What is a Python full-stack engineer?
That is, from the implementation of the front-end page, to the writing of the back-end code, to the management of the database, one person can handle everything for a company website, truly realizing full-stack development.
Full-stack is just a concept, and it can be divided into many categories. A true full-stack engineer covers web development, DBA, crawler, testing, operation and maintenance. There is a huge amount of content to learn. What you need to learn in terms of web development: front-end knowledge includes HTML5 CSS3 JS Jquery Ajax. The back-end needs to be at least proficient in using Django and tornado. Of course, flask is better.
Python full-stack engineers are more popular A type of talent, the Python language itself has full-end development capabilities, so there are many full-stack engineers in Python.
For Python full-stack engineers, they need to master the following knowledge structure:
First, the basic syntax of the Python language. It is not difficult to master the basic syntax of the Python language. It can even be said to be very simple. Because Python is a scripting language, the syntax is relatively straightforward.
Second, Python is used for web development.
Python is one of the three traditional solutions for web development (also including Java and PHP). Web development is something that full-stack engineers must master. To do web development with Python, you need to learn the corresponding framework, such as Django.
Third, Python does data analysis. Data analysis in Python is a relatively common application scenario. To do data analysis in Python, you need to learn the corresponding algorithm and implementation process. Libraries that will be used include Numpy, matplotlib, etc.
Fourth, Python is used as a crawler. Python crawler application is a very common application, and there are a large number of cases for reference.
Fifth, Python is used for machine learning applications. Python is currently very commonly used in the field of machine learning (artificial intelligence related), so it is necessary for Python full-stack engineers to master machine learning-related content. Algorithms that need to be mastered include decision trees, naive Bayes, regression, kNN classification, etc. At the same time, basic knowledge of linear algebra and probability theory is required.
Of course Python also has its shortcomings, that is, it is slower, slower than C, C, and Java. This is a limitation because in many fields there are extreme requirements for the running speed of the language. For example, if you take the missile interception program, every second counts. Obviously python will be passed in the first step. But for other areas, such as the calculation of bridge parameters, I believe there is not much difference between 20 minutes and 18 minutes. The second point is from a utilitarian point of view, that is, there is no Python option in the current computer grade examination. Of course, I believe that in 10 years, maybe 5 years, we will be able to get the Python computer grade certificate.
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By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

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Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

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