A rapidly increasing number of applications in industry, academia, and everyday life are – or should be – based on careful analysis of data. With more and more data sets being easily available, some industries have described themselves as “drowning in data”. This course aims to communicate that anyone and everyone needs to know how to be data-curious, how to access data, and how to analyze data. In this course students will learn to appreciate that with the right tools from statistics and computer science we can learn to take advantage of the growing amounts of data without drowning in it. The course will explore the topics such as reproducibility of data analyses (with the consistent use of literate programming and version control tools throughout the course) as well as data privacy, data sharing, and data science ethics, which are becoming increasingly more important in today’s society.
The course highlights tools and techniques from biostatistics, mathematics, and computer science to introduce students to various facets of data analysis such as data visualization, and wrangling; data management to be able to access data quickly and reproducibly; exploratory data analysis to generate hypotheses and provide intuition; modeling to understand and quantify patterns and prediction; and effective communication of results using visualizations and interpretable summaries.
As part of each class, assignment, and assessment, students will use data analysis skills to solve problems of relevance to global health and present their process and their results as fully reproducible written reports as well as periodic oral presentations.