**Univariate data analysis** is the simplest form of data analysis. As the name suggests, it deals with one variable. It doesn’t find cause and effect or relationship between variables. The purpose of univariate data analysis is to provide **summary statistics** on only one variable. If we don’t do enough of a univariate analysis it will result in using resources inefficiently because perhaps data is skewed, or has outliers, or has too many missing values, or has some values that are inconsistent. …

When we are writing algorithms to solve problems, it is possible to find more than one solution. Specially, in Machine Learning, we have different algorithms that can be used to solve the same ML problem. For example, for classification problems, we can use different algorithms like Logistic Regression, SVM, Decision Trees, etc. The time and splace complexity of algrithms can help to choose the proper algorithm for the specific problem. Each ML algorithm has its own time and space complexity. Before going to dive into ML, we should have introductory information about basics of the time and space complexity.

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