Summary
You just learned a lot regarding how to analyze a dataset. This a very critical step in any data science project. Getting a deep understanding of the dataset will help you to better assess the feasibility of achieving the requirements from the business.
Getting the right data in the right format at the right level of quality is key for getting good predictive performance for any machine learning algorithm. This is why it is so important to take the time analyzing the data before proceeding to the next stage. This task is referred to as the data understanding phase in the CRISP-DM methodology and can also be called Exploratory Data Analysis (EDA).
You learned how to use descriptive statistics to summarize key attributes of the dataset such as the average value of a numerical column, its spread with standard deviation or its range (minimum and maximum values), the unique values of a categorical variable, and its most frequent values. You also saw how to use data visualization...
