Computational and Applied Data Science, Master of Science

NA On Campus Masters Program

Northern Arizona University

Program Overview

The Master of Science in Computational and Applied Data Science at Northern Arizona University prepares students to become skilled data scientists, analysts, and engineers through a multidisciplinary curriculum combining computer science, statistics, and database management. Designed for students with a STEM background, the programme develops expertise in collecting, cleaning, storing, analysing, and visualising large datasets, with practical applications in healthcare, finance, bioinformatics, environmental science, and research.

Curriculum Structure

Foundational Coursework: Students begin by strengthening their programming, computational, and data management skills. Key courses include Data Structure Foundations (CS 500), Data Wrangling and Management (INF 506), and Databases (ITC 503), covering algorithm development, data preparation, data integration, and database design.

Advanced Coursework: Students progress into statistical analysis and applied data science through Modern Regression I (INF 511) and electives from computer science, informatics, mathematics, statistics, and business analytics. Options include Applied Business Intelligence (BAN 540) and High Performance Computing (CS 552), allowing students to develop specialised analytical and computational expertise.

Applied Learning: Students complete Data Analytics Capstone (INF 586), a hands-on, project-based course involving real-world datasets. They work collaboratively on large-scale data analysis, machine learning models, data visualisation, and interdisciplinary applications such as bioinformatics, eco-informatics, astroinformatics, and health informatics.

Curriculum note: The programme requires 30 graduate-level units. Northern Arizona University does not prescribe a fixed year-by-year sequence in its catalogue.

Focus Areas

Data Science, Computational Data Science, Data Wrangling and Management, Database Management, Data Warehousing, Statistical Modelling, Machine Learning, Data Mining, High-Performance Computing, Business Intelligence, Data Analytics, Data Visualisation, Bioinformatics, Eco-informatics, Health Informatics, Astroinformatics.

Learning Outcomes

Identify and apply data discovery, capture, and cleaning techniques; design and evaluate database solutions for data warehousing, staging, and processing; implement machine learning, data mining, statistical modelling, and quantitative analytical methods; analyse complex spatial, time-series, and survey datasets; communicate analytical findings through data visualisation, business intelligence, and reporting tools; apply computational data science techniques to real-world problems.

Professional Alignment (Accreditation)

Northern Arizona University is institutionally accredited by the Higher Learning Commission (HLC). The university's official accreditation information does not separately list the MS in Computational and Applied Data Science as a professionally accredited programme.

Reputation (Employability Rankings)

Industry Relevance: The programme is designed to prepare data science analysts, engineers, and leaders for opportunities in healthcare, finance, government, research, and other data-driven industries.
Career Relevance: The curriculum develops practical capabilities in data modelling, machine learning, data analytics, database management, and visualisation.
Employment Outlook: The university's programme description cites a US Bureau of Labor Statistics projection of 36% growth in demand for data scientists over the referenced decade.
Programme Rankings: No specific QS or Guardian ranking for this individual master's programme is stated in the official programme catalogue.

Experiential Learning (Research, Projects, Internships etc.)

At Northern Arizona University, the MS in Computational and Applied Data Science program is designed to provide students with hands-on experience that is essential for success in today’s data-driven world. Students engage in practical learning through various projects, internships, and access to state-of-the-art facilities and tools. This experiential learning approach not only enhances theoretical knowledge but also equips students with the skills needed to tackle real-world challenges in data science.

Here are some key aspects of the experiential learning opportunities available in this program:

- Collaborative Projects: Students often work in teams on projects that simulate real-world data science problems, fostering teamwork and communication skills.
- Internships: The program encourages internships with local businesses and organizations, allowing students to apply their skills in professional settings and gain valuable industry experience.
- Advanced Software Tools: Students have access to industry-standard software such as Python, R, and SQL, which are essential for data analysis and visualization.
- Research Laboratories: The university features dedicated labs equipped with high-performance computing resources, enabling students to conduct advanced research in data science.
- Field Trips: Opportunities for field trips to tech companies and research institutions provide insights into the latest industry trends and practices.
- Libraries and Resources: The Cline Library offers extensive resources, including databases and journals specific to data science, supporting students in their research and projects.
- Data Science Institute: The university hosts a Data Science Institute that promotes interdisciplinary collaboration and provides additional resources for students.

These experiential learning opportunities are designed to ensure that graduates are not only knowledgeable but also ready to excel in their careers.

Consider applying to the MS in Computational and Applied Data Science program and take the first step towards a rewarding career in data science!

Progression & Future Opportunities

Pursuing an MS in Computational and Applied Data Science at Northern Arizona University opens the door to a wealth of career opportunities. Graduates from this program are well-prepared for roles such as Data Scientist, Data Analyst, Machine Learning Engineer, and Business Intelligence Analyst. With the increasing demand for data-driven decision-making across industries, your skills will be highly sought after.

Here’s how Northern Arizona University supports your journey toward a successful career:

- Career Services: The university offers dedicated career counseling, resume workshops, and interview preparation to help you stand out in the job market.
- Employment Statistics: Graduates from the program have reported a high employment rate, with many securing positions within six months of graduation. The average starting salary for graduates in data science roles is around $85,000, reflecting the strong demand for skilled professionals.
- University–Industry Partnerships: NAU has established partnerships with leading companies in technology and analytics, providing students with internship opportunities and real-world projects that enhance learning and employability.
- Accreditation: The program is accredited by the Higher Learning Commission, ensuring that your degree holds long-term value and is recognized by employers nationwide.
- Graduation Outcomes: Alumni have successfully transitioned into impactful roles in various sectors, including healthcare, finance, and technology, showcasing the versatility of the skills gained in this program.

Further Academic Progression: After completing your MS in Computational and Applied Data Science, you may choose to further your studies by pursuing a Ph.D. in Data Science or a related field. This advanced degree can open doors to academic positions, research opportunities, and higher-level roles in industry, allowing you to contribute to innovative projects and lead teams in data-driven initiatives.

Embarking on this academic journey at Northern Arizona University not only equips you with essential skills but also positions you for a thriving career in a rapidly evolving field.