Spring 2024
Digital Repositories
This course offers a complete immersion into the world of Data Science, Information Systems, and Digital Repositories. It is designed to give students a deep understanding of the theoretical concepts and practical skills needed to navigate and lead in the field of information technology.
Staff
Syllabus
Download the full syllabus as a PDF.
Syllabus (PDF)Topics
Introduction to Data Science and Information Systems
- Overview of Data Science and Information Systems
- Key Tools and Technologies
Digital Repositories
- Fundamentals of Digital Repositories
- Content and Metadata Management
- Interoperability and Metadata Standards
- Semantic Web and Knowledge Graphs
- Case Studies
Organizing Information
- Taxonomies and Hierarchies for Data Organization
- Ontologies
- Metadata Standards
- Folksonomies and Tagging
- Data Cataloging Processes
- International Metadata Standards
Information Systems Architecture
- Fundamentals of Systems Architecture
- Hardware and Software Components of an Information System
- Systems Design and Modeling
- Databases and Data Storage
- Current and Future Trends in Systems Architecture
- Systems Integration and Data Migration
Computing in Digital Repositories
- Introduction to Computing in Digital Repositories
- Data Processing and Analysis in Repositories
- Computational Methods for Large-Scale Data
- Distributed Computing in Data Science
- Tools and Frameworks for Computing in Repositories
- Data Visualization and Reporting
Legal and Ethical Aspects
- Intellectual Property Protection in Data Science
- Data Privacy and Security
- Ethical Considerations in Data Handling
Funding Management and Strategies
- Fundamentals of Information Technology Funding
- Cost Models for Information Systems and Digital Repositories
- Crowdfunding and Collaborative Financing
- Business Models and Revenue Generation for Digital Repositories
Grading
- 20%
Assignments
Short exercises and case studies related to the course topics.
- 20%
Student Presentations
Presentations on specific course topics.
- 15%
Quizzes
Mid-course assessments.
- 25%
Final Exam
Written evaluation at the end of the course covering all topics.
- 20%
Practical Project
Projects involving the practical application of the course's concepts.
Readings
- Downey, A. (2018). Elements of Data Science. O'Reilly Media, Inc.
- Glushko, Robert J. (2013). The Discipline of Organizing (4th Professional ed.).
- Dietrich, David, Barry Heller, and Beibei Yang. (2015). Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data. Wiley.
- Schneier, B. (2015). Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World. WW Norton & Company.
- Davis, K. (2012). Ethics of Big Data: Balancing Risk and Innovation. O'Reilly Media, Inc.
- Jones, R. E., Andrew, T., & MacColl, J. (2006). The Institutional Repository. Elsevier.
Final Project
COVID-19 Data Analysis in Latin America
Teams collected, harmonized, and analyzed open COVID-19 data from Latin American countries, then presented their findings through an interactive dashboard and a final report.