The University of Missouri’s Data Science and Analytics MS degree

A pioneer in educating companies and individuals on the advantages of using Data Science to its fullest capacity. Students will work closely with faculty mentors, gain experience through solving real-word Big Data issues, explore concepts targeted to their emphasis and more

Required Credits (34 Hours)

  • 19 Credit hours for Data Science Core Curriculum
  • 9 Credit Hours for Emphasis Area courses
  • 3 Credit Hours of Case Study and
  • 3 Credit Hours Capstone
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Innovative courses set the groundwork for an education in data science to advance your career or business.

Core Courses

DATA_SCI 7600

Introduction to Data Analytics

The first course in Data Science and Analytics MS degree and graduate certificate programs. 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 have hands on training in Python, R, SAS, SPSS, and other open-source analytic tools. Concurrent technologies in Big Data ecosystem will be introduced with applications in the emphasis areas.

INFOINST 8610

Statistical and Mathematical Foundation for Data Analytics

An introductory statistics class designed to build the mathematical foundation for students dealing with Big Data phenomena. Topics include reviews of probability, data sampling, data summarization, sampling distributions, statistical inference, statistical pattern analysis, hypothesis testing, regression, and nonparametric inference. Students will have Big Data projects using R, SAS, or SPSS for their course projects using Hadoop.

INFOINST 8620

Database & Analytics

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.

INFOINST 8630

Data Mining & Information Retrieval

Theory and techniques for the modeling, indexing, and retrieval of text-based and multimedia databases. Topics include introduction to different information retrieval models, retrieval evaluation, query languages, query operations, and indexing/searching methods.

INFOINST 8640

Big Data Security

This course provides an overview of state-of-the-art topics in Big Data Security, looking at data collection (smartphones, sensors, internet), 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.

INFOINST 8650

Data Visualization

Data visualization broadly covers transforming multidimensional and time varying datasets to dynamic visual representations and encodings facilitating exploratory data mining, knowledge discovery, improved understanding, summarization, structural modeling, collaboration, and decision making using interactive methods.

INFOINST 8660

Data and Information Ethics

The course will be changed to a modular format containing a series of units targeted to the different “Emphasis Areas”. Students will select from the various modules to create a 1 cr. hr. package suited to their Emphasis Area. Introduces the ethics related to Big Data in industry, business, academia, and research settings. Students will learn the social, ethical, legal, and policy issues underpinning 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.

Advanced Courses

INFOINST 8680

Big Data Analytics Case Study

Using a case-study approach, students will engage in discussions on a variety of big data topics relevant to their emphasis area and the realm of Big Data. This course will help students generate ideas and prepare them for the Big Data Capstone

INFOINST 8690

Big Data Capstone

As a culmination of their data science experience, students will engage in a Big Data Capstone project, incorporating the theoretical knowledge of coursework learned throughout the program into a hands-on practical data project. Students will work closely with faculty mentors and leaders in business, industry, and government to solve real-world Big Data issues in an applied setting.

One Week Intensive

Each spring students will come to campus at the University of Missouri – Columbia for 1 week. Expenses paid as a component of tuition. Expect a week long, intensive application of the core curriculum after the first year. With the execution of a project from beginning to end. In the second year, expect to showcase capstone designs and participate in a week long, intensive collaboration with industry partners.

Pricing

  • 2 YEAR, EXECUTIVE STYLE PROGRAM
  • HIGHLY INTERACTIVE ONLINE COURSES
  • ONE WEEK FACE-TO-FACE EACH YEAR
  • TEAM SUPPORT TOOLS
  • APPROXIMATELY $17,000/YEAR

Faculty

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Sean P. Goggins

Ph.D. – Director, Data Science and Analytics MS Program

Sean Goggins is an Associate Professor at Missouri’s iSchool and the University of Missouri Informatics Institute. He teaches, publishes and conducts research on the uptake and use of information and communication technologies.

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Grant Scott

Ph.D. – Director, Data Science and Analytics MS Program

Dr. Grant Scott is an assistant research professor in the Center for Geospatial Intelligence (CGI) and the Electrical and Computer Engineering Department at the University of Missouri.

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Chi-Ren Shyu

Director, MU Informatics Institute

Chi-Ren Shyu is the chairman of the Electrical and Computer Engineering Department and director of the University of Missouri Informatics Institute.

Trupti Joshi

Computer Science

Ilker Ersoy

Biotechnology, Course Coordinator

Esther L. Thorson

Strategic Communications, Course Coordinator

Curt Davis

Geospatial Engineering

Sanda Erdelez

School of Information Science and Learning Technologies

Dong Xu

Computer Science

Harsh Tenaja

Journalism

Timothy Matisziw

Geospatial Engineering

Twyla G. Gibson

School of Information Science and Learning Technologies

David Herzog

Journalism, Course Coordinator

Yi Shang

Computer Science, Course Coordinator

Jeffrey Uhlmann

Computer Science

Joi Moore

School of Information Science and Learning Technologies, Course Coordinator

Alina Zare

Geospatial Engineering

Industry Partners

A board comprised of industry and business experts in companies vital to Data Science and Analytics has been formed to help assure the program is relevant and responsive to the marketplace, and help ensure the student’s learning experience is relevant to “real-world” technologies, problems and challenges.

sandfordhealth
newyorktimes
monsanto
boeing
kpmg
ibm
google
fleishmannhillard
faircom
expressscripts
dow
dishnetwork
centurylink
siliconvalleydatascience
Deloitte
thomson-reuters
centene