• Course Delivery
    100% Online
  • Total Credits
  • In-State Tuition Per Credit
  • Out of State Tuition Per Credit

Large scale data represents opportunity in nearly every field of advanced study. The online graduate certificate in data science and analytics from MU will help you understand the full spectrum of the science and how it can help you make better decisions. If you want to see how data science can help inform and develop future leaders, this may be the certificate for you.

This program was designed for:

  • Working professionals who deal with data in their business and have institutional support for data science education.
  • Doctoral students of anthropology, biotechnology, business, computer science, geography, geospatial science or related fields.

Program structure and topics

Delivery of this program is 100% online: no campus visits are required.

Course work covers

  • Structuring and formatting data
  • Data visualization and interpretation
  • Database systems, design and implementation
  • Probability, statistics and pattern analysis
  • Statistical programming

Admissions & Requirements

To be accepted to this program, you must have:

  1. A bachelor's degree

    Completed baccalaureate or advanced degree from a regionally accredited institution.

  2. Minimum GPA of 3.0 in the last 60 hours of undergraduate education.
  3. If English is not your native language, please submit a TOEFL score of at least 80 (internet-based test), an IELTS score of at least 6.5, a Pearson Test of English (PTE) score of at least 59 or a Cambridge C1 Advanced score of at least 180.
  4. GRE scores are not required.
NOTE: This program is authorized, exempt, or not subject to state regulatory compliance and may enroll students from all 50 states, U.S. territories and the District of Columbia.

Application Deadlines

April 19


  • * Core Courses (12 credit hours)
  • DATA_SCI 7600Introduction to Data Science and Analytics3 Credits

    An introductory course in data science and analytics. The objective of the course is to give students a broad overview of the various aspects of data analytics such as accessing, cleansing, modeling, visualizing, and interpreting data. Students will perform hands-on learning of data analytic topics, using technologies such as Python, R, and open source analytic tools. Two Big Data cyberinfrastructure platforms will be introduced through case studies, allowing students to perform data analytical learning modules on modern cloud infrastructure and other relevant technologies. Graded on A-F basis only. Recommended: Basic programming experience and Basic database experience.

  • DATA_SCI 8610Statistical and Mathematical Foundations for Data Analytics 3 Credits

    An intermediate statistics class designed to build the mathematical foundation for students dealing with Big Data phenomena. Topics include discussions of probability, data sampling, data summarization, sampling distributions, statistical inference, statistical pattern analysis, hypothesis testing, regression, and nonparametric inference over multidimensional data collections. Students will engage in Big Data projects using various publicly available data sets and leveraging modern Data Science tools, techniques, and cyberinfrastructure. Graded on A-F basis only. Recommended: Basic understanding of mathematical principles of vectors and matrices, and Basic course in probability and statistics.

  • DATA_SCI 8620Database and Analytics3 Credits

    Covers the Fundamental concepts of current database systems and query methods with emphasis on relational model and non-relational techniques in Big Data environments. Topics include entity-relationship model, relational algebra, indexing, query optimization, normal forms, tuning, security, NoSQL, and data analytics skills in both relational and non-relational environments. Project work involves modern relational DBMS systems and NoSQL environments. Graded on A-F basis only. Recommended: Basic understanding of mathematical principles of vectors and matrices, and Basic course in probability and statistics.

  • DATA_SCI 8650Big Data Visualization3 Credits

    Covers the Fundamental concepts of current visualization concepts and technologies. Unlike many data visualization courses, this one focuses on principles of visualization design and the grammar of graphics. These principles are then implemented in popular contemporary visualization technologies. Students will develop an advanced knowledge of the appropriate selection, modeling, and evaluation of data visualizations. Graded on A-F basis only. Prerequisites: DATA_SCI 7600 and DATA_SCI 8620. Recommended: Basic understanding of mathematical principles of vectors and matrices; Basic course in probability and statistics; Basic course in databases and data analytics.

  • *Students with a limited programming and statistics background are encouraged to take select foundational courses. Please contact DSAMasters@missouri.edu for more information.
  • *Electives (3 credit hours)

    Choose one data science course in consultation with the department.

Tuition & Fees

Missouri Resident Fee Rates
  • Per Credit Hour
  • Tuition: $1075.00
  • Fees: $14.03
  • Total: $1089.03
  • 3 Credit Hours
  • Tuition: $3225.00
  • Fees: $42.09
  • Total: $3267.09
Missouri Non-Resident Fee Rates
  • Per Credit Hour
  • Tuition: $1075.00
  • Fees: $14.03
  • Total: $1089.03
  • 3 Credit Hours
  • Tuition: $3225.00
  • Fees: $42.09
  • Total: $3267.09
Tuition rates are subject to change.

What's it like to take a program online?

  • Earn a quality education online

    With more than 100 online programs to choose from across our four campuses, our online students have incredible access to career-advancing education. Our online courses are developed and taught by the same excellent faculty and instructors who teach the courses on campus. Whether you’re looking for an undergraduate education, graduate education, or a certificate program to further your career, one of our four campuses has an option to fit your needs.

  • Learning that fits your schedule

    Online learning provides students with the flexibility and freedom to attend classes whenever and wherever you are, in a way that is convenient for you. You can save time and money by being able to continue working and by avoiding relocation, travel and commuting costs. Courses must be completed in scheduled time frames, but in most cases, you can log in and complete course work whenever doing so best fits your schedule. Online education means learning on your terms – not the other way around.

  • Service you expect from a renowned University

    Our goal is to provide our online students with an excellent academic experience, without forcing you to make sacrifices in other areas of your life.

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