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.
Most data science projects that use Python will require you to access and integrate different types of data from a variety of external sources. This course will give you experience identifying and integrating data from spreadsheets, text files, websites, and databases. To prepare for downstream analyses, you first need to integrate any external data sources into your Python program. You will utilize existing packages and develop your own code to read data from a variety of sources. You will also practice using Python to prepare disorganized, unstructured, or unwieldy datasets for analysis by other stakeholders.
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, and Creating Data Arrays and Tables in Python or have equivalent experience.
How It Works
Who Should Enroll
- Data analysts and business analysts
- Database managers
- Technical and systems analysts
- Programmers interested in data science
- Business managers