Master of Science in Data Analytics

1 Years On Campus Masters Program

George Washington University

Program Overview

The Master of Science in Data Analytics at The George Washington University offers an interdisciplinary education combining computer science, business analytics, and systems engineering to prepare students for careers in data science and analytics. Jointly administered by the Department of Computer Science and the Department of Engineering Management and Systems Engineering, the programme is designed for students interested in managing large datasets, developing analytical algorithms, applying machine learning, and using data-driven insights to improve decision-making across government, industry, and research organisations.

Curriculum Structure

The programme requires 30 credit hours, including 15 credits of core coursework and 15 credits of track-specific and elective courses. Students can choose between two specialisation tracks: Computer Science or Engineering Management and Systems Engineering.

Year 1 – Foundational Coursework: Students develop essential knowledge of programming, data management, probability, and big data analytics. Core subjects include Data Analysis for Engineers and Scientists (EMSE 6765) or Probability for Computer Science (CSCI 6362), Introduction to Big Data and Analytics (CSCI 6444), and Programming for Analytics (EMSE 6574). These courses introduce statistical analysis, programming techniques, data processing, and computational methods for managing large datasets.

Year 2 – Advanced Coursework and Specialisation: Students deepen their technical expertise through one of two specialised pathways. In the Computer Science track, students study Design and Analysis of Algorithms (CSCI 6212) and Machine Learning (CSCI 6364), while the Engineering Management and Systems Engineering track includes Applied Machine Learning for Analytics (EMSE 6575) and Data-Driven Policy (EMSE 6577). Students also select electives within their chosen track to develop advanced analytical, computational, and decision-making skills.

Applied Learning and Capstone: Students complete Data Management Systems for Data Analytics (EMSE 6586) or Database Management Systems (CSCI 6441), followed by Data Analytics Capstone (SEAS 6402). The capstone provides an opportunity to integrate programming, data management, analytical methods, and computational techniques to address practical data analytics challenges.

Curriculum note: The university specifies the credit and course requirements but does not prescribe a fixed year-by-year course sequence. The two-year structure above represents an indicative progression for full-time students.

Focus Areas

Data Analytics, Data Science, Computer Science, Business Analytics, Systems Engineering, Big Data Analytics, Machine Learning, Applied Machine Learning, Statistical Analysis, Probability, Data Management, Database Management Systems, Programming for Analytics, Algorithm Design, Data-Driven Decision Making, Data-Driven Policy, Data Processing, Computational Analytics, Decision Support Systems, Predictive Analytics.

Learning Outcomes

Apply data science and analytical techniques to improve organisational decision-making; collect, store, clean, transform, and manage large datasets; design and implement computing infrastructure and algorithms for big data analytics; apply machine learning and statistical methods to solve practical problems; develop programming skills for data analytics; understand database management and data processing systems; interpret analytical results to support business and organisational decisions; apply computational and engineering principles to complex data challenges; gain practical experience using analytical tools and technologies; develop solutions for government, industry, and research organisations.

Professional Alignment (Accreditation)

The George Washington University is institutionally accredited by the Middle States Commission on Higher Education (MSCHE). The MS in Data Analytics is also a STEM-designated programme, making it relevant to international students seeking STEM-related employment opportunities in the United States. The university does not identify a separate professional accreditation specifically for this master's programme.

Reputation (Employability Rankings)

Interdisciplinary Education: The programme is jointly administered by the Departments of Computer Science and Engineering Management and Systems Engineering, combining technical, computational, and management perspectives.
Industry Relevance: The curriculum addresses the growing demand for data analytics professionals across government, industry, and research organisations.
Practical Experience: Students complete a Data Analytics Capstone, applying their technical and analytical knowledge to practical problems.
Career Opportunities: Graduates can pursue roles such as Data Scientists, Data Analysts, Data Engineers, Analytics Managers, Data Security Analysts, Machine Learning Engineers, and Business Intelligence Analysts.
Professional Development: The programme develops skills in big data analytics, machine learning, programming, data management, and data-driven decision-making.
Programme Rankings: No specific QS or Guardian ranking or programme-level employment statistic is published on the official programme page.

Experiential Learning (Research, Projects, Internships etc.)

At George Washington University, the MSc in Data Analytics program is designed to provide students with hands-on experience that bridges the gap between theory and practice. Students engage in real-world projects, utilize cutting-edge technology, and collaborate with peers to solve complex data challenges. The program emphasizes experiential learning through various opportunities that enhance practical skills and prepare graduates for successful careers in data analytics.

Here are some key aspects of the experiential learning opportunities available in the MSc in Data Analytics program:

- Capstone Projects: Students work on a comprehensive capstone project that involves analyzing real datasets and presenting actionable insights, simulating a professional data analytics environment.

- Internship Opportunities: The program encourages students to pursue internships with industry partners, providing invaluable experience and networking opportunities in the field of data analytics.

- Access to Advanced Software: Students gain proficiency in industry-standard tools such as Python, R, SQL, Tableau, and SAS, which are essential for data analysis and visualization.

- Collaborative Group Projects: The curriculum includes team-based projects that foster collaboration and communication skills, essential for working in diverse teams in the workplace.

- Research Facilities: Students have access to state-of-the-art laboratories and research centers, where they can engage in innovative projects and collaborate with faculty on cutting-edge research.

- Field Trips and Industry Visits: The program includes opportunities for field trips to local companies and organizations, allowing students to see data analytics in action and learn from industry leaders.

- Dedicated Libraries and Resources: The Gelman Library and other university resources provide extensive access to academic journals, databases, and research materials relevant to data analytics.

- Networking Events and Workshops: The program hosts various events, including guest lectures and workshops, where students can connect with professionals and gain insights into the latest trends in data analytics.

These experiential learning opportunities not only enhance your academic experience but also equip you with the skills and connections needed to thrive in the data analytics field.

Consider applying to the MSc in Data Analytics program and take the first step towards a rewarding career in this dynamic field!

Progression & Future Opportunities

Pursuing an MSc in Data Analytics at George Washington University opens the door to a wealth of career opportunities in a rapidly growing field. Graduates often find themselves in roles such as Data Analyst, Business Intelligence Analyst, Data Scientist, and Quantitative Analyst. The demand for skilled professionals in data analytics is on the rise, making this program a strategic choice for your future.

Here’s how GWU supports your journey toward a successful career:

- Career Services: The university offers dedicated career services, including resume workshops, interview preparation, and networking events tailored specifically for data analytics students.
- Employment Statistics: GWU boasts an impressive employment rate, with over 90% of graduates securing jobs within six months of graduation. The average starting salary for graduates in data analytics is around $85,000, reflecting the high demand for these skills.
- University–Industry Partnerships: GWU has established partnerships with leading companies in technology and finance, providing students with internship opportunities and real-world projects that enhance learning and employability.
- Accreditation Value: The program is accredited by the Middle States Commission on Higher Education, ensuring that your degree holds long-term value in the job market.
- Graduation Outcomes: Alumni of the program have gone on to work at prestigious organizations such as Deloitte, Amazon, and the World Bank, showcasing the program's strong reputation and the quality of education provided.

Further Academic Progression: After completing your MSc in Data Analytics, you may choose to further your studies by pursuing a PhD in Data Science or a related field. This advanced degree can open doors to academic positions, research opportunities, and higher-level roles in data strategy and analytics leadership. Additionally, you might consider obtaining professional certifications in specialized areas such as machine learning or big data, which can further enhance your expertise and marketability in the field.

Choosing GWU for your MSc in Data Analytics is not just about earning a degree; it's about setting yourself up for a successful and fulfilling career in a dynamic industry.