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Python is not just for programming. According to StackOverflow, the fastest-growing applications of this language are currently Data Science, machine learning, and academic research. There are many reasons why Python is chosen for working with data.

Increased data availability, stronger calculations and focus on analytical decisions in business are now major parts of the work (also in academic and scientific research) for a growing number of people. Many people use programs such as Microsoft Excel or Google Sheets to work with huge amounts of data. These are powerful tools but have serious limitations, such as problems with analyzing data sets above a certain size. These limitations, however, are not a problem for Python - one of the most popular and fastest-growing major programming languages in the world.




Ease of use and versatility

Python is a simple language to learn from scratch and it is developing rapidly. Writing a program in Python will also require less time from you than other languages such as C++ or Java. This will allow you to spend time on real research, instead of having to constantly review the documentation before you reach your real destination. In short, you will focus on your main research goal, not the tool you are using.


One of the main factors that make Python so popular is its versatility. You can use it not only for research but also for creating websites, text processing, artificial intelligence, machine learning, and more.


Extremely stable libraries with excellent support

There are over 125,000 external Python libraries that make Python more useful for specific purposes, including research.

Libraries such as NumPy, SciPy, Pandas, and matplotlib have been around for a long time, and are extremely well maintained, optimized, ready for production and well documented. Need more arguments? The Python developer community is one of the best in the world; it is large and very active. If you have any questions or problems, there are many people who can help.


Python is a dynamic language

Python is an interpreted, object-oriented high-level programming language with dynamic semantics. It has built-in high-level data structures, combined with dynamic typing and dynamic binding. Many programmers fall in love with Python because it helps increase productivity. The compilation is not necessary when using Python. This means the ability to be immediately productive, which helps with initial exploratory data analysis. As a result, Python's approach to software development is more iterative.


Python is easy to use, flexible, versatile, and has many useful, stable, and well-maintained libraries with excellent communication. If you work on large data sets and want to increase your productivity and automate data analysis, you should give Python a try.




Ease of use for beginners

First of all, Python is one of the easiest programming languages to learn. You do not need any programming experience to start analyzing data in Python. Unlike R and MATLAB, two other popular languages in science and engineering, Python has a very simple syntax and coding rules, making it an ideal language for beginners. It is also very easy to set up and implement.


Fast application development time

Fintech and the traditional areas of finance prefer Python to other languages because of the fast application development time. Due to the countless number of open-source data analysis libraries, creating fintech applications in Python does not take as much time as data analysis tools such as Microsoft Excel and R, because you do not have to waste time writing code from scratch.


Extensive data visualization support

Python will provide you with great tools for data visualization. With data analysis kits such as plotters, gplots, and panda, you can create professional charts and other forms of data imaging.


Open-source libraries

Python has many open source libraries that extend the functionality of the basic language, and their installation is so trivially simple. There are Python libraries for almost everything you can come up with - from simple GUI application development to machine learning, networking, and efficient data analysis.


Some of the best libraries for learning data in Python include:

NumPy: a full-fledged scientific computer library for Python's linear algebra and high-level math, allowing you to work with matrices and other data structures.

Matplotlib: Need to create histograms, pie charts, line charts, or other professional data visualizations for your work? No problem. There really is no limit to what you can do with matplotlib. You can also export all graphics to popular formats for publication.

Pandas: is an excellent open-source library for data manipulation and analysis.


Python is not just for free programming - it is used today by the world's leading companies, including Google, Facebook, Instagram, and Spotify.


Many companies require candidates for the position of a data scientist to master Python and take training in this language. This is why at Coders Lab we decided to offer the new Python: Data Analysis course.

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