M.S. in Management of Information and Technology
The M.S. in Management of Information and Technology at OU is designed for individuals ...
M.S. in Data Science and Analytics
Norman, Oklahoma
INTAKE: Jan & Aug
Program Overview
The M.S. in Data Science and Analytics at OU is a 33-credit-hour program that can be completed in as little as 14 months (full-time, with a summer start) or typically around 24 months. It is offered both on-campus and fully online, providing significant flexibility for both traditional students and working professionals. The program merges expertise from the Schools of Computer Science and Industrial and Systems Engineering, ensuring a comprehensive understanding of data management, technology, and analytics. Graduates will develop skills to design and build tools for data extraction, assimilation, and analysis, along with the systems understanding to predict and enhance performance for enterprises in both private and public sectors.
STEM Designated: Yes, the Master of Science in Data Science and Analytics at the University of Oklahoma is a STEM-designated program. This designation is highly advantageous for international students, as it qualifies them for a 24-month Optional Practical Training (OPT) extension beyond the initial 12 months, totaling up to 36 months of work authorization in the U.S. after graduation. This reflects the program's rigorous quantitative, analytical, and technical focus, aligning it with in-demand fields in science, technology, engineering, and mathematics.
Curriculum: The curriculum for the M.S. in Data Science and Analytics provides a robust foundation in core data science concepts and advanced analytical techniques. Core courses (24 credit hours) typically include "Computing Structures," "Algorithm Analysis," "Advanced Analytics and Metaheuristics," "Fundamentals of Engineering Statistical Analysis," "Database Management Systems," "Intelligent Data Analytics," and a "Professional Practice" course. Students also complete 9 credit hours of electives, allowing for specialization in areas of interest. The program emphasizes in-demand skills such as advanced programming (Java or C++ preferred prerequisites), statistical modeling, data mining, and data visualization.
Research Focus: Research is a vital component of the Data Science and Analytics Institute at OU, which aims to merge the creation and dissemination of knowledge with the development of technologies. While the M.S. program is primarily coursework-based for the non-thesis option, students are exposed to and can engage with faculty research in areas such as machine learning for data science, big data engineering, applied data science projects, and advanced algorithms. The institute's focus on extracting knowledge from data that arises in various application domains (e.g., aerospace, healthcare, finance) ensures the curriculum is informed by cutting-edge developments and prepares students for careers that may involve research and development.
Industry Engagement: The University of Oklahoma's Data Science and Analytics Institute (OU DSAI) has strong industry engagement, which is key to successful knowledge transfer from academia to the real world. OU DSAI collaborates with industry partners through various avenues, including professional development seminars (e.g., Applied Machine Learning, Data Visualization with Python), sponsored research projects, and industry practicum and internship opportunities. These engagements allow students to work on real-world complex datasets and apply their skills under the mentorship of both DSAI faculty and industry professionals. This strong connection ensures the curriculum remains industry-relevant and provides graduates with a head start in transitioning from the classroom to the professional world.
Global Perspective: The M.S. in Data Science and Analytics at the University of Oklahoma inherently incorporates a global perspective, as data science and analytics are universally applicable disciplines in our increasingly interconnected world. Organizations worldwide leverage data to drive decision-making, characterize customers, predict risks, and improve efficiencies. The program prepares graduates to understand and work with diverse types of data from various global contexts, considering issues of data security, privacy, and ethics that vary across international boundaries. OU's diverse student body and faculty further enrich this global outlook, fostering a learning environment where students can collaborate on projects with international relevance and prepare for careers in a globalized data-driven economy.
Location
Norman, Oklahoma
Score
IELTS 6.5
Tuition Fee
USD 22582
Postgraduate Entry Requirements
Application Fee: $100
Academic Qualifications: Applicants for postgraduate programs typically require a minimum academic achievement of 70% or above in their bachelor's degree.
English Language Proficiency:
- IELTS: Overall band score of 6.5 or 7.0 with a minimum of 6.0 in each component.
- TOEFL: Overall score of 79 or higher.
- PTE: Overall score of 60 or higher.
The University of Oklahoma offers a variety of scholarships specifically designed to support international students in achieving their academic goals. These scholarships aim to recognize outstanding academic achievements, leadership qualities, and contributions to the campus community while helping to make education more affordable for students from around the world.
Merit-Based Scholarships: OU awards several merit-based scholarships to incoming international undergraduate students based on academic excellence, standardized test scores, and extracurricular accomplishments. These scholarships can significantly reduce tuition costs and are automatically considered during the admissions process or require a separate application.
International Ambassador Scholarship: This prestigious scholarship recognizes students who demonstrate strong leadership skills, a commitment to cultural exchange, and active participation in university life. Recipients often serve as ambassadors for OU’s international community, promoting diversity and inclusion.
Graduate Fellowships and Assistantships: For international graduate students, OU offers numerous fellowships, research assistantships, and teaching assistantships. These positions provide financial support in the form of tuition waivers and stipends while offering valuable professional experience in academic and research settings.
Country-Specific Scholarships: OU periodically partners with governments, foundations, and organizations to offer scholarships targeting students from specific countries or regions. These scholarships foster international collaboration and cultural exchange.
External Scholarships and Financial Aid Resources: In addition to university-funded awards, OU provides guidance and resources to help international students find external scholarships, grants, and financial aid opportunities available through private organizations, embassies, and international agencies.
A Master of Science (M.S.) in Data Science and Analytics from the University of Oklahoma (OU) provides graduates with a highly sought-after skill set to navigate the complex world of "Big Data." This STEM-designated program, a collaborative effort between the Schools of Computer Science and Industrial and Systems Engineering within the Gallogly College of Engineering, focuses on equipping students with advanced analytical models, methods, and the technical prowess to extract actionable insights from vast datasets. Graduates are prepared to drive data-driven decision-making across a multitude of industries.
Data Scientist: The quintessential role for this degree, involving the application of statistical modeling, machine learning algorithms, and programming skills (Python, R) to analyze complex data, build predictive models, identify patterns, and communicate findings to inform business strategy and solve challenging problems.
Machine Learning Engineer: Specializing in the design, development, and deployment of machine learning models and AI systems. This role often involves building robust data pipelines, optimizing algorithms, and integrating ML solutions into existing software applications.
Data Analyst (Advanced): Focusing on the collection, cleaning, processing, and interpretation of data to identify trends, create reports, and provide insights that support operational and strategic decision-making. With an M.S., these roles often involve more complex analyses and greater responsibility.
Business Intelligence (BI) Developer/Analyst: Designing and implementing BI solutions, including dashboards, reports, and data visualizations, to help organizations track performance, identify trends, and make informed business decisions. They bridge the gap between raw data and actionable business insights.
Data Engineer: Building and maintaining the infrastructure and pipelines necessary for data collection, storage, processing, and access. They ensure data quality, reliability, and efficient flow for data scientists and analysts, often working with big data technologies like Hadoop and Spark, and cloud platforms.
Quantitative Analyst (Quant): Applying advanced mathematical, statistical, and computational methods to analyze financial markets, develop trading strategies, manage risk, and price complex financial instruments. This role is prominent in investment banks, hedge funds, and financial institutions.
Analytics Consultant: Working with clients across various industries to understand their business problems, leverage data to find solutions, and implement data-driven strategies. They provide expert advice on data analytics best practices, tool selection, and model deployment.
Operations Research Analyst: Using advanced analytical and optimization techniques to improve efficiency, reduce costs, and enhance decision-making in complex operational systems, such as supply chains, logistics, resource allocation, and scheduling.
Product Analyst (Data-focused): Working within product teams to analyze user behavior, product performance metrics, and market trends to inform product development, feature prioritization, and strategic direction. They use data to optimize the product lifecycle.
Research Scientist (Data Science): Conducting cutting-edge research in academia, government labs, or R&D departments of major tech companies to develop new data science methodologies, algorithms, and applications, contributing to the theoretical and practical advancements of the field.
