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.
Analyzing and Visualizing Data with Python
COURSE OVERVIEW
In order to be useful within a professional environment, data must be structured in a way that can be understood and applied to real-world scenarios. This course introduces using Python to perform statistical data analysis and create visualizations that uncover patterns in your data. Using the tools and workflows you developed in earlier courses, you will carry out analyses on real-world datasets to become familiar with recognizing and utilizing patterns. Finally, you will form and test hypotheses about your data which will become the foundation upon which data-driven decision-making is built.
It is recommended to only take this course if you have completed Constructing Expressions in Python, Writing Custom Python Functions, Classes, and Workflows, Developing Data Science Applications, Creating Data Arrays and Tables in Python, and Organizing Data with Python or have equivalent experience.
- Carry out analyses on a real-world dataset
- Perform statistical data analysis and visualization
- Use grouping operations to further data analysis
- Use Python to explore your own dataset
How It Works
Course Author
Who Should Enroll
- Data analysts and business analysts
- Database managers
- Technical and systems analysts
- Programmers interested in data science
- Marketers
- Business managers
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