It’s nearly impossible to remember all the libraries, it can be confusing to decide which one to use. There are several tools and libraries available in the market.

While trying to make accurate assumptions, we need the best tools to explore and visualize the data. There are some basic and widely used charts which we use or see in our day-to-day work – in data science and otherwise: Using a line chart instead of a scatter chart might not make sense. We also use charts and graphs to check the performance of models or while categorizing or clustering the data.Ĭhoosing a correct chart to communicate your findings about data is also important. Many visuals are used in the data exploration phase to find outliers, correlation between features, etc. We need visualization as a visual summary of the data, because it’s easier to understand for identifying relations and patterns. Data visualization can be done in two forms – tabular and graphical.

Data visualization is nothing but a mapping between tables or graphs and data (inputs or outputs). We can use visuals to represent the data at any point in our project. checking data distribution, finding correlation, finding outliers and missing values, etc.ĭata Visualizations aren’t part of any specific phase in a data analytics project.

It’s not a single step phase, but we get to determine a lot about our data during data exploration e.g. Whatever you decide to do in the later phases (creating/selecting a machine learning model or summarizing your findings), will depend on the assumptions you make in the exploration phase. When you are working on a data science project or trying to find data insights to strategize your plans, there are two key steps that can not be avoided – Data Exploration and Data Visualization.ĭata Exploration is an integral part of EDA ( Exploratory Data Analysis).

The best data visualization tools