Master of Science in Data Science

Program Description

The M.S. in Data Science program provides a strong foundation for developing, implementing, and evaluating data analytics solutions to transform raw data into meaningful and actionable insights. The program builds competencies in data analytics, database systems, big data technologies, and machine learning techniques that are essential to harnessing large structured and unstructured datasets for solving complex problems. Students gain a conceptual understanding of the technical, social, organizational, and ethical challenges in data science projects and deploy and evaluate solutions for mitigating such risks.

The program is designed to be completed within two years, however many students should be able to complete the program in 18 months. The program is designed for students who have a significant interest in learning data science and analytics concepts, methods and software tools. The coursework incorporates emerging trends in big data, machine learning and artificial intelligence. The program is suitable for both recent graduates as well as professionals interested in developing data science competencies.

The curriculum of the M.S. in Data Science program focuses on building the core competencies and technical and business/professional skills that were identified through research and industry interviews. In addition, the coursework emphasizes real-word problem solving and applications related to data science. The curriculum is designed to foster experiential learning and critical thinking.

Why Study Data Science

The emergence of data science as an academic discipline is tied directly to the massive and exponential growth of data; a phenomenon typically referred to as “big data.” Big data has become ubiquitous due to the proliferation of web, social media, smart phones, and sensors. Big data systems and machine learning (a branch of artificial intelligence) are leading technologies that organizations seek to harness to make better decisions and find insights for solving complex problems. Data science is an emerging, interdisciplinary field that focuses on scientific methods, processes, and systems to extract knowledge, insights or patterns from data.

There are numerous popular applications of data science. For example, leading companies use data science to recommend products and services to customers. Retailers make decisions about product promotions and inventories using data science techniques. Major sports teams use sports analytics to recruit players and analyze injury risks. E-retailers and web companies use data science to optimize web traffic and customize customer experience to enhance sales. Self-driving vehicles, language-translation and image recognition applications, and voice assistants (Chatbots) are recent applications of data science, especially deep learning and machine learning.

Job Demand

Organizations are increasingly looking for data scientists who have the technical and business skills. According to IBM, the number of jobs for all U.S. data professionals will increase by 364,000 openings to 2,720,000 by 2020. According to Glassdoor, there were more than 500 jobs posted for “data scientist” positions in Minnesota (in February 2019) with an average base salary of over $117,000. Most data scientist positions require an advanced education.

According to Glassdoor research, data scientist was the highest-paying entry-level job in 2018! Young adults in the data science field earned a median annual base salary of $95,000.

After completing the MS in Data Science program, students will be able to:

  • Identify, examine, and assess the opportunities and challenges for a data science project that can help an organization create measurable value and advance its strategic goals.
  • Formulate clear and precise business or research questions that can inform decision-making.
  • Design and apply the process of collecting, storing, cleaning, transforming, and visualizing, data from disparate data sources, including very large, unstructured, and temporal datasets.
  • Analyze data using appropriate concepts, tools, and techniques to develop, and evaluate analytics models.
  • Evaluate and interpret the outcomes of data science models and articulate and integrate the outcomes into actionable strategies.
  • Develop and explain methods and formats of communication appropriate for data science projects that can help decision makers act on the findings.
    Identify social, legal, organizational, and ethical issues related to data science projects and recommend solutions and strategies for mitigating such risks.

Please visit the coursework page to review the coursework (core courses, restricted and unrestricted elective courses, and capstone experience) for the MS in data science program. You can review the class schedule page to review scheduled courses for a given semester.

The current CIS students who are in their junior year can apply to the M.S. Data Science program and double count up to 12 graduate credits toward both the undergraduate and graduate programs. Students may apply to the combined program at any time after the conclusion of their sophomore year, but prior to the start of their final undergraduate semester. To apply for the combined option, complete the Combined Program Application.

  • All students are required to successfully complete the required core courses, restricted electives, unrestricted electives, and capstone experience for graduation.
  • One course can be used to satisfy only one requirement (e.g. core, restricted elective, capstone or unrestricted elective etc.) within the M.S. in Data Science degree program.
  • Students must complete a minimum of 50% of all graduate credits at the 600-level, excluding the thesis or APP credits, and must maintain a grade point average of a “B” or above in all coursework.
  • Students who have not completed the required undergraduate courses identified in the admission requirement may be granted provisional admission. Students who are granted provisional admission will be required to complete necessary undergraduate IT and/or statistics/math courses.
  • Submit the completed application and applicable fee to Minnesota State University, Mankato graduate school.
  • A four-year bachelor's degree earned at a regionally accredited college or university and a cumulative grade point average (GPA) of 3.0. Official college transcripts are required. (Applicants with a GPA below 3.0 who present convincing evidence of a potential for success may be considered for provisional admission).
  • Successful competition of the following undergraduate courses with a grade of “C” (2.0) or better: computer programming, data structures & algorithms, databases, systems analysis, statistics, and calculus 1 & 2 or equivalent. (Students who do not meet requirement may be granted provisional admission, but they will be required to complete prerequisites.)
  • A professional goal statement in which the applicant documents experience with and an interest in data science.
  • A professional resume including: name, education, certifications, professional employment history, honors, awards, scholarships, professional organization memberships and involvement, scholarly activities (including publications, professional presentations, research activities, and/or consultation).
  • Three letters of recommendation from individuals who can respond to questions about the applicant's abilities and potential for success in a graduate program.

Please see the admission requirements section for details. In addition, International students must demonstrate English language proficiency by submitting TOEFL or IELTS results. IELTS results must be from the Academic and not the General Training module. The undergraduate transcript must be verified by a credential evaluation service if it is not from a US institution. International applicants must submit the required financial affidavit. For additional information, see Graduate International Student Admission Requirements.

Is financial aid available for this program?
Yes, financial aid is available to students who qualify. Please see the Financial Aid page for details.

What are tuition and fees for the MS in Data Science program?
Please see the Cost of Attendance page for details.

Do you have graduate assistantships?
Graduate assistantships are competitive and are available to students who qualify. Please see the Graduate Assistantship page to review current vacancies.

Is this program offered online or in Minneapolis area?
The MS in Data Science program is currently offered face-to-face at the Mankato campus.

What are the admission requirements for international students?
Please see the admission requirements section for details. In addition, International students must demonstrate English language proficiency by submitting TOEFL or IELTS results. IELTS results must be from the Academic and not the General Training module. The undergraduate transcript must be verified by a credential evaluation service if it is not from a US institution. International applicants must submit the required financial affidavit. For additional information, see Graduate International Student Admission Requirements.

My undergraduate degree is not related to computer science, information technology, mathematics, or statistics. Can I apply for the data science program?
Yes! Data Science is an interdisciplinary field, so we welcome students from any discipline. If admitted, you may be required to take additional undergraduate courses in deficient areas (IT and/or math/statistics) depending on your undergraduate coursework. Additional courses may include computer programming, data structures & algorithms, databases, systems analysis, statistics & probability, and/or calculus 1 & 2 or equivalent.

Is the GRE required to apply for the MS in Data Science program?
The GRE is not required.

My undergraduate GPA is less than 3.0. Can I apply for the program?
The GPA is an important criterion for admission; however, other parts of the application are also considered. Applicants with a GPA below 3.0 who present convincing evidence of a potential for success may be considered for provisional admission. Such evidence may include undergraduate research, awards, and job experience in IT or data science.

How to Apply

To apply for the MS in Data Science program, see the Graduate Application Instructions.

The Graduate Program Coordinator is Dr. Guarionex Salivia.