Data science programming presents many challenges for programmers entering the field. Roughly, data science programming can be broken up into several activities: data wrangling, analysis, modeling, or visualization. Data wrangling is an important first step that involves cleaning and shaping tabular data--or dataframes--into a form amenable for…

Data science encompasses the most prominent collection of methods for creating scientific knowledge in the 21st century. Currently, data scientists must navigate a wide-ranging and often incoherent ecosystem of tools, in addition to organizing sociotechnical interactions with colleagues across many fields of expertise. This predicament motivates…