University of Arizona 3 credits Biosystems Engineering
This course provides a comprehensive introduction to Python for data analytics focused on the interpretation of biological data. The course is structured as a series of short lectures covering key concepts and analytical strategies using Python and cutting-edge open source packages for data analytics. The majority of the course focuses on hands-on exercises both in- and out- of class to develop practical coding skills for interpreting and analyzing high-dimensional biological data. Students work in a collaborative learning classroom to gain skills in (1) basic Unix and Python, (2) Python data structures functions, and files, and (3) data wrangling and visualization using IPython, NumPy, and pandas, and (4) analytics using machine-learning methods available in Scikit-Learn. These skills are taught by implementing real-world coding examples to manipulate and process biological data in Python, and effectively use data-oriented Python libraries to analyze and interpret data from biological systems.
Our subject experts help with essays, research papers, case studies, discussion posts, and capstone projects for these programs β written from scratch to your course requirements.
Program and course information is reproduced from each university's published academic catalog for informational purposes and may change. Always confirm current requirements with your university. We are an independent academic support service and are not affiliated with or endorsed by the universities listed.
Expert guides on essay writing, citation styles, study skills, and academic success.
View All Articles
Learn how to write a good research question for a paper. Narrow your topic, identify a research problem, and create a focused question with practical examples.
Learn how to choose a research topic you can actually research. Discover how to narrow broad ideas, find reliable sources, and develop a focused research question.
Learn how to cite sources in a research paper using APA, MLA, and Chicago styles, with in-text citation examples and reference-list tips.