How to analyze data with python?
Different data analysis methods can usually be used in different scenarios. For example, for most professionals, Excel can meet most data analysis scenarios. When the amount of data is relatively large, Excel can be used to analyze data. Learn database knowledge to complete data analysis tasks. For more complex data analysis scenarios, you can use BI tools to complete data analysis. Data analysis through tools is more convenient on the one hand, and easier to master on the other.
But for more open data analysis scenarios, data analysis needs to be done through programming, such as data analysis through machine learning, and the Python language is widely used in the field of machine learning. . Data analysis using machine learning requires five steps, namely data preparation, algorithm design, algorithm training, algorithm verification and algorithm application.
When using machine learning for data analysis, you must first understand common algorithms, such as knn, decision tree, support vector machine, naive Bayes, etc. These algorithms are very common algorithms in the field of machine learning. , also has a relatively wide range of application scenarios. Of course, learning these algorithms also requires a certain foundation in linear algebra and probability theory. It is best to learn different algorithms and analyze them in conjunction with the corresponding application scenarios. Some scenarios also need to be analyzed in conjunction with multiple algorithms. In addition, the use of scene-based learning algorithms will establish a visual sense as quickly as possible.
Using Python for data analysis also requires mastering the use of a series of libraries, including Numpy (matrix operation library), Scipy (statistical operation library), Matplotlib (drawing library), pandas (data set operation), Sympy ( Numerical operation library) and other libraries, these libraries are widely used in Python for data analysis.
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