Exploratory Data Analysis in Python

Exploratory Data Analysis in Python, available at $59.99, has an average rating of 4.58, with 15 lectures, based on 478 reviews, and has 6941 subscribers.

You will learn about 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 This course is ideal for individuals who are Python developers or Data scientists It is particularly useful for Python developers or Data scientists.

Enroll now: Exploratory Data Analysis in Python

Summary

Title: Exploratory Data Analysis in Python

Price: $59.99

Average Rating: 4.58

Number of Lectures: 15

Number of Published Lectures: 15

Number of Curriculum Items: 15

Number of Published Curriculum Objects: 15

Original Price: $29.99

Quality Status: approved

Status: Live

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

Who Should Attend

Python developers
Data scientists

Target Audiences

Python developers
Data scientists

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to the course

Lecture 2: What is EDA?

Lecture 3: The dataset

Lecture 4: Required Python packages

Lecture 5: Jupyter notebooks

Chapter 2: Univariate analysis

Lecture 1: A first sight to our dataset

Lecture 2: Summarization

Lecture 3: Histograms

Lecture 4: Boxplots

Chapter 3: Multivariate analysis

Lecture 1: Pairplots

Lecture 2: Correlation matrix and histograms

Lecture 3: Stacked histograms

Chapter 4: Some useful libraries

Lecture 1: Sweetviz

Lecture 2: Pandas profiling

Chapter 5: General guidelines

Lecture 1: Practical suggestions

Instructors

Gianluca Malato
Your Data Teacher

Rating Distribution

1 stars: 1 votes
2 stars: 12 votes
3 stars: 48 votes
4 stars: 189 votes
5 stars: 228 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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