What to do after learning python?
Python is one of the most powerful languages and it is used in many areas from web development, building bots, scrapping tools, machine learning, etc.
After knowing the fundamentals and working knowledge one can try to explore the things python has to offer. It will help to visualize how things are created in real-life.
Web Development:
Django and Flask are one of the most powerful tools and backend frameworks that support rapid and scalable development. It is very easy to get hands-on with both of these frameworks since most of the configurations are pre-built.
For building a full-scale application you will also require to learn HTML, CSS, and javascript but you can use a template/bootstrap (these are pre-built frontend resources) and focus on backend development with Flask or Django.
Instagram is one of the popular applications that use Django for developing the backend.
List of important resources:
Books you can follow:
- Django 3 By Example: Link
Automation Tools & Web Scraping:
Python has the capability to automate tasks that are boring and cumbersome to do manually. One can make a simple converted for their files from one format to other.
The program can also be converted into a bot (discord/telegram bot) for easy access. (One popular library is rmtbot).
Web Scraping:
Python can also help in Web scraping i.e. collecting large information from websites. It can have several applications such as comparing prices of items on different websites, getting the public email addresses of someone, getting summaries on a topic etc.
Good resources:
Desktop application:
Python can make a full-fledged desktop application with the help of the PyQt library. PyQt is one of the popular choices but there are also other libraries that can help you build desktop applications.
Good Resources:
Machine Learning:
Python is a very popular choice in the field of machine learning mainly because it is easy to understand and learn and it has wide support libraries and community support.
If you are someone who loves to deal with numerical data, categorical data, time-series data, and text you can explore this field. It has a higher learning curve, but it is a great choice for your career.
Resources for beginners:
