The greatest number of mistakes and failures in data analysis comes from not performing adequate Exploratory Data Analysis (EDA). Lack of EDA knowledge can expose you to the great risk of drawing incorrect, and potentially harmful, conclusions from your data analysis.

In this course, you will learn how EDA helps you draw conclusions to make better sense of your data and implement correct techniques. We'll begin with a brief introduction to EDA, its importance, and advantages over BI tools. Using R libraries like dplyr and ggplot2, we will generate insights and formulate relevant questions for investigation and communicate the results effectively using visualizations. You will learn how to spot missing data and errors, validate assumptions, and identify the patterns for understanding the problem. Based on this, you’ll be able to select a correct ML model to use for your data.

By the end of the course, you will be able to quickly get know and interpret various kinds of data sets you will be presented with, and easily understand how to handle and work with them in order to make them ready for further modeling activities.

Please note that basic knowledge of R and R Studio, together with some knowledge of descriptive statistics, are key to getting the best out of this course.

About the Author

Andrea Cirillo is a Senior Audit Quantitative Analyst at Intesa Sanpaolo Banking Group. He works daily with copious volumes of "messy" data for the purpose of auditing credit risk models. This has prompted him to develop the key skills needed to succeed in Exploratory Data Analysis (EDA). Andrea is also an active contributor to the R community with well-received packages like updateR and paletteR. He recently focused resolving some of his R-related pain-points by helping R users draw the most out of their data through effective data visualization tools like the dataviz bot Vizscorer.