BIOST 2036 - INTRODUCTION TO HEALTH DATA SCIENCE

Minimum Credits: 2
Maximum Credits: 2
This course will teach students methods and concepts in data science that are motivated by real life problems in public health. Students will become familiar with data science terms such as data wrangling. Students will learn the concepts of exploratory data analysis, data cleaning, data wrangling, and visualization. Students will learn the necessary skills to tidy, manage, and visualize data and communicate results. This course will mainly use the R programming language but will also teach certain concepts in SQL and Python. The course lectures will cover the following general themes: data structures and representation, data wrangling and processing, computational tools and techniques, and case studies illustrating steps of analysis of real data, including examples from public health.
Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad Letter Grade
Course Requirements: PLAN: Biostatistics (MS or PHD)

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