This course focused on manipulating, combining, and making available datasets generated during my PhD. The first section of the course focused on learning SQL (Structured Query Language), a programming language used for communicating with and extracting data from databases, and other Python packages for data manipulation. The second part of the course focused on creating and managing code in GitHub and Jupyter Notebooks for science reproducibility.
Learning Objectives:
- Gain experience in manipulating and extracting information from several datasets with SQL and Python, including familiarity with the structure and tools available for data management.
- Learn how to process and make available datasets and pipelines written in different programming languages.
- Apply this to datasets needed for use in my dissertation.