BRWN-200007 - Introduction to Python for Public Health Data Analysis

Registration deadline: June 4th This course will be conducted via a virtual Zoom meeting online format and Canvas. Access to a computer/laptop with internet access is required. Please contact Professional Development with any questions. 15 CEUs/CPH units - (7.5 live, 7.5 self-paced)   This course will introduce students to the fundamentals of the Python language, common Python modules for data manipulation and analysis, and Jupyter notebook environment. The course will begin with how to acquire data from publicly available sources and databases, cleansing and transformation of data, and the creation of descriptive statistics and graphics.   The course will also introduce Python's natural language processing and machine learning modules for basic data classification and predictive modeling applications. Throughout the course, instruction and assignments will promote best practices for creating programs that can be shared and used for reproducible research.   Note: Students taking this class should have experience doing data preparation or data analysis within the last 5 years. This could be demonstrated through previous work with statisical packages like R, SAS, SPSS, or Stata, or advanced data manipulation and analysis in Excel or Business Intelligence tools such as Tableau or Qlikview. Prior programming experience in Python is not required. The course will consist of daily live Zoom class meetings from 2:00 - 3:30 p.m., and additional course material in Canvas.   Class size is limited to 20.   $650 General admission** **Available discount class code may be entered during check out for the following: SI001 - $450 Non-profit/government employees (1st Summer Institute class) SI002 - $400 Non-profit/government employees (Additional Summer Institute classes) This class will include both degree-seeking graduate students and practicing professionals. Individuals registering through Professional Development will receive continuing education units - but not academic credit - for the class.