exploratory data analysis (EDA) workflow

Exploration of data is the most tedious but also the most useful part of data science. If we don't have a good understanding of our data, we won't be able to apply a correct model, include sufficient predictor variables, or even exclude erroneous entries. There is a plethora of packages that can help us with exploratory data analysis (EDA). Some are specific for a given data type, while others are more general - and it can be difficult sometimes to know where to start!

In this talk, Priyanka Gagneja (https://github.com/priyankagagneja) will present her EDA workflow that includes a combination of R packages such as {dataExplorer}, {chronicle}, {esquisse}, amongst others.

About the speaker:
Priyanka is a data science practitioner, most recently working as a data science consultant with Onpoint Insights in US where her efforts focus on quality control and reporting for a biomedical devices company. Previously she used her analytics skills in industry serving clients in retail and financial services. She graduated from Boston College with a Masters in Applied Economics and holds an MBA and Bachelor's in Computer Science from India.