Designed to up-skill and re-skill in-career and transitioning professionals, The Masters of Translational Data Analytics transforms you into a one of a kind master data storyteller. Our program embeds design thinking into the foundations of data analytics, data computing and programming and machine learning. You’ll get everything you need to be able to assemble and translate end-to-end big data workflows into compelling stories and user interfaces.

Your courses will be a blend of traditional classroom learning, seminars, and capstones with a focus on taking what we teach and applying it through experiential learning.

Here’s what we teach:

  • 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 interfaces
  • Skills focused seminars in data governance, research methods, and in-demand professional skills
  • Applied real-world capstones

Employers need these skills

  • 95% of employers say data science and analytics skills are hard to find
  • There will be an estimated 2.72 million job postings in data science and analytics in 2020 (67% analytics enabled and 23% data science)
  • Only 23% of educators say graduates will have the skills they need
  • 69% of employers prefer those with these skills than those without

Source: Investing in america’s data science and analytics talent: The case for action, April 2017 Business Higher Education Forum and PwC report

Your journey to being a master data storyteller



  1. Data Analytics Foundations I (3 CREDIT HOURS): 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.
  2. Big Data Computing Foundations I (3 CREDIT HOURS):  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.
  3. Seminar I: Data Governance (1 CREDIT HOUR): 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.



  1. Data Analytics Foundations 2 (3 CREDIT HOURS): Part II of a two semester sequence. This class layers in more advanced R programming and analysis using R. It teaches distribution theory via simulation, statistical modeling such as A/B testing, ANOVA, multiple linear regressions, logistic regression and multivariate analysis, while also emphasizing the communication of results.
  2. Big Data Computing Foundations 2 (3 CREDIT HOURS): Part II of a two semester sequence. This class focuses on creating scalable data organizations and access to high-performance computing workflows for Big Data. Cloud computing, data organization, data warehousing, and basic and advanced data structures are emphasized.
  3. Seminar II: Research Design (1 CREDIT HOUR): The second skills based seminar gives a general overview of research methods which are common across disciplines. Topics include formulating research questions to conducting specific analytical methodologies and writing up/presenting results.



  1. Practical Learning and Mining for Big Data (3 CREDIT HOURS): Split into two modules focusing on (1) practical and scalable data mining and (2) scalable machine learning. You’ll learn how to build practical workflows to mine associations and patterns, classify data and build recommendation systems for data and questions.
  2. Information Design (3 CREDIT HOURS): Explores relationship between data visualization and design. Presents programming skills and design strategies to structure and visualize information to create effective communications and stimulate viewer attention and engagement.
  3. Seminar III: Professional Development (1 CREDIT HOUR):Professional skills development focused on communications, project management, leadership and teamwork, and professionalism and work ethic.



  1. Interactive Arts Media (3 CREDIT HOURS): 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.
  2. Capstone I (3 CREDIT HOURS): Experiential training for students in data analysis with design thinking on non-trivial data. Students formulate data questions and create complete workflows. Emphasis on teamwork, translational competency and professional competency in data rich environments by deploying and using computing technology, data analysis methods and creation of user interfaces.



  1. Emerging Trends in Data Visualization (3 CREDIT HOURS): This course enables students to explore new and emerging visualization approaches, topics and trends in visualization research and their applications.  Students will research, write about, create, and experience visualization trends.
  2. Capstone II (3 CREDIT HOURS): Part II of two semester sequence. Culminating experience through direct engagement with community partners who will formulate challenge questions and provide data. Emphasis on teamwork, translational competency with added emphasis on processing of large data and interpretation of domain specific results. Illustrates data storytelling through deployment of scalable computing technology, data analysis and user interfaces.
Frequently asked questions
What does "translational data analytics" mean?×

“Translational” is often used in the clinical sciences to mean that we act as a bridge between “bench” research and the patient’s bedside. In this context, “translational” means that you’ll be able to perform specialized analytics and translate that into accessible and understandable data stories which people can easily understand.

Do I need programming or other technical experience?×

Students from a variety of backgrounds can and have been successful in our program. That includes many students without prior experience in programming or data analysis experience. In other words, we don’t require you to have any previous experience in programming or data analysis. Your courses will give you what you need to get up to speed.

However, students who come in with little to no previous experience may find their learning curve to be steeper during the early semesters of the program. If you are someone without a lot of technical experience in Python, R, or a firm statistics background, we recommend that you consider supplemental preparation before you begin with us in the Fall. There are many online resources and courses, as well as reference materials which will help you learn the foundations of these before you start with us. Contact us to discuss what some of these options are!

Who are you looking for?×

We’re looking for curious, hardworking problem solvers who thrive in team environments. Our program is built to accommodate a range of backgrounds, so you don’t need to be a computer scientist, statistician or data analyst to be successful with us.

What counts as work experience?×

Any work experience! It is important to highlight how it is relevant to the program.

How big is the program?×

We’re aiming to have around 30 people per cohort. In a lock-step cohort based program like ours, we know that small environments mean a lot more individual attention for our students.

How much will the program cost?×

You should always check to make sure you have the most up to date figures. You can access the per semester tuition tables for the University HERE.

Estimated resident tuition and fees are below (figures may change over time)*:


Semester Credit Hours Instructional Cost General Fee Union Fee Activity Fee Recreation Fee COTA RESIDENT TOTAL
1 7 8338.82 161 65.1 37.5 123 13.5 8738.92
2 7 8338.82 161 65.1 37.5 123 13.5 8738.92
3 7 8338.82 161 65.1 37.5 123 13.5 8738.92
4 6 7147.56 138 55.8 37.5 123 13.5 7515.36
5 6 7147.56 138 55.8 37.5 123 13.5 7515.36
39311.58 759 306.9 187.5 615 67.5 41247.48


Estimated non-resident tuition and fees are below (figures may change over time)*:

Non-Resident Surcharge per credit hour 1544.75
Semester Credit Hours Base Instruction + Fees Non-Resident Fee Non-Resident Total
1 7 8738.92 10813.25 19552.17
2 7 8738.92 10813.25 19552.17
3 7 8738.92 10813.25 19552.17
4 6 7515.36 9268.5 16783.86
5 6 7515.36 9268.5 16783.86
41247.48 50976.75 92224.23
How is the program delievered?×

In light of the coronavirus pandemic,  FALL ’20 and SPRING ’21 classes have been moved online. We are in process of pursuing a fully online format for future semesters to provide consistency for students. We will not be formally recognized as an online program until we are approved through the state of Ohio. Stay tuned for updates for subsequent semesters.

Fall ’20 and Spring ’21 classes may be completed either synchronously or asynchronously via recordings. Classes take place during evenings during the week.

What do you mean by experiential learning?×

We know that learning is best when it’s applied to real-world problems. We work with a network of real community partners who will provide us with challenge problems and data sets. That way, you’re learning by engaging with real problems and developing a portfolio of results. Our program also integrates a two-semester sequence of capstones where you will integrate and apply everything you’ve learned.

What are your minimum criteria?×

Our minimum criteria are below:

  1. An earned bachelor’s degree or higher in any subject,
  2. At least 1 year of professional experience in a field relevant to the degree program,
  3. A 3.0 cumulative GPA from all courses from all course attempts,
  4. ANY of one of the following graduate level standardized tests (ex: GRE, GMAT, PCAT, MCAT, OAT or LSAT, no minimum score is required; see “What do I need to complete the application for testing exemption criteria”

*For International Applicants

  1. We accept the Graduate School minimums of the TOEFL, MELAB or IELTS
  2. For applicants with no access to the TOEFL Home Edition in China and Iran, the Graduate School will temporarily accept Duolingo English Test examination results. Results will be accepted for these Chinese and Iranian applicants for Autumn Semester 2020 only. The Graduate School encourages applicants to take the TOEFL Home Edition or iBT if it is available (or when it becomes available) in preference to Duolingo. The Duolingo English Test does not provide sub-score breakouts and cannot be used to satisfy the English proficiency requirement for graduate teaching associates (GTA). Graduate students who submit a Duolingo score for admission will need to take the TOEFL at a later date upon arrival to campus if planning to hold a GTA position
What do I need to complete the application?×

What you’ll need to complete the application:

  1. Transcripts from every institution where you’ve attended and taken courses (except Ohio State),
  2. Any one of of the above Graduate Level test scores, if you don’t meet the exemption criteria,
  3. A statement of purpose (1 page),
  4. 3 essay questions (250 words max),
  5. A Resume/CV,
  6. 3 letters of recommendation


  1. LETTERS OF RECOMMENDATION: At least two should be professional in nature. If possible, they should attest to your experience using data, data analytics or data science in your work context. They may also address the program’s benefit to your work and why you are a good candidate for admission.
  2. RESUME/CV: When possible it should reflect your work with any of the following: data, data analytics, data science, statistical or computer methods, data visualization, user experiences, user interfaces, information design and/or other relevant skills not detailed elsewhere on your application.
What are your testing exemption criteria?×

Students are eligible for a test exemption if they meet either of the two profiles below:

    • OPTION 1: 
      • Earned 3.2 GPA cumulative undergraduate GPA
      • Earned a B or better in 2 college-level quantitative and/or computing courses
      • 3 years of professional experience


    • OPTION 2:
      • Earned a 3.5 GPA cumulative undergraduate GPA
      • Earned a B or better in 2 college-level quantitative and/or computing courses
      • 2 years of professional experience

**Students who do not meet either of these profiles may still be granted an exemption based on a combination of education and experience. Please email us at with a rationale of why you are seeking an exemption and under what circumstances.

Do you interview applicants?×

Yes, we do plan to have a brief conversation with applicants prior to making a final admissions decision.

What are the prompts for the statement of purpose and essays?×

Personal Statement Prompt (1 page maximum): In case study format containing (a) a statement of problem, (b) approach and analysis, and (c) results, insight or impact: Describe an example from your professional experience about how you have or could have applied data analytics to solve a business or other work challenge.

Essay question prompts (250 words max):

  1. What in your life experience or background will contribute to the program/your success in the program?
  2. Why is diversity and inclusion important in the work and learning environment?
  3. Describe why you are interested in the program and why you are a good candidate for admission.
What are your deadlines?×

For FALL 2021:

Domestic students should apply no later than: 8/02/2021

International students should apply no later than: 06/02/2021

We conduct rolling review of applications. We highly suggest you turn in your complete application before these deadlines to give yourself the best chance of admission.

Can you tell me more about what the program is doing about diversity and inclusion?×

The vibrant diversity of thought, skills, backgrounds and identities reflected in our community is one of our greatest strengths. The Masters of Translational Data Analytics is committed to creating a program which includes and honors the identities, skills, thinking and working styles and personal backgrounds of those in it.

Our diversity and inclusion goals are to:

  1. Shape a class with a diversity of backgrounds, experiences and identities which reflects our community,
  2. Expand the representation and opportunity of populations underrepresented in data science and analytics,
  3. Make careers in data science and analytics accessible and attainable to those of non-traditional backgrounds,
  4. Create a culture and environment which allows us all to thrive and succeed,
  5. Engage in robust dialogues across difference in a safe and comfortable environment.

Read more about Graduate Admissions HERE

Apply ONLINE today