Exploratory Data Analysis for Data Scientists
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Join this introductory workshop and learn about missing values from an exploratory data analysis (EDA) point of view. There are four foundational aspects to EDA, that make up its core, and they all should be thought about while or even before completing any analysis.
This workshop will include an introduction to EDA and one of the core aspects to completing it correctly, missing values.
- This class will cover topics including:
- An overview of EDA
- The 4 foundational aspects of EDA
- What role missing values play in EDA
- What can be done about missing values
- Some code in Python and R that may be useful in exploring it
This class is geared for those without strong exploratory analysis background, those who want to complete data science and data analytics projects correctly, those who want a better statistical understanding of data science, and anyone interesting in getting started in data science. This workshop is primarily targeted for beginner to intermediate data scientists who are interested to learn more about the fundamentals EDA and how to leverage it in each project.
This session will be taught by Alejandro Daviano, an 11-year Data Science professional. For more details, check out his tutor listing here.
Event link sent upon registration.