Exploratory Data Analysis in Python

A course about how to approach a dataset for the first time

When we put our hands on a dataset for the first time, we can’t wait to test several models and algorithms. This is wrong because if we don’t know the information before feeding our model, the results will be unreliable and the model itself will surely fail. Moreover, if we don’t select the best features in advance, the training phase becomes slow and the model won’t learn anything useful.

What you’ll learn

  • Exploring a dataset for calculating overall statistics.
  • Visualize the correlations between the features.
  • Visualize the predictive power of the features.
  • Create useful insights from a dataset.

Course Content

  • Introduction –> 5 lectures • 16min.
  • Univariate analysis –> 4 lectures • 32min.
  • Multivariate analysis –> 3 lectures • 38min.
  • Some useful libraries –> 2 lectures • 23min.
  • General guidelines –> 1 lecture • 1min.

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Requirements

When we put our hands on a dataset for the first time, we can’t wait to test several models and algorithms. This is wrong because if we don’t know the information before feeding our model, the results will be unreliable and the model itself will surely fail. Moreover, if we don’t select the best features in advance, the training phase becomes slow and the model won’t learn anything useful.

So, the first approach we must have is to take a look at our dataset and visualize the information it contains. In other words, we have to explore it.

That’s the purpose of the Exploratory Data Analysis.

EDA is an important step of data science and machine learning. It helps us explore the information hidden inside a dataset before applying any model or algorithm. It makes heavy use of data visualization, it’s bias-free.

Moreover, it lets us figure out whether our features have predictive power or not, determining if the machine learning project we are working on has chances to be successful. Without EDA, we may give the wrong data to a model without reaching any success.

With this course, the student will learn:

  • How to visualize information that is hidden inside the dataset
  • How to visualize the correlation and the importance of the columns of a dataset
  • Some useful Python libraries

All the lessons are practical and made using Python programming language and Jupyter notebooks. All the notebooks are downloadable.

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