Data Science MS

16 Months On Campus Masters Program

Michigan Technological University

Program Overview

The Master of Science in Data Science at Michigan Technological University provides an interdisciplinary education combining computer science, mathematics, statistics, business, and engineering to prepare students for careers in the rapidly growing data science industry. The programme is suitable for students interested in extracting insights from complex datasets, developing predictive models, applying machine learning, and solving real-world challenges across industries such as healthcare, finance, environmental science, cybersecurity, and engineering.

Curriculum Structure

Core Coursework: Students develop essential knowledge of data science, business analytics, and predictive modelling. Important subjects include Introduction to Data Science (UN 5550), Predictive Modeling (MA 5790), and Information Systems Management and Data Analytics (BA 5200). These courses introduce data collection, data wrangling, exploratory data analysis, statistical modelling, classification, prediction, and the application of analytics in business environments.

Advanced Coursework: Students strengthen their computational and analytical skills through electives such as Foundations of Machine Learning (CS 4801), Advanced Data Mining (CS 5831), Deep Learning (CS 5841), and Introduction to Big Data Analytics (SAT 5165). These subjects explore machine learning algorithms, artificial intelligence, data mining techniques, large-scale data processing, and technologies such as Hadoop and Spark.

Specialisation and Applied Learning: Students can customise their studies through domain-specific courses in areas such as healthcare, biomedical engineering, cybersecurity, and computational science. Options include Introduction to Health Informatics (SAT 5001), Population Health Informatics (SAT 5424), Machine Learning in Security (SAT 5520), and Computer Vision for Microscopic Images (BE 5870). Depending on their selected degree option, students complete coursework, an independent research report, or a supervised research thesis.

Curriculum note: Michigan Tech does not prescribe a fixed year-by-year course sequence. The descriptions above represent the programme's course categories and possible progression, rather than an official semester or year-wise schedule.

Focus Areas

Data Science, Data Analytics, Predictive Modelling, Machine Learning, Deep Learning, Artificial Intelligence, Big Data Analytics, Data Mining, Statistical Analysis, Data Visualisation, Cloud Computing, Computational Science, Bioinformatics, Health Informatics, Cybersecurity Analytics, Biomedical Data Analysis, Sensor Networks, Environmental Data Science, Geospatial Intelligence, Business Intelligence.

Learning Outcomes

Develop skills in collecting, cleaning, managing, and analysing complex datasets; apply statistical modelling and predictive analytics to real-world problems; design and implement machine learning and deep learning algorithms; use data mining techniques to extract meaningful information from large datasets; understand big data processing frameworks such as Hadoop and Spark; develop data visualisation and communication skills; apply computational techniques to interdisciplinary challenges in healthcare, engineering, business, and environmental science.

Professional Alignment (Accreditation)

Michigan Technological University is institutionally accredited by the Higher Learning Commission (HLC). The university's accreditation applies institutionally, and the official programme information does not identify a separate professional accreditation specifically for the MS in Data Science.

Reputation (Employability Rankings)

Employment Outlook: According to the US Bureau of Labor Statistics figures cited by Michigan Tech, employment for data scientists is projected to grow by 34% between 2024 and 2034.
Graduate Salaries: Michigan Tech reports a mean annual salary of $126,800 for data scientists, based on US Bureau of Labor Statistics data dated May 2025.
Industry Connections: Graduates have secured employment with major organisations, including Google, Amazon, Microsoft, Apple, IBM, Intel, Adobe, and Oracle.
University Recognition: Michigan Tech highlights its position among Michigan's leading public universities for return on investment and internship opportunities.
Programme Rankings: No specific QS or Guardian ranking for the individual MS in Data Science programme is provided on the official programme page.

Experiential Learning (Research, Projects, Internships etc.)

At Michigan Technological University, the MS in Data Science program is designed to provide students with hands-on experience that is essential for success in the rapidly evolving field of data science. Students engage in practical learning through various projects, internships, and access to state-of-the-art facilities and tools. This experiential learning approach ensures that you not only understand theoretical concepts but also apply them in real-world scenarios, preparing you for a successful career.

Here are some key aspects of experiential learning in the MS in Data Science program:

- Collaborative Projects: Engage in group projects that simulate real-world data challenges, allowing you to work alongside peers and faculty to develop solutions.
- Internship Opportunities: Gain valuable industry experience through internships with local and national companies, enhancing your resume and professional network.
- Advanced Software Tools: Utilize industry-standard software such as Python, R, SQL, and Tableau for data analysis and visualization, ensuring you are proficient in the tools used by professionals.
- Research Laboratories: Access dedicated research labs equipped with high-performance computing resources, where you can conduct experiments and work on cutting-edge data science research.
- Data Science Institute: Collaborate with the Michigan Tech Data Science Institute, which focuses on interdisciplinary research and provides opportunities for students to engage in innovative projects.
- Field Trips and Workshops: Participate in field trips to tech companies and workshops led by industry experts, giving you insights into the latest trends and practices in data science.
- Library Resources: Benefit from extensive library resources, including access to academic journals, databases, and research materials that support your studies and projects.

These experiential learning opportunities are designed to enrich your educational journey and equip you with the skills needed to excel in the data science field.

Consider applying to the MS in Data Science program at Michigan Technological University and take the first step toward a rewarding career in data science!

Progression & Future Opportunities

Pursuing an MS in Data Science at Michigan Technological University opens the door to a wealth of career opportunities in a rapidly growing field. Graduates often find themselves in roles such as Data Scientist, Machine Learning Engineer, Business Intelligence Analyst, and Data Analyst. The demand for skilled professionals in data science continues to rise, making this an excellent time to invest in your future.

Here’s how Michigan Tech 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 science students.
- Employment Statistics: Over 90% of graduates from the College of Computing secure employment within six months of graduation, with starting salaries averaging around $85,000 per year.
- University–Industry Partnerships: Michigan Tech collaborates with leading companies such as IBM, Ford, and General Motors, providing students with internship opportunities and real-world projects that enhance their learning experience.
- Accreditation Value: The program is accredited by the Higher Learning Commission, ensuring that your degree meets high academic standards and is recognized by employers worldwide.
- Graduation Outcomes: Alumni have gone on to work at top tech firms and research institutions, contributing to innovative projects and advancements in data science.

Further Academic Progression: After completing your MS in 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 roles, or specialized industry positions, allowing you to contribute to cutting-edge developments in data science and analytics. Additionally, you could explore certifications in specific tools or methodologies, enhancing your expertise and marketability in the job market.

Choosing Michigan Tech for your MS in Data Science is not just about earning a degree; it's about building a future filled with possibilities.