![]() ![]() ![]() And which technique to use when is actually dependent on the type of data you are dealing with. There are various ways of treating your missing values in the data set. ![]() We can see that we have various missing values in the respective columns. – Bivariate Analysis # Importing Libraries – Encoding Categorical variables( Dummy Variables) – Normalizing and Scaling( Numerical Variables) The major topics to be covered are below: You can refer to our python course online to get on board with python. ![]() We will explore a Data set and perform the exploratory data analysis in python. It helps in drilling down the information, to transform metrics, facts, and figures into initiatives for improvement. It is used to show historical data by using some analytics tools. At an advanced level, EDA involves looking at and describing the data set from different angles and then summarizing it.ĭata Analysis: Data Analysis is the statistics and probability to figure out trends in the data set. There is not a very big difference between the two, but both have different purposes.Įxploratory Data Analysis(EDA): Exploratory data analysis is a complement to inferential statistics, which tends to be fairly rigid with rules and formulas. While starting a career in Data Science, people generally don’t know the difference between Data analysis and exploratory data analysis. Exploratory data analysis is one of the best practices used in data science today. ![]()
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