Data analysis involves statistics, linear algebra, drawing graphical analysis and data mining knowledge, recommended systematic study of electronic data "for data analysis using the Python 2nd Edition," "Python data analysis and mining combat", "start from scratch to learn Python data analysis and mining "e-book and code testing.
"Data analysis was performed using the Python 2nd Edition" e-book code, a progressive relationship between each chapter, the entry for Python "Python programming from entry to practice" after reading e-books in this field specifically for data analysis. I read it carefully, knocked code again, at the beginning and do not know, slow progress, then gradually clearer.
To learn programming more practice, it is difficult for people without foundation, because no start. My experience is that when your foundation is not strong, you see more, more in mind, summary, build their own framework of ideas.
"Python data analysis and mining real" electronic data, starting from the application of data mining, manufacturing and electricity and other public services real industry case the main line, introduced in layman's language Python data mining modeling process, highly practical.
"Python Data Analysis and Mining combat" a total of 15 chapters, divided into two parts: Basics, actual articles. Basics introduces the basic principles of data mining, actual articles describes one real case, in simple terms by case analysis, data mining projects to obtain experience through practice cases unknowingly, while quickly comprehend the seemingly incomprehensible data mining theory. In reading the labeling process, we should make full use of the book with the supporting case modeling data, with relevant data mining modeling tool, by experiment on the computer to quickly understand the relevant knowledge and theories.
"From scratch learning Python Data Analysis and Mining" electronic data contains 16 chapters, covering basic knowledge of Python entry, data analysis case (based on numpy module, pandas modules explain various data preprocessing; matplotlib based module, seaborn module to explain the drawing process commonly used statistical graphics), data mining case (based on sklearn module, transfer theory to explain the ten kinds of data mining models and practical applications, such as regression model, decision tree model, integrated model tree, knn model, Bayesian model Wait).
, Ideal for entry of data mining code for e-books, and learning is recommended.
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学习Python编程，数据分析，数据挖掘，笔记代码训练，每天都在进步！