Employers need these skills

7 CREDIT HOURS

3 CLASSES

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.

7 CREDIT HOURS

3 CLASSES

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.

7 CREDIT HOURS

3 CLASSES

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.

6 CREDIT HOURS

2 CLASSES

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.

6 CREDIT HOURS

2 CLASSES

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.

“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.

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.

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

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.

Tuition for next Fall isn’t yet set for the University. However, it but should be soon. We are cost competitive with other data science and analytics programs nationwide and do everything we can to keep your costs as low as possible, while also making sure you get the support you need to succeed.

The program is in-person on nights and weekends. When classes will be offered will vary, but we know it’s best to keep your classes condensed to just a few days a week.

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.

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”)
5. *For International Applicants, we accept the Graduate School minimums of the TOEFL, MELAB or IELTS

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.

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

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

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.

For FALL 2020:

Domestic students should apply no later than: 8/04/2020

International students should apply no later than: 06/09/2020

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.