Join our Social media channels to get the latest discounts
Learn attractive and informative statistical graphics and data visualization in Python using Seaborn
There are a variety of skills that participants will learn through this training program like visualizing statistical relationships, different types of visualizations, scatter plots, plotting with categorical data, bar plots, color palettes, visualizing linear relationships, bivariate distributions, univariate distributions, visualizing the distribution of a data set, cat plot, strike plot, etc.
What’s the training gets completed the learners can easily enter the industry as a data analyst, business analyst, statistician, quality engineer, quality manager, consultant, team leader, statistical analyst, software test engineer, QA engineer, consultant, analytics manager, and many other diverse fields are open for individuals to show their potential and skills in the world.
Under this part of the training program, advanced concepts of seaborn tool that are an introduction to seaborn advance, building structure multiplot grids, conditional small multiplies, use of custom functions, plotting pairwise data relationships, choosing color palettes, use of different seaborn figure styles, setting different color palettes, use of reference files in advance, etc.
In the domain of analytics and data science, there are a huge number of libraries that are gaining huge popularity with every passing day due to their wide array of benefits and the features that they have to offer. One such important library is matplotlib of which seaborn is made up. These libraries together are used for visualizations and making a good level of predictions which is specifically based on the outcome of data. These plots help analyze and determine the nature of the data and how it can be used for better use. Seaborn is Python-based which is built on top of Matplotlib, therefore, it consists of all the features which are present with the main libraries. Seaborn libraries help in resolving two fundamental issues that are related to default matplotlib parameters and the ones related to working with data frames and therefore hold a higher value than the parent library.