Data Analytics, M.S.

1 Years On Campus Masters Program

Clark University

Program Overview

The Master of Science in Data Analytics (MSDA) at Clark University prepares students to transform complex datasets into meaningful insights that support business growth and strategic decision-making. The STEM-designated program combines Python programming, advanced statistical analysis, machine learning, data mining and data warehousing, making it suitable for students interested in careers across technology, finance, healthcare, marketing and business intelligence.

Curriculum Structure:

Year 1:

Students begin by developing a strong foundation in programming, mathematical statistics, data management and analytical techniques. Courses such as Python for Data Analytics (MSDA 3090), Mathematical Statistics (MSDA 3045) and Data Mining with Splunk (MSIT 3350) introduce students to programming, statistical inference, data exploration and large-scale data processing. Students also develop practical skills in data visualization, data acquisition and analytical problem-solving.

Year 2:

Students advance their expertise through machine learning, deep learning and modern data engineering. Courses such as Applied Machine Learning (MSDA 3050), Applied Deep Learning (MSDA 3100) and Modern Data Engineering (MSDA 3040) help students build predictive models, work with advanced algorithms and develop efficient data-processing systems. Students complete a Capstone Practicum (MSDA 3999), applying their technical and analytical knowledge to real-world projects involving industry partners, nonprofit organizations, government agencies or independent research.

Focus Areas:

Data Analytics, Python Programming, Applied Machine Learning, Applied Deep Learning, Mathematical Statistics, Data Mining, Data Warehousing, Modern Data Engineering, Predictive Analytics, Statistical Modelling, Business Intelligence, Data Visualization, Big Data Analytics, Data Architecture, Data Management and Data-Driven Decision-Making.

Learning Outcomes:

Graduates will be able to collect, organize and analyze complex datasets, develop statistical and machine learning models, apply Python programming to analytical challenges, design and optimize data warehouses, implement data mining techniques, create meaningful dashboards and visualizations, evaluate analytical evidence and communicate insights effectively. They will also develop the ability to apply ethical data practices and use analytical solutions to support organizational decision-making.

Professional Alignment (Accreditation):

The program is offered through Clark University's School of Professional Studies and emphasizes practical learning through its practitioner-scholar teaching model, industry collaboration and capstone experiences. Students gain exposure to Python, Splunk, machine learning, data management and advanced statistical techniques. The degree is STEM-designated, potentially allowing eligible international graduates to obtain up to 36 months of Optional Practical Training (OPT) in the United States. Clark University is institutionally accredited by the New England Commission of Higher Education (NECHE).

Reputation (Employability Rankings):

72% Employment Rate: Clark University's MS in Data Analytics graduates reported a 72% employment rate within six months of graduation.
Industry Opportunities: Graduates can pursue opportunities with organizations such as Amazon, Amazon Web Services, ADP, financial institutions and healthcare organizations.
Growing Career Fields: The program prepares students for careers in data analytics, data science, business intelligence, data engineering and machine learning.
Professional Roles: Potential positions include Data Analyst, Data Scientist, Marketing Analyst, Machine Learning Engineer, Business Intelligence Analyst, BI Developer, Data Engineer and Data Architect.
Practical Industry Experience: The MSDA Capstone Projects Showcase allows students to present real-world analytical projects developed with industry partners, nonprofit organizations, government agencies and research initiatives.

Experiential Learning (Research, Projects, Internships etc.)

At Clark University, the Data Analytics, M.S. program is designed to provide students with hands-on experience that translates theory into practice. Students engage in real-world projects, utilize cutting-edge software, and collaborate with peers to solve complex data challenges. The program emphasizes experiential learning through various facilities and resources that enhance your educational journey.

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

- State-of-the-Art Labs: Access to dedicated computer labs equipped with the latest data analytics software, including R, Python, and Tableau, allowing you to work on real datasets and develop your technical skills.

- Collaborative Projects: Engage in group projects that simulate real-world data analytics scenarios, fostering teamwork and communication skills essential for your future career.

- Internship Opportunities: The program encourages internships with local businesses and organizations, providing you with invaluable industry experience and networking opportunities.

- Field Trips: Participate in field trips to tech companies and data-driven organizations, giving you insights into how data analytics is applied in various sectors.

- Research Institutes: Collaborate with faculty on research projects at Clark’s research institutes, where you can contribute to cutting-edge studies and gain experience in academic research.

- Library Resources: Utilize the extensive resources at the Goddard Library, which offers access to a wealth of academic journals, databases, and research materials relevant to data analytics.

- Workshops and Seminars: Attend workshops and seminars led by industry professionals, enhancing your learning experience and keeping you updated on the latest trends in data analytics.

Progression & Future Opportunities

Pursuing a Master of Science in Data Analytics at Clark University opens the door to a wealth of career opportunities in a rapidly growing field. Graduates are well-prepared for roles such as Data Analyst, Business Intelligence Analyst, and Data Scientist, among others. With the increasing demand for data-driven decision-making across industries, the skills you acquire will position you for success in a variety of sectors.

Here’s what you can expect from the program and the support available to you:

- University Services: Clark offers dedicated career services, including resume workshops, interview preparation, and networking events specifically tailored for data analytics students. The university also hosts career fairs that connect students directly with employers in the tech and analytics sectors.

- Employment Stats and Salary Figures: According to recent data, over 90% of graduates from the Data Analytics program secure employment within six months of graduation, with starting salaries averaging around $75,000 annually. This figure can vary based on location and industry, but the potential for growth is significant.

- University–Industry Partnerships: Clark University has established partnerships with leading companies in the tech industry, such as IBM and Deloitte. These collaborations often lead to internship opportunities and real-world projects that enhance your learning experience.

- Long-term Accreditation Value: The Data Analytics program is accredited by the New England Commission of Higher Education (NECHE), ensuring that your degree holds value and recognition in the job market for years to come.

- Graduation Outcomes: Alumni of the program have gone on to work at prestigious organizations, including Google, Amazon, and various financial institutions, showcasing the program's strong reputation and the effectiveness of its curriculum.

Further Academic Progression: After completing your M.S. in Data Analytics, 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 data strategy and analytics leadership. Additionally, you might consider certifications in specialized areas such as machine learning or big data, which can further enhance your expertise and marketability in the field.