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Exploratory data analysis

Objectives

  • Get used to data analysis tools

  • Develop analytical thinking

Preliminary steps

First of all, select a dataset of your convenience from the ones available in Seaborn build-in datasets. You must not choose any datasets used as examples in class (i.e. Iris, titanic and tips). Suggested datasets are the following ones:

  • car_crashes.
  • diamonds.
  • exercise.
  • mpg.
  • planets.

You can load those datasets with the following code:

import seaborn as sns

df = sns.load_dataset('planets')

We recommend you to read a notebook with an example of data exploration to better understand the whole process. An interesting one is this one, which uses the famous iris dasaset.

Assignment goal

The goal of this assignment is to perform an exploratory data analysis of a simple dataset. To this end, create an notebook, load a dataset of your interest and apply techniques developed in along the lecture (summary statistics and/or graphical tools).

In order to properly frame the study, you should set a general objective. A brief discussion with your instructor would help to this end.

Remember that the goal of an exploratory analysis is not to provide a definitive answer to a question, but rather to get an insight to the data to better frame futher analysis. In other words, we want to describe our data instead of reaching solid conclusions.