Python Data Visualization using Seaborn - Beginners

What you'll learn

One will learn about introduction to seaborn, review of the training, different types of Descriptions, distribution Description, scatterDescription and heat map, case studies of scatter Description, boxDescription, bank problem, case study on swarm Description, etc.
Other skills that are going to be covered under this training program is visualizing statistical relationships which include scatterDescriptions, line Descriptions, Descriptionting with categorical data, showing multiple relationships with facets, categorical scatter Descriptions, distribution of observations with categories, statistical estimation with categories, count Description, point Description, boxDescription, bar Description, use of reference files, etc.

Requirements

The user should also have a mathematical background as most of the algorithms being used and the concepts which are discussed are mathematics-based.
The basic prerequisite for this course is that the student or the professional should have a basic knowledge and understanding of the machine learning tools and techniques and also should have a basic knowledge and overview of the data science techniques. Apart from this, he should also be aware of the basic analytical concepts which are a must while opting for this course.

Description

As training goes ahead, individuals will start realizing the importance and value of seaborn training with diverse skills and concepts that are going to be taught under this training program. The curriculum of the training program is developed in such a way that it helps in getting all the industry requirements and also takes squares of individuals' requirements who are investing their time and efforts in learning something new and interesting. The core skills that are going to be covered under this training program are as follows:

Introduction of Seaborn

Visualizing Statistical Relationships

Scatter Description

Line Descriptions

Descriptionting with Categorical Data

Showing Multiple Relationships with Facets

Categorical ScatterDescriptions

Distributions of Observations within Categories

Statistical Estimation within Categories

CountDescription

PointDescription

BoxenDescription

ViolenDescription

BarDescription

SwarmDescription

StripDescription

CatDescription

One will learn about introduction to seaborn, o review of the training, different types of Descriptions, distribution Description, scatterDescription and heat map, case studies of scatter Description, boxDescription, bank problem, case study on swarm Description, etc.

Other skills that are going to be covered under this training program is visualizing statistical relationships which include scatterDescriptions, line Descriptions, Descriptionting with categorical data, showing multiple relationships with facets, categorical scatter Descriptions, distribution of observations with categories, statistical estimation with categories, count Description, point Description, boxDescription, bar Description, use of reference files, etc.
Who this course is for:

Data scientists, data engineers, analysts, consultants, software developers, software engineers, testers.
The target audience becomes anybody who is interested in learning this Python Seaborn Tutorial and follows the above-mentioned pre-requisites
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