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The aim of this course is to improve the working efficiency of graduate students on data analysis projects. There are numerous powerful tools available for scientists to automate manual tasks, and this essential topic is often left for the students to discover while tackling a newly collected dataset in the lab. By learning and taking advantage of these tools, students will become more efficient in handling data heavy projects, avoid problems such as data loss or inability to reproduce certain tasks.
Additionally, the NIH has begun requiring data management and sharing plans in all grants, and funded research must preserve and share their data in a FAIR (Findable, Accessible, Interoperable, and Reusable) manner. How do we integrate good analysis pipelines with datasets? What are the skills required and tools available to help you achieve these principles? At the end of this course, students will be able to answer all these questions, dissect any type of data, and take any new analysis workflow head-on.