Section 1.7 Data Science Workflows
The phrase "data science workflow" describes the method or steps by which a data scientist might evaluate data to perform a data analysis from start to finish. In this course we will provide examples of workflows with real data. In general, workflows involve the following steps:
-
Identifying a question of interest - determining if it is feasible
-
Identifying data to answer that question
-
Importing that data into a programming language such as R
-
Cleaning / wrangling / and tiding the data
-
Exploratory data analysis to get to know the data
-
Data analysis to look for associations in the data
-
Generation of data visualizations to demonstrate findings
-
Communication of your analysis and findings
We will demonstrate potential ways of organizing a workflow using real data from the Open Case Studies project.
