• Course Delivery
    Blended
  • Total Credits
    34
  • In-State Tuition Per Credit
    $1043.76
  • Out of State Tuition Per Credit
    $1043.76

The online masters degree in data science and analytics from MU will provide you with the knowledge, tools and experiences to turn big data into smart data. If you want to help companies in any industry solve their complex data problems, this may be the degree for you.


This degree will give you:

  • Real-world experience in applying state-of-the-art data science tools and techniques to solve industry, academic, and/or business data and decision-making challenges.
  • A clear understanding of the ethics and security mechanisms required to safeguard large-scale data collections that contain sensitive and critical information.
  • A comprehensive understanding of modern data analytics, statistical analysis and visualization tools that facilitate timely, large data analysis.
  • An understanding of database systems, database design and information retrieval.
  • A demonstrated ability to effectively communicate to a broad audience the relevant information derived from large data collections using a variety of visualization and presentation methods.
  • Training in the latest data analytic methods and tools.


Program structure and topics

Delivery of this program is blended; some campus visits are required.

Courses are semester-based. Students typically finish the program in two years.


Course work covers

The program provides unique curriculum emphases in five different disciplines:

  • Biotechnology
  • Geospatial concentration area (coming soon)
  • High performance computing
  • Human centered design for data
  • Strategic communications and data journalism

Admissions & Requirements

To be accepted to this program, you must have:

  1. 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) or an IELTS score of at least 6.5.
  4. Transcripts of all previous college or university education. Upload unofficial transcripts in your online application. If you are accepted, you will be asked to provide official transcripts.
  5. Letter of interest describing why you wish to pursue a master’s degree in data science and analytics, plus a short biographical statement and any other information you feel might support your application.
  6. Résumé or curriculum vitae.
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

Fall
July 15

Courses

Core
  • 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 Analytics3 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 8630Data Mining and Information Retrieval3 Credits

    The course introduces the main concepts and techniques of data mining and information retrieval. It covers a variety of data mining topics and methods to extract hidden and predictive patterns from large data collections. Furthermore, theory and techniques for the modeling, indexing, and retrieval of relational, non­relational, text­based and multimedia databases is covered. Topics include introduction to data mining process, mining frequent patterns, and pattern analysis, as well as different information retrieval models and evaluation, query languages and operations, and indexing/searching methods. 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.

  • DATA_SCI 8640Big Data Security3 Credits

    This course provides an overview of state-of-the-art topics in Big Data Security, looking at data collection (smartphones, sensors, the Web), data storage and processing (scalable relational databases, Hadoop, Spark, etc.), extracting structured data from unstructured data, systems issues (exploiting multicore, security). Securing sensitive data, personal data and behavioral data while ensuring a respect for privacy will be a focus point in the course Graded on A-F only. Prerequisites: DATA_SCI 7600 and DATA_SCI 8620.

  • 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.

  • DATA_SCI 8660Data and Information Ethics3 Credits

    Introduces the ethics related to Big Data in industry, business, academia, and research settings. Students will learn the social, ethical, legal and policy issues that underpin the big data phenomenon. Discussions and case studies will help guard against the repetition of known mistakes and inadequate preparation. The course content will follow the guidelines to be developed by the Council for Big Data, Ethics, and Society. Graded on A-F basis only. Prerequisites: DATA_SCI 7600 and DATA_SCI 8650.

Tuition & Fees

Missouri Resident Fee Rates
  • Per Credit Hour
  • Tuition: $1043.76
  • Fees: $13.77
  • Total: $1057.53
  • 3 Credit Hours
  • Tuition: $3131.28
  • Fees: $41.31
  • Total: $3172.59
Missouri Non-Resident Fee Rates
  • Per Credit Hour
  • Tuition: $1043.76
  • Fees: $13.77
  • Total: $1057.53
  • 3 Credit Hours
  • Tuition: $3131.28
  • Fees: $41.31
  • Total: $3172.59
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

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