heiSKILLS Certificates Data Literacy
Increasing digitalisation is accompanied by a trend toward collecting, storing, and analysing a wide variety of information from different areas of society. This digital data is also becoming increasingly important in academia and professional life. The ongoing advance of digitalisation therefore requires technical skills in data collection, storage, processing, analysis, evaluation, and visualisation. In addition, it is important to be able to critically examine the findings of computational research and social trends in digitalisation.
TARGET GROUP AND LEARNING OBJECTIVES
Target Group
The Data Literacy Certificate addresses these pressing needs early in the Bachelor’s program by equipping students in the humanities and social sciences with a sound understanding of how data shapes society and with fundamental skills in data-driven research.
Learning Objectives
After completing the certificate program as a student of the humanities and social sciences, you will be able to:
- name digital data sets and their key characteristics.
- recognise and critically evaluate the legal, economic, political, and ethical implications of digitalisation on society.
- understand the basics of programming and apply this knowledge using the R programming language.
- identify specific application areas for digital data sources to address research questions, and analyse and interpret them using standard computational techniques.
- recognise the principal challenges associated with computer-assisted analysis results and critically reflect on them on this basis.
- engage in domain-specific discussions with IT experts as a non-specialist in academic and professional contexts.
Modules
The certificate requires the completion of coursework worth 10 ECTS credits.
Basic: Fundamentals of Digital Data
The first module provides students with an overview of the origins and forms of digital data, as well as their impact on society and research. Students will then learn how to apply these data sources in their studies and beyond. The module also includes a programming course designed to equip students with the essential skills needed to effectively work with digital data in their own projects.
Specialization: Selected Focus Areas
In the second module, students can focus on areas relevant to their individual projects (term papers, theses, etc.). This module offers courses in the following areas:
- Data Collection
- Data Management
- Data Preparation
- Data Analysis
- Data Visualisation
- Data Interpretation and Critical Evaluation
Capstone: Reflection based on an Application Idea
In the third module, the portfolio enables students to document and reflect on their experiences working with digital data through an individual application idea. This process supports the transfer of knowledge to real-world applications.
Modalities for Course Attendance and Certificate Graduation
Each course can be taken and credited independently of the certificate program. Students register via heiCO and, upon successful completion, receive a confirmation of attendance that may be submitted for credit as part of the transferable skills (ÜK). It is recommended to confirm eligibility for recognition with your department before enrolling in the course.
To obtain the certificate, students must complete the basic course, three specialisation courses, and create a portfolio. Finally, students submit their confirmations of attendance and portfolio by email to the certificate coordinator, who will issue the certificate after reviewing the documents: cujai@uni-heidelberg.de
The basic module course does not require any specific prior knowledge. However, some courses in the specialisation module require basic knowledge of the R programming language, which can be acquired in the foundation module if needed. Detailed participation requirements are provided in the course descriptions on the heiCO platform.
The final portfolio consists of three parts, each with a distinct objective:
- Process Documentation: In the first part, students document the courses they attended. This allows them to review and reflect on what they have learned.
- Application of Knowledge: In the second part, students apply their newly acquired knowledge. They develop a research plan demonstrating how this knowledge can be used to answer a specific question in their field. This helps students recognise the practical benefits for their academic work while practicing essential steps for working with large digital text data sets and computational techniques for future projects. Students can also use this task to advance a current or future project, such as a bachelor’s thesis.
- Review and Outlook: In the final part, students reflect on the entire certification process and consider how to apply the knowledge gained to future projects.
Although the portfolio is completed at the end of the certification process, students are encouraged to work on it continuously. In particular, it is advisable to answer the questions for each course promptly, while the content is still fresh in mind. The document can be downloaded at any time to begin working on it.
Do you have questions about the Data Literacy Certificate? Our FAQ provides detailed answers to the most frequently asked questions.