Biomedical Data Science in Python: As biology is saturated with complex datasets that have to be sorted, explored, and “looked into”, anyone handling data generation, analysis or decision-making based on data has to gain some level of “data science” skills. The increasing necessity to process big data and develop algorithms in all fields of science means that programming is becoming an essential skill for scientists, with Python the language of choice for the majority of bioinformaticians. In most biological and biomedical settings, you will be expected to run or implement programs written in Python, R, and others.
This course will cover practical and conceptual aspects of machine learning in application to high-throughput biomedical data using various tools and Python. Throughout the course, students will get an understanding of the opportunities and limitations of machine learning in the context of pre-clinical and clinical research. The course is designed as a combination of online resources, practical assignments, and live workshops that will be conducted online. Throughout the course, we will review several project examples that demonstrate the successes and limitations of conventional machine learning (ML) methods and deep learning (DL) using data from public repositories.
How is Python used in Bioinformatics?
Bioinformatics can be defined as “the application of computational tools to organize, analyze, understand, visualize and store information associated with biological macromolecules”. Dealing with data efficiently to process, analyze, visualize and annotate will ultimately require some coding – even if the code launches other scripts developed by a more experienced programmer. Therefore, everyone dealing with data (and especially omics data) needs to develop an understanding of how to read, write, change or optimize code.
Learn more and Register for the program: Biomedical Data Science in Python (omicslogic.com)