Master of Science in Data Science (Non-Thesis)

1 Years On Campus Masters Program

Colorado School of Mines

Program Overview

The Master of Science in Data Science (Non-Thesis) at Colorado School of Mines is designed for students with backgrounds in science, technology, engineering and mathematics who want to develop advanced expertise in data analytics and computational problem-solving. The program combines statistical inference, computer science, machine learning and mathematical modelling, allowing students to apply data science techniques to complex real-world challenges across technology, business, energy, healthcare and engineering.

Curriculum Structure:

Year 1:

Students begin by building a strong foundation in statistical methods, data science, machine learning and computational techniques. Courses such as Advanced Data Science (DSCI 503), Introduction to Machine Learning (DSCI 570) and Introduction to Statistical Methods (MATH 530) introduce students to data processing, statistical inference, predictive modelling and algorithm development. Students also explore elective subjects based on their chosen specialization and professional interests.

Year 2:

Students advance their analytical and technical capabilities through advanced machine learning, statistical learning methods and specialized electives. Courses such as Advanced Machine Learning (DSCI 575), Introduction to Key Statistical Learning Methods I (MATH 560) and Introduction to Key Statistical Learning Methods II (MATH 561) help students develop sophisticated predictive models, evaluate complex datasets and apply computational techniques to practical problems. Students also complete professional development coursework in areas such as research ethics, leadership, teamwork and communication, alongside electives tailored to their chosen application domain.

Focus Areas:

Data Science, Machine Learning, Advanced Machine Learning, Statistical Inference, Statistical Learning, Predictive Analytics, Computer Science, Applied Mathematics, Business Analytics, Data Visualization, Data Processing, Computational Algorithms, Parallel Computing, Data Modelling, Big Data Analytics and Responsible Data Management.

Learning Outcomes:

Graduates will be able to collect, process and interpret complex datasets, apply statistical and computational methods, develop predictive and machine learning models, identify meaningful patterns in large datasets, communicate analytical findings effectively, make evidence-based decisions and implement responsible data practices. They will also develop the ability to apply data science techniques to specialized fields such as engineering, business, technology, science and energy.

Professional Alignment (Accreditation):

The program combines technical coursework with professional development and application-focused electives, preparing students for data science roles across industry, government and research organizations. Colorado School of Mines is institutionally accredited through the doctoral degree by the Higher Learning Commission (HLC). The university does not list a separate program-specific professional accreditation for this Data Science master's degree. Students also benefit from facilities such as the Labriola Innovation Hub, McNeil Center for Entrepreneurship and Innovation, and Mines Shared Instrumentation Facility.

Reputation (Employability Rankings):

$96,329 Median Salary: Colorado School of Mines reports a median salary of $96,329 for recent graduates of this Data Science program.
Leading Technology Employers: Organizations seeking Mines graduates include Amazon, Google, Microsoft, Oracle, Salesforce, Databricks and Uber.
Financial and Consulting Opportunities: Employers include Accenture, Capital One, Deloitte, Ernst & Young, Visa and McKinsey & Company.
Energy and Engineering Opportunities: Graduates can explore opportunities with companies such as Boeing, Lockheed Martin, Chevron, ExxonMobil, BHP and Newmont.
Research and Government Organizations: Employers include Los Alamos National Laboratory, Sandia National Laboratories, the National Institute of Standards and Technology and the U.S. Geological Survey.

Experiential Learning (Research, Projects, Internships etc.)

At Colorado School of Mines, the Master of Science in Data Science program is designed to provide students with hands-on, practical experience that is essential for success in the rapidly evolving field of data science. Students engage in real-world projects, collaborate with industry partners, and utilize state-of-the-art facilities and tools that enhance their learning experience. This program emphasizes experiential learning, ensuring that you not only understand theoretical concepts but also apply them in practical settings.

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

- Collaborative Projects: Engage in team-based projects that simulate real-world data challenges, allowing you to work alongside peers and faculty to develop innovative solutions.
- Internship Opportunities: Gain valuable industry experience through internships with leading companies, providing you with insights into the practical applications of data science.
- Advanced Software Tools: Utilize industry-standard software such as Python, R, SQL, and Tableau, equipping you with the technical skills needed for data analysis and visualization.
- Research Laboratories: Access dedicated research labs equipped with cutting-edge technology, where you can conduct experiments and work on projects that contribute to the field of data science.
- Data Science Institute: Collaborate with the Data Science Institute, which focuses on interdisciplinary research and partnerships, enhancing your learning through exposure to current trends and challenges in the industry.
- Field Trips and Workshops: Participate in field trips to tech companies and workshops led by industry experts, providing you with networking opportunities and insights into the latest advancements in data science.
- Library Resources: Benefit from extensive library resources, including access to databases, journals, and research materials that support your studies and projects.

Progression & Future Opportunities

The Master of Science in Data Science at Colorado School of Mines equips graduates with the skills and knowledge to excel in a rapidly evolving field. Alumni often find themselves in high-demand roles such as Data Scientist, Machine Learning Engineer, Data Analyst, and Business Intelligence Developer. With a strong emphasis on practical experience and industry relevance, this program prepares students for a successful career in data science:

- University Services for Employment: The Career Center at Mines offers personalized career coaching, resume workshops, and networking events specifically tailored for data science students. Additionally, the Mines Alumni Network provides valuable connections in the industry.

- Employment Stats and Salary Figures: According to recent data, over 90% of graduates from the Data Science program secure employment within six months of graduation, with starting salaries averaging around $100,000 per year.

- University–Industry Partnerships: Mines has established partnerships with leading companies such as IBM, Google, and Lockheed Martin, providing students with opportunities for internships, collaborative projects, and job placements.

- Long-term Accreditation Value: The program is accredited by the Higher Learning Commission, ensuring that the education you receive meets high standards and is recognized by employers worldwide.

- Graduation Outcomes: Graduates of the program have gone on to work at top-tier companies and have reported high levels of job satisfaction and career advancement.

Further Academic Progression: After completing the Master’s program, students have the option to pursue a Ph.D. in Data Science or related fields, allowing for deeper research opportunities and specialization. This can lead to roles in academia, advanced research positions, or leadership roles in industry. Additionally, graduates may choose to obtain professional certifications in areas such as machine learning or big data analytics to further enhance their expertise and career prospects.