The last histogram you've built seems a bit biased, isn't it? Despite the majority of values located in the range close to 0, there were observations located close to -1400.

What is the most interesting part? This is the fact that one day contains 1440 minutes, which most likely means there are *date* discrepancies for the flights that fly at midnight. As we may recall, we simply concatenated the date we have and the time. But we had no information on what these dates are referred to.

In this dataset, you are going to recall all functions that were learned to consolidate knowledge. With the help of fascinating tasks, you will discover correlations between the condition of the used car and its price. We are going to provide you with brand new information too to make the process of analyzing data more fruitful.

This dataset is full of data about sales, but here you will work with the preprocessing it a lot. The gripping part of this section is determining the correlation between the weather and sales; finding out the relationship between holidays and sales. After this section, you are going to be ready to work with the real data.

In the third section, you will analyze a large dataset containing information about flights of American civil Aviation. There you will analyze the delays, is there a relation between flight distance and delay, which day is the most popular for flights, and so on.