Translational Data AnalyticsMasters of Translational Data Analytics

About us

The Master of Translational Data Analytics is designed to up-skill working and transitioning professionals. With classes taken over five (5) semesters on nights and some weekends, the Masters of Translational Data Analytics works around your life to make you a master data storyteller.

Our program is situated at the crossroads of data, technology, and the human experience. The program is designed to teach you to perform analytics and translate that into data stories for practical impact. We focus on marrying data with context to present results as compelling stories. We do that by embedding design thinking into the foundations of data analytics, computing, programming and machine learning.

With courses which blend traditional classroom learning, seminars and capstones, you’ll engage with real problems from real community partners to expand your network and develop a portfolio of results.

Here are the basics about what we teach:

  1. The core concepts of statistical analysis,

  2. The foundations of big data computing, data mining and machine learning

  3. Information design, the foundations of data visualization and emerging trends in data storytelling

  4. How to assemble and present compelling user experiences and user interfaces

  5. Three skills focused seminars in data governance, research methods and in-demand "soft" professional skills

  6. Appication of your learning in real-world capstones

Our curriculum includes 33 credit hours of instruction:

Semester 1 (3 classes, 7 credit hours):

  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. 

Semester 2: (3 classes, 7 credit hours)

  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. 

Semester 3: (3 classes, 7 credit hours)

  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. 

Semester 4: (2 classes, 6 credit hours)

  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.

Semester 5: (2 classes, 6 credit hours)

  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.