1 Syllabus
Our overall learning outcome is that by the end of the course:
Learners will be able to describe the core details of a server environment, how it differs from working locally. They will explain the special considerations needed for conducting reproducible research in this type of environment. Using that knowledge, they will be able to identify storage formats and computational approaches that efficiently and optimally use the server resources for working with large data. Learners will apply these techniques and practices by using R.
Our specific learning objectives are:
- List common types of servers available for working with data and their strengths and weaknesses (such as their technical specifications), and recognize which type they are working on with their own projects (and in the course).
- Describe common types of data storage formats, how they can affect data analysis, and which types are better suited for server settings.
- Apply pipeline management tools and then identify and select settings that optimize use of server resources to minimize computing time and resource usage.
- Recognize and identify potential issues for privacy and security, and use specific strategies to minimize risk.
- List specific strategies for effectively working with and prototyping on larger data to minimize computing time and resource use.
- Continue to build core reproducible research practices by using version control and reproducible documents.
Maybe?
- Understanding how RStudio can be setup on server environment (different from working on RStudio on a desktop)
- When to do local work vs on the server (server’s cost money and resources)
We will not cover:
- Any?
Because learning and coding is ultimately not just a solo activity, during this course we also aim to provide opportunities to chat with fellow participants, learn about their work and how they do analyses, and to build networks of support and collaboration.
The specific software and technologies we will cover in this course are R, RStudio, Git (and maybe GitHub), …, while the specific R packages are …
1.1 Is this course for you?
To help manage expectations and develop the material for this course, we make a few assumptions about who you are as a participant in the course:
- Assumptions
While we have these assumptions to help focus the content of the course, if you have an interest in learning R but don’t fit any of the above assumptions, you are still welcome to attend the course! We welcome everyone, that is until the course capacity is reached.
In addition to the assumptions, we also have a fairly focused scope for teaching and expectations for learning. So this may also help you decide if this course is for you.
- List of what we will teach and won’t teach