MPM 200: Introduction to Information Management for Epidemiologists

Course Overview

MPM 200 — Introduction to Information Management for Epidemiologists (1 unit) Course Description: Introduction to the practical application of epidemiological methods to solve problems involving population health data. Emphasis on using worksheet/database software tools for organizing, analyzing, reporting, and interpreting data.

Session 5: Introduction to R Programming and Collaborative Science

Objectives

• Introduction to R Programming: Learn the basics of the R programming language, a crucial tool for data analysis in epidemiology.
• Project Management: Understand the fundamentals of project management in the context of epidemiological research.
• Collaborative and Reproducible Science: Explore the principles of collaborative and reproducible science, including the use of various Integrated Development Environments (IDEs) such as Jupyter and Anaconda.

Key Topics

• Basic R syntax and data types
• Setting up and using Jupyter and Anaconda environments
• Best practices for collaborative research and reproducibility

Session 8: Collaborative Analysis with Version Control Using GitHub

Objectives

• Version Control with GitHub: Learn how to collaboratively work on analyses using version control systems, specifically GitHub.
• GitHub Desktop: Understand key concepts of GitHub using the Desktop application.

Key Topics

• Creating and managing repositories
• Committing and pushing changes
• Collaborating with team members using GitHub
• Resolving conflicts and managing branches

Session 9: Introduction to Python and Data Management for Epidemiologists

Objectives

• Introduction to Python: Learn the basics of the Python programming language and compare it with R.
• Data Management and Database Development: Explore the basics of data management and database development specifically for epidemiologists.

Key Topics

• Basic Python syntax and data types
• Comparison of R and Python: pros, cons, and use cases
• Data management principles: data cleaning, data storage, and database development
• Using Python libraries for epidemiological data analysis (e.g., Pandas, NumPy)

Resources

• Course materials and examples will be available on this GitHub repository.
• Additional resources and links to relevant tools and software will be provided throughout the sessions.

Participation and Engagement

Active participation in class discussions, hands-on exercises, and collaborative projects is expected. Students are encouraged to ask questions and share their experiences to enhance the learning environment.

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For more details and updates, please refer to the course schedule and announcements on this GitHub repository. If you have any questions or need further assistance, feel free to reach out to the course instructors.

Dr. Pranav Pandit: [email protected]
Dr. Sharif Aly: [email protected]