How can I master the python backend? What do I need to learn?
The foundation of a nine-tiered platform is built on a base. To become a high-end talent in the field of Python development, basic knowledge is important, and real-world experience is also important. Only by closely combining theoretical knowledge with real-world projects and integrating existing knowledge with trendy technologies can you stand at the top of the technology chain.
The following is the Python development learning route, which is divided into 10 major stages.
The first phase is Python Language Fundamentals, which focuses on learning the most basic knowledge of Python, such as Python 3, data types, strings, functions, classes, file manipulation, and so on. At the end of the phase of the course, students need to complete the Pygame real-world aircraft wars, 2048 and other projects;
The second phase of the Python language advanced, the main study of Python libraries, regular expressions, process threads, crawlers, traversal, and MySQL databases;
The third phase of the front-end of the full-stack engineers of Python, the main learning HTML, CSS, JavaScript, jQuery and other front-end knowledge, students need to complete the web interface design practice
The fourth to fifth stage for Python full-stack engineer back-end, mainly learning Django, Flask and Tornado, students need to complete the corresponding practical projects;
The sixth stage for the Linux basics, mainly learning Linux-related commands, such as file processing commands, compression and decompression commands, rights management, and Linux Shell development;
The seventh phase of the Linux operation and maintenance automation development, the main study of Python development of Linux operation and maintenance, Linux operation and maintenance alarm tool development, Linux operation and maintenance alarm security Audit development, Linux business quality reporting tool development, Kali security detection tool detection and Kali password cracking combat;
The eighth stage for Python data analysis, mainly learning numpy data processing, pandas data analysis, matplotlib data visualization, scipy data statistical analysis and python financial data analysis;
The ninth stage for Python big data, mainly learning Hadoop HDFS, python Hadoop MapReduce, python Spark core, python Spark SQL and python Spark MLlib;
The tenth stage for Python Machine Learning, which focuses on KNN algorithm, linear regression, logistic Steele regression algorithm, decision tree algorithm, plain Bayesian algorithm, support vector machine, and clustering k-means algorithm.