning
• XML and the Enterprise
• Business Modeling with Spreadsheets
• Data Analytics for Accounting
• Supply Chain and Logistics Management
• Business Intelligence and Big Data
• Project Management
• Calculus III: Multi–Variable and Vector Calculus
• Scientific Computing
• Analytics for Decision Support
• Management of Information Technologies
• Statistical Learning via Sports Analytics

Capstone Options

• Creative Computing Major Capstone
• Senior Design
• Data Science Capstone

Careers with Data Science

Graduates of this programme are prepared for high-demand roles that require the integration of data science with other professional fields. The curriculum supports career paths in management science, engineering, and the social sciences. Students often find opportunities in industries focused on business intelligence, data mining, and information technology.

The dual-major requirement enhances employability by providing students with a specific domain of expertise alongside their technical data skills. Common career outcomes include roles in data analysis, systems design, and strategic consultancy. Graduates are equipped to handle the ethical and technical challenges of modern technological organisations.