There are many Python skills one needs to learn to become a good developer, here are 10 of them
As a programmer in the modern-day tech industry, you have to learn programming languages that have excellent in-built features and can help develop your applications quickly. Not only that, the language has to be easy to learn and should also support the developer community. So, owing to these facilities most programmers, coders, and developers have started using the Python programming language. Python is one of the most widely used and sought-after programming languages in the industry. There are many Python skills one needs to learn to become a good developer. Here are the top 10 python skills to help land a high-paying job.
Knowing Python frameworks is a must, however, it doesn’t mean that a Python developer has to know them all. Depending on the project you may be asked to know one or another, but mostly used are Django, Flask, and CherryPy. Undoubtedly, if you already know Python, you have a chance to work with at least one of the most popular frameworks. This is one of the top Python skills that you must know about being a developer.
Object Relational Mappers
ORM is a programming technique in computer science that comes in handy when we convert data between two incompatible type systems using Object Oriented programming languages. It creates a “virtual object database” that can be used from within any programming language. There are customized ORM tools used by programmers.
Artificial Intelligence and Machine Learning Skill
AI and ML skills are the most important Python skills. A developer in Data Science should have good knowledge about Artificial Intelligence and Machine Learning as it comes under Data Science. One should have good proficiency in Machine Learning algorithms. One should have a good understanding of Neural Networks, the ability to produce insights from data, data visualisation, data analysis, and data collection skills.
Python has several exceptional benefits, and one of them is its extensive collection of libraries. As per the Python Package Index, Python has more than 267,000 projects. Well, this means there is a solid chance that whatever you’re trying to create, a package already exists that can make the development easier for you.
Once you have covered the fundamentals of Artificial Intelligence and Machine Learning, the next step in your journey should be Deep Learning. Deep Learning is a part of Machine Learning, and the learning processes and techniques are much similar to those of our human brains. Once you figure out what Deep Learning is, you should be able to use your newfound skills to develop Deep Learning-powered systems.
Keeping track of every change made to the file to later on source the code is a must-know for each developer. For most of the job openings, you can see this as a requirement. Thankfully it is not difficult to get familiar with and if you have been coding for a while, have properly set your GitHub and terms like “push, fork, pull, commit” are not random words for you.
Understand Multi-Process Architecture
Your team may consist of a design engineer, but you should also know how the code works in deployment and release. As a Python-Dev you should know about the MVC(Model View Controller) and MVT(Model View Template) Architecture. Once you understand the multi-Process Architecture you can solve issues related to the core framework etc.
One must have excellent Python skills in the field of Data Science to become a good python developer. The required analytical skills may need a good understanding of building useful websites for web development, visualizing datasets for Data Science in a better way, optimization of algorithms while coding, writing clean code which is not redundant, etc.
Many times, a Python developer has to agree with the frontend team to match the server-side with the client-side. Hence, you must know how the frontend works, what is plausible and what is not, how the app is going to look.
The Ability of Integration
A developer should have the Python skills of integrating multiple databases and data sources into a single system. This integration will help in the smooth running of the system and have fewer discrepancies.
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