Program Overview
The Master of Translational Data Analytics from The Ohio State University equips working professionals with expertise in data storytelling, combining statistics, machine learning, user experience, and data visualization. The interdisciplinary program offers flexible cohort pathways and a focus on real-world capstone projects, preparing graduates for careers in various industries.
Program Outline
Degree Overview:
Overview:
The Master of Translational Data Analytics from The Ohio State University's Translational Data Analytics Institute is a fully online program designed for working professionals. It aims to equip students with expertise in data storytelling by combining statistics, machine learning, user experience, and data visualization. The interdisciplinary program draws students from diverse fields such as healthcare, education, finance, government, and the arts.
Objectives:
- Develop proficiency in statistical analysis, big data computing, data mining, and machine learning.
- Master the principles of information design, data visualization, and data storytelling.
- Gain expertise in assembling and presenting compelling user experiences and interfaces.
- Enhance skills in data governance, research methods, and design thinking.
- Apply learning to real-world capstone projects that address industry challenges.
Outline:
Content:
- The core concepts of statistical analysis
- The foundations of big data computing, data mining and machine learning
- Information design, the foundations of data visualization and emerging trends in data storytelling
- How to assemble and present compelling user experiences and user interfaces
- Three skills focused seminars in data governance, research methods and design thinking
- Application of your learning in real-world capstones
Structure:
- 33-credit program
- Two cohort pathway options:
- 5-semester option
- 10-semester option
- Flexibility to take courses in asynchronous, synchronous, or hybrid format
- Emphasis on team-based case studies and projects
- Final two semesters dedicated to a workforce-focused capstone project
Course Schedule and Modules:
Teaching:
Teaching Methods:
- Practical and experiential learning projects
- Design thinking approach
- Integration of new research methods and in-demand data analysis skills
Faculty:
- Subject matter experts from the departments of Computer Science and Engineering, Statistics, Design, and the Advanced Computing Center for the Arts and Design engage with students in the program.
Unique Approaches:
- No significant background in analytics and programming is required for admission.
- Hybrid learning format allows students to customize their learning experience.
