Note: This series takes place on Tuesdays, Sept 26-Oct. 31 from 3:30-5:00 PM
Enrollment will be capped at 25 - priority will be given to those who can commit to attending the majority of sessions.
REGISTER HERE
This six-part workshop series is meant to give non-specialists a practical foundation for working with data and understanding approaches to answering questions with data that are rooted in statistical thinking. Topics covered:
A) Data formatting: This session will get students comfortable with handling quantitative datasets, coding text into the appropriate format, understanding different types of variables (continuous, categorical, ordinal), understanding when transformations might be needed and how to execute them, and tidying data for analysis.
B) Intro to graphs: Students will learn to visualize individual variables, relationships between different variables, and the appropriate way to present them depending on the question. This will also cover some basics of aesthetics associated with formatting figures to aid in showing patterns in the data through graphs.
C) Intro to statistical thinking: Here students will learn the motivations for measuring central tendencies and spread of data, their relevance in hypothesis testing and data analysis, and how to interpret these summaries.
D) Experimental design: We will cover appropriate study design to test hypotheses, types of statistical tests and how to determine which to use. Students will learn how to report statistical analysis in a way that describes patterns in the data.
E) Storytelling with your data: Over the course of each of the previous 4 workshops, students will have time to come up with a question they would be interested in testing with a dataset of their choice (either gathered from existing databases, collected from their own projects, etc.) They will apply principles learned in the past 4 workshops to prepare, visualize, and analyze their own data and create a data analysis exercise for other students.
F) Bridge to R: We will discuss the limitations of graphical user interface tools such as DCU and other platforms and when coding based statistical tools may be needed. We will then see how data visualizations and statistical tests can be conducted in a language such as R using our Bridge to R interface. At each point we will highlight relevant modules that students could take in other data literacy courses in R/Python.