Python Data Visualization using Seaborn – Beginners

What you’ll learn

• One will learn about introduction to seaborn, review of the training, different types of plots, distribution plot, scatterplot and heat map, case studies of scatter plot, boxplot, bank problem, case study on swarm plot, etc.

• Other skills that are going to be covered under this training program is visualizing statistical relationships which include scatterplots, line plots, plotting with categorical data, showing multiple relationships with facets, categorical scatter plots, distribution of observations with categories, statistical estimation with categories, count plot, point plot, boxplot, bar plot, 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.

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 Plot
• Line Plots
• Plotting with Categorical Data
• Showing Multiple Relationships with Facets
• Categorical Scatterplots
• Distributions of Observations within Categories
• Statistical Estimation within Categories
• Countplot
• Pointplot
• Boxenplot
• Violenplot
• Barplot
• Swarmplot
• Stripplot
• Catplot
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One will learn about introduction to seaborn, o review of the training, different types of plots, distribution plot, scatterplot and heat map, case studies of scatter plot, boxplot, bank problem, case study on swarm plot, etc.

Other skills that are going to be covered under this training program is visualizing statistical relationships which include scatterplots, line plots, plotting with categorical data, showing multiple relationships with facets, categorical scatter plots, distribution of observations with categories, statistical estimation with categories, count plot, point plot, boxplot, bar plot, 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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