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Learn attractive and informative statistical graphics and data visualization in Python
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.
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
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.
Overview
