University of Maryland

Data Literacy & Evidence Building

University Of Maryland | Coleridge Initiative

Past Teams’ Presentations

Class content

Class participants will develop the key data analytics skill sets necessary to scope a real world project using real world data, as well as develop an analytical product and apply that product to practice. It trains analysts how to recognize and deal with some of the common pitfalls in terms of making sense of data, identifying and minimizing bias, and communicating clear operational information to elected or appointed officials and other policy-makers. Each step of the way participants will learn how generative AI can fit into the workflow.  It is explicitly designed to respond to the recommendations of the Commission on Evidence-based Policymaking, the Foundations of Evidence-based Policymaking Act of 2018, and the recommendations of the Advisory Committee on Data for Evidence Building.

Participant requirements

The class is designed for agency managers who wish to use data and evidence for decision making, agency staff who will learn and apply these skills to their state data, as well as public policy graduate students, who are seeking a stronger foundation in data literacy. Participants are expected to have some experience in working with data and applying it to real world problems, and some statistical or computer science training. They must also be passionate about the value of using data to produce evidence to inform decisions. In order to be awarded the certificate, participants have to attend each lecture and the following group meeting. Participants must also fully contribute to the group project and presentations.

Class structure & use case

The class is structured to learn data literacy through value creation: by working hands on Its design offers hands-on training in how to make sense of and use large scale real world heterogeneous datasets in the context of addressing real world problems, such as COVID-19. Participants work in teams to build networks by using education and workforce data to develop measures of job quality, and education credentials. The use case is the KYStats Multi-State Post Secondary Report, which was highlighted in the final report of the Advisory Committee on Data for Evidence Building (ACDEB).

Class logistics

The lectures and group sessions will be held 12-2 Eastern via zoom. The first block of classes for Fall 2024 will be on September 4, 5, 11, 12, 18, 25, 26, with an interim presentation October 3. The second block will be October 9, 10, 16, 17, 23, with the final presentation October 30. We will be sending more detailed information about preparatory work and team assignments in late July/early August. We will also send payment information at the beginning of July, through the University of Maryland payment portal.

Example of class dates


The textbook is authored by two of the instructors: Dr. Frauke Kreuter and Dr. Julia Lane
Big Data and social science: Data science methods and tools for research and practice. CRC Press, 2020 by Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, and Julia Lane, eds. The team will use the class to develop the third edition of the textbook, which will also be published by CRC press. Participants will also have the opportunity to provide examples – code, presentations, projects etc. – that will be featured online as part of the text book supplemental materials (and will be credited as contributors).

Program Details

Dates: 2024; September 4, 5, 11, 12, 18, 19, 25, 26, October 3, 9, 10, 16, 17, 23, 30
Commitment: 2 hours lecture, 2 hours group work per contact week (7 weeks total, Wednesdays and Thursdays); additional group presentation preparation time
Format: Online
Program Cost: $6,000 for full 12 class program
Note: Partial scholarships (up to $3,500) available for eligible state and local government employees and enrolled students in public policy programs.


Much of the content for this course was generated by the original NYU-Accenture-KYStats-UMunich-UMD-OSU-Coleridge team.  The lead individuals from each institution included Julia LaneXiangyu Ren, and Rafael Ladislau Alves (NYU), Jared Dubin (Accenture), Angie Tombari (KYStats), Frauke Kreuter (UMD and U Munich), Caro Haensch (NYU and U Munich),  Jody Derezinski Williams (UMD), Tian Lou (OSU), and Jessica Cunningham (Coleridge).  They drew on over a decade of successful experience in training government agency staff and researchers in how to work with real world data to develop projects that could go from products to practice.