Online Master of Translational Data Analytics

Translational Data Analytics Institute

Become an expert in data storytelling with the fully online Master of Translational Data Analytics from Ohio State's Translational Data Analytics Institute. This truly interdisciplinary program is designed for working professionals in fields as diverse as health care, education, finance, government, and the arts who want to apply statistics, machine learning, and user experience and data visualization tactics to uncover and present insights from data.

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Subject matter experts from the departments of Computer Science and Engineering, Statistics, Design, and the Advanced Computing Center for the Arts and Design engage students in practical and experiential learning projects. Through a design thinking lens, students can immediately apply new research methods and in-demand data analysis skills with a focus on developing full-cycle workflows for big data.

Unique in the growing data science landscape, no significant background in analytics and programming is required, and students have the flexibility to take their courses in an asynchronous or synchronous format or a hybrid of the two. Each cohort is carefully curated, placing an emphasis on students' varied backgrounds and disciplines to enrich team-based case studies and projects. Students will spend the final two semesters of the 33-credit program working on diverse teams to complete a workforce-focused capstone project.

Beginning Fall 2021, students will be able to select one of two cohort pathways, either a 5-semester or 10-semester option, at the time of application.

Campus Requirements: 
NONE — 100% Online
Time to Completion: 
5, 8, or 10 semesters, depending on cohort
Clara Davison delivers a presentation.
Clara Davison
Senior Product Analyst
"Arts managers run essential organizations with limited resources. Becoming a master data storyteller will make me an effective arts leader."

Sample Courses


Analytics Foundations I

This class is all about extracting useful information from data, using data to address work challenges, engaging in data-driven decision making under uncertainty, and identifying, sourcing, manipulating, and interpreting data. Software and skills taught in this class include using R programming and analysis using R.

Big Data Computing Foundations I

This class teaches students how to construct schemas that locate, scrape, process, and clean data to develop practical workflows which extract useful information and create usable representations. Focuses on the use of Python and Javascript, as well as tools such as Hadoop and Scala.

Data Governance

Trust in data assets is essential. This skills-based seminar focuses on practical elements of good data governance, privacy and data security through the use of case studies.

Interactive Arts Media

Practice in methods to design and craft user experiences and user interfaces for applications that provide cohesive, satisfying experiences for users. Contemporary methods and software to produce application prototypes. Cohort identification and user testing and research.

Featured Faculty

Associate Professor of Practice, Computer Science and Engineering

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