Professor Myers has been with CAC since 2017, having previously been a member of the research staff of the Bioinformatics Facility of the Institute of Biotechnology (2007-2017) and the Cornell Theory Center (1993-1997, 1998-2007). In addition, Professor Myers is an Adjunct Professor in the Department of Physics at Cornell and a member of the graduate faculty in the fields of physics, computational biology, applied mathematics, and computational science and engineering. Professor Myers works primarily in the field of computational biology, addressing problems in the systems biology of cellular regulation, signaling, metabolism, development, virulence and immunity, as well as in host-pathogen interactions and the spread of infectious diseases on populations, networks, and landscapes.
Python is much more than a programming language. In this course, you will leverage the comprehensive Python ecosystem of libraries, frameworks, and tools to develop complex data science applications. Throughout this course, you will practice using the different Python tools appropriate to your dataset. You will leverage library resources for data acquisition and analysis as well as machine learning. Dataframes will be introduced as a means of manipulating structured data tables for advanced analysis. Additionally, you will practice basic routines for data visualization utilizing Jupyter Notebooks.
It is recommended to only take this course if you have completed Constructing Expressions in Python and Writing Custom Python Functions, Classes, and Workflows or have equivalent experience.
- Import Python modules and libraries to access additional functionality
- Use Python libraries for data science
- Choose the right Python tools for your datasets
- Use Jupyter Notebooks to integrate data analysis, visualization, and documentation
How It Works
Who Should Enroll
- Data analysts and business analysts
- Database managers
- Technical and systems analysts
- Programmers interested in data science
- Business managers