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In the era of big data, we first need to manage the data, which requires us to find missing data or predict the trend, so we need operations including interpolation and data fitting. Interpolation is a process to discover deducing new data points in a range through known and discrete data points. When solving scientific and engineering problems, data points are usually obtained by sampling, experiments, and other methods. These data may represent a finite number of numerical functions in which the values of independent variables. According to these data, we often want to get a continuous function, i.e., curve; or more dense discrete equations are consistent with known data, while this process is called fitting. This article describes why the main idea come out logically and how to apply various method since the definitions are already written in the textbooks. At the same time, we give examples to help introduce the definitions or show the applications. We divide interpolation into several parts by their methods or functions for the structure. What comes first is the polynomial interpolation, which contains Lagrange interpolation and Newton interpolation, which are essential but critical. Then we introduce a typical stepwise linear interpolation - Neville's algorithm. If we are concerned about the derivative, it comes to Hermite interpolation; if we focus on smoothness, it comes to cubic splines and Chebyshev nodes. Finally, in the Data fitting part, we introduce the most typical one: the Linear squares method, which needs to be completed by normal equations.