M.A. in Early Christian Studies
The M.A. in Early Christian Studies at The Catholic University of America is an interdi...
M.S. in Data Analytics
Washington, D.C
INTAKE: Jan & Aug
Program Overview
The M.S. in Data Analytics at The Catholic University of America is a 30-credit-hour graduate program designed to equip students with advanced analytical skills for the rapidly growing fields of data science and data analytics. Housed within the Department of Electrical Engineering and Computer Science, and offered in partnership with other departments like Mathematics, Library & Information Sciences, and the Busch School of Business, this interdisciplinary program merges computing, statistics, machine learning, and human-computer interaction. It caters to individuals from diverse backgrounds, including business, engineering, healthcare, and the physical or social sciences, preparing them to collect, process, analyze, visualize, and interact with data to create useful knowledge.
STEM Designated: Yes, the M.S. in Data Analytics program at The Catholic University of America is STEM-designated. This is a significant advantage for international students, as it allows them to apply for an extension of their Optional Practical Training (OPT) in the United States, providing additional work authorization after graduation. The STEM designation reflects the program's strong scientific, technological, engineering, and mathematical core.
ABET Accredited: While several undergraduate engineering and computer science programs at The Catholic University of America are ABET accredited, the M.S. in Data Analytics program is not specifically listed as ABET accredited. ABET accredits programs at the college and university level in applied and natural science, computing, engineering, and engineering technology, but not all graduate programs fall under its direct accreditation. Students interested in ABET accreditation for a specific program should always verify directly with the university or ABET.
Curriculum: The 30-credit curriculum for the M.S. in Data Analytics includes core courses, elective courses, and a capstone practicum. Core courses (12 credits) typically cover: DA 501: Introduction to Data Science and Python, DA 514: Applied Statistics and Data Analysis, DA 515: Introduction to Machine Learning, and DA 591: Data Science and Analytics Practicum. The practicum involves tackling real-world data challenges in partnership with external organizations, culminating in a professional project presentation. Students then select 18 credits from a variety of elective courses, which may include topics like Data Visualization with Tableau, Decision Analysis, Web Design & Programming, Applications of Data Analytics and Development, Business Data Analytics, Fundamentals of Neural Networks, Introduction to Computer Vision, Introduction to Database Management, and Introduction to Deep Learning. The curriculum is developed with guidance from industry leaders, such as Booz Allen Hamilton, to ensure relevance to workforce needs.
Research Focus: The program's research focus is highly practical and applied, emphasizing the use of data analytics tools and methods to solve real-world problems. It trains students to extract useful information from complex datasets, apply statistical modeling, and implement data-driven decision-making. While not typically a thesis-based program in the traditional sense, the capstone practicum serves as a significant research component, allowing students to engage in hands-on data analysis projects with industry, government, or civic organizations. The program aims to bridge the gap between theoretical knowledge and practical application, preparing students to design and implement analytical infrastructure, models, and modeling techniques.
Industry Engagement: The M.S. in Data Analytics program at Catholic University boasts strong industry engagement. Its curriculum is developed with guidance from lead industry partners like Booz Allen Hamilton, ensuring that it meets the workforce needs of businesses, government agencies, and non-profit organizations. The program's location in Washington, D.C., provides unparalleled opportunities for students to connect with a vast network of potential employers in the federal government, technology sector, and various industries that increasingly rely on data insights. The capstone practicum directly involves students in real-world data challenges in partnership with industry, government, and civic organizations, providing invaluable practical experience and networking opportunities.
Global Perspective: The M.S. in Data Analytics program inherently incorporates a global perspective by addressing the universal need for data-driven decision-making in an increasingly interconnected world. The tools, techniques, and methodologies taught in the program (e.g., machine learning, statistical analysis, data visualization) are globally applicable across diverse industries and geographic regions. The challenges and opportunities presented by big data, artificial intelligence, and predictive analytics are global phenomena, and the program prepares graduates to contribute to solutions on an international scale. The diverse student body and faculty further enrich the learning environment with varied international viewpoints and applications of data analytics.
Location
Washington, D.C
Score
IELTS 6.5
Tuition Fee
USD 23500
Postgraduate Entry Requirements
Academic Qualifications: Applicants for postgraduate programs typically require a minimum academic achievement of 65% 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 80 or higher.
- PTE: Overall score of 58 or higher.
- DET (Duolingo English Test): Minimum score of 105.
The Catholic University of America (CUA) offers a variety of scholarship opportunities to support international students in their pursuit of higher education. These scholarships are designed to recognize academic excellence, leadership potential, and financial need, making CUA an attractive destination for talented students from around the world.
Merit-Based Scholarships: CUA provides merit scholarships to international students based on their academic achievements and overall profile. These scholarships can significantly reduce tuition costs and are often renewable each year, provided students maintain satisfactory academic performance. Scholarships are awarded automatically during the admission process or through a separate application depending on the program.
Need-Based Financial Aid: While need-based financial aid for international students is limited due to federal regulations, CUA offers institutional grants and assistance based on demonstrated financial need. International students are encouraged to provide comprehensive financial documentation to be considered for these awards.
Program-Specific Scholarships: Certain departments and colleges within CUA offer specialized scholarships for international students pursuing specific fields such as theology, law, engineering, or music. These awards often recognize outstanding talent or the discipline and may include research stipends or assistantships.
Graduate Assistantships and Fellowships: Graduate international students have access to assistantships and fellowships that provide tuition waivers and stipends in exchange for teaching, research, or administrative support. These opportunities not only help finance education but also provide valuable professional experience.
External Scholarships and Resources: CUA encourages international students to seek scholarships from external organizations, foundations, and government programs in their home countries. The university’s financial aid office provides guidance on identifying and applying for such opportunities.
Graduates of The Catholic University of America's M.S. in Data Analytics program are highly sought after due to their specialized skills in data analysis, statistical modeling, machine learning, and data visualization. Their ability to extract actionable insights from data makes them valuable assets across nearly every industry.
Data Scientist: Developing and implementing algorithms and models to analyze large, complex datasets, identifying trends, and making predictions for various industries.
Data Analyst: Collecting, processing, and performing statistical analyses of data to identify insights, support decision-making, and create reports and visualizations.
Business Intelligence (BI) Analyst: Designing and building dashboards, reports, and data models that provide insights into business performance and support strategic planning.
Machine Learning Engineer: Designing, building, and deploying machine learning models and systems to automate tasks, improve processes, and drive innovation.
Analytics Consultant: Advising businesses and organizations on how to leverage data for improved decision-making, operational efficiency, and competitive advantage.
Data Engineer: Designing, constructing, installing, and maintaining large-scale data processing systems and pipelines to ensure data availability and quality for analysts and scientists.
Marketing Analyst: Using data to understand customer behavior, evaluate campaign effectiveness, optimize marketing strategies, and personalize customer experiences.
Financial Data Analyst: Analyzing financial data to identify market trends, assess risks, develop investment strategies, and support financial forecasting for banks, investment firms, or corporations.
Healthcare Data Analyst: Analyzing patient data, clinical trials, and healthcare operational data to improve patient outcomes, optimize resource allocation, and identify public health trends.
Government Data Specialist: Working for federal, state, or local government agencies to analyze public policy data, demographic trends, operational efficiency, and support data-driven governance initiatives.
