Exploratory Data Analysis in Python

About This Course

This course offers a thorough introduction to Exploratory Data Analysis (EDA) using Python. You will start your analysis by comprehensively understanding the contents of a dataset and learning how to incorporate your exploratory findings into a data science workflow. By utilizing datasets pertaining to unemployment figures and plane ticket prices, you will explore various Python tools to summarize and validate data, manage missing values, and clean both numerical and categorical data. Additionally, you will create impressive visualizations using Seaborn to reveal relationships between variables, such as the correlation between alcohol consumption and student performance. The course will also illustrate how to integrate your exploratory insights into the data science process, covering aspects such as feature engineering, balancing categorical features, and generating hypotheses. By the conclusion of this course, you will be equipped to confidently conduct your own EDA in Python, effectively present your findings visually, and suggest actionable next steps based on your data insights.