Exploratory Data Analysis in Python

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

4.88 (17 reviews)
Udemy
platform
English
language
Data Science
category
instructor
Exploratory Data Analysis in Python
2,689
students
2 hours
content
Oct 2021
last update
FREE
regular price

What you will 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

Description

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.

Content

Introduction

Introduction to the course
What is EDA?
The dataset
Required Python packages
Jupyter notebooks

Univariate analysis

A first sight to our dataset
Summarization
Histograms
Boxplots

Multivariate analysis

Pairplots
Correlation matrix and histograms
Stacked histograms

Some useful libraries

Sweetviz
Pandas profiling

General guidelines

Practical suggestions

Charts

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4354856
udemy ID
10/18/2021
course created date
10/21/2021
course indexed date
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