Masters in Translational Data Analytics

Masters in Translational Data Analytics

Masters in Translational Data Analytics (MTDA)

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About the MTDA

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.

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.

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

 

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Two pathways to becoming a Master Data Storyteller

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Semester 1 - Fall (7 Credit Hours)

  • Data Analytics Foundations I
  • Big Data Computing Foundations I
  • Seminar I: Data Governance

Semester 2 - Spring (7 Credit Hours)

  • Data Analytics Foundations 2
  • Big Data Computing Foundations 2
  • Seminar II: Research Design

Semester 3 - Summer (7 Credit Hours)

  • Practical Learning and Mining for Big Data
  • Information Design
  • Seminar III: Professional Development

Semester 4 - Fall (6 Credit Hours)

  • Interactive Arts Media
  • Capstone I

Semester 5 - Spring (6 Credit Hours)

  • Emerging Trends in Data Visualization
  • Capstone II
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Semester 1 - Fall (4 Credit Hours)

  • Data Analytics Foundations I
  • Seminar I: Data Governance

Semester 2 - Spring (4 Credit Hours)

  • Data Analytics Foundations 2
  • Seminar II: Research Design

Semester 3 - Summer (4 Credit Hours)

  • Information Design
  • Seminar III: Professional Development

Semester 4 - Fall (3 Credit Hours)

  • Big Data Computing Foundations I

Semester 5 - Spring (3 Credit Hours)

  • Big Data Computing Foundations 2

Semester 6 - Summer (3 Credit Hours)

  • Practical Learning and Mining for Big Data

Semester 7 - Fall (3 Credit Hours)

  • Interactive Arts Media

Semester 8 - Spring (3 Credit Hours)

  • Emerging Trends in Data Visualization

Semester 9 - Fall (3 Credit Hours)

  • Capstone I

Semester 10 - Spring (3 Credit Hours)

  • Capstone II

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Course Listing

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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.

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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. 

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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. 

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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. 

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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. 

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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. 

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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. 

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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. 

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Seminar III: Professional Development (1 CREDIT HOUR):Professional skills development focused on communications, project management, leadership and teamwork, and professionalism and work ethic. 

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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.  Syllabus TBD

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.  Syllabus TBD

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.  Syllabus TBD

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.  Syllabus TBD

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Frequently Asked Questions

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“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.

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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!

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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.

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

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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.

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University Cost Estimates

  • Ohio State Application Fee:  $60 one-time fee ($70 for international students)

Program Cost Estimates

  • Instructional Fees (Tuition):  $39,311.58 (for 5 semesters)
  • Distance Learning Fee:  $100 / semester
  • Non-Resident surcharge:  $200 / semester

Total Cost Estimates

  • Ohio Residents:  $39.811.58
  • Non-Ohio Residents:  $40,811.58
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The MTDA is now designated as a fully distance learning program, which means it can be completed from anywhere. We offer a combination of synchronous and asynchronous instruction for maximum flexibility for working professionals.

As a benefit to enrollment, we're also offering optional drop-in access to some of TDAI's collaboration spaces if you or your group wants to work or study together.

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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.

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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
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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

Notes:

  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.
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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

-OR-

    • 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 tdai-mtda-contact@osu.edu with a rationale of why you are seeking an exemption and under what circumstances.

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

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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.
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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.

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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.
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The Ohio State University is authorized to offer the MTDA in all 50 states. For more information on state authorization requirements and disclosures related to online programs, please visit online.osu.edu/state-authorization/disclosures.

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Read more about Graduate Admissions HERE