metadata
Why?
Metadata plays a critical role in ensuring data is discoverable, interpretable, and reusable. In data science, well-managed metadata supports efficient data preprocessing, integration, and governance. This course equips students with the skills to understand, generate, and work with metadata effectively—an essential skill for large-scale data projects and collaborative environments.
What?
This course introduces students to the principles and applications of metadata in data science workflows. Topics include metadata creation, standards and frameworks, its role in preprocessing and cleaning, and practical tools such as Pandas. Students will learn how to leverage metadata to automate and optimize data preparation tasks.
Curriculum:
Data Cleaning
Identifying and correcting errors in datasets, handling missing values, detecting duplicates and outliers, and preparing data for analysis. Introduction to reproducibility in cleaning workflows through metadata tracking.
Metadata in Data Preprocessing
Understanding how metadata is used to guide and document preprocessing steps such as filtering, encoding, and transformation. Learning to use metadata to maintain consistency and traceability in data pipelines.
Metadata Standards and Frameworks
Overview of common metadata standards (e.g., Dublin Core, schema.org), their roles in data documentation and sharing, and how frameworks are used to ensure data interoperability in diverse environments.
Pandas
Using the Pandas library to manage and explore metadata in structured datasets. Techniques for labeling, summarizing, and inspecting data, with emphasis on good metadata practices in DataFrames.
Notes
Metadata might sound abstract at first, but it’s actually everywhere in data work—from column names and file formats to data cleaning logs. Try to think of it as 'data about your data' that helps make everything clearer, more traceable, and reusable.
