Product Analyst's Opinion

[Mst.Rina1R]

I would recommend starting to learn Python without diving too deep into the basics of the language and syntax, but by solving simple practical problems. This will allow you to quickly get positive confirmation that you really need to use Python. This way, you will understand that you can make your life much easier. This will become a strong motivation to continue learning.

When talking about getting data, it is important to mention that the method of solving the problem depends on the source of the data. For example, if you need to get data from social networks, then first of all you should get acquainted with how to work with the API of this social network:

look for code examples in documentation or amateur articles;
run this code on your machine;
adapt it to your needs.
For some services, you can find ready-made libraries. In this case, you no longer need to try to use the API yourself, the library will do it for you. Libraries will help you if you are engaged in the analysis chile telegram of marketing data. For example, for Google Analytics there is an excellent library that allows you to extract site traffic statistics in a few lines of code. If you want to use data obtained from sites, then you need to do web parsing. There is an excellent tool for thisBeautifulSoup, which allows you to extract structured information from HTML pages.

In terms of data processing, everything is a little simpler. In the data analytics environment, the standard is the Pandas library. It includes a huge functionality for various data manipulations:

statistical processing,
cleaning,
transformation,
aggregation.
Pandas can be a worthy replacement for Excel if you do a lot of work with datasets.

Python has great capabilities for data visualization as well. You can start by mastering the libraryBokehorChartify.

Python has gained particular popularity in the field of machine learning. In this industry, a large number of libraries have been written in it. There is the easy-to-learn sklearn, and for professionals there is the high-performance TensorFlow.