Why Python programming is essential for Data Analysis?

Answer 1

There are many popular programming languages used by data analysts such as Python, R, C++, etc. But Python holds a unique place among them. Python programming language is an OOP, open-source, adaptable, and easy to learn. It includes a rich group of libraries and tools that makes the tasks easier for Data scientists.

In addition to this, Python language has an enormous community base where engineers and data analysts can put in their queries and gets answer questions from others. Data science as services is using Python for quite some time and it will keep on being the top choice for many Data scientists and Developers.

Python programming has been around since the late 80s. Today, this outstanding programming language is useful for software development, mobile app development, web development, etc. Moreover, it is also useful in the examination and capturing of numeric and scientific data.

Anyone will be amazed to hear that major online platforms like Google, Dropbox, Instagram, and Spotify & YouTube— all had worked with this Programming language.

In the early days, this language was first used for automating repetitive tasks, prototyping apps, and the usage of those applications in multiple languages. It is relatively easier to learn and understand, on account of the spotless and straightforward syntax and extensive documentation.

Why learn Python for data analysis

Data analysis consulting companies are allowing their group of developers and data analysts to use Python as a programming language. Moreover, it has acquired well known and the most noteworthy prog language in an extremely small timeframe.

Data Scientists need to manage a huge amount of data such as big data. With simple usage and a huge organization of Python libraries; it becomes the most popular choice to deal with big data.

Easy to Use:

The framework is easy to use and includes a fast learning curve. New data analysts can easily learn this language with its simple to use syntax and better understandability. Moreover, it additionally provides a lot of data mining tools that help in better data handling. Ex;- Rapid Miner, Orange, etc.

This is noteworthy for data scientists since it has many useful and easy to use libraries. Such as; Pandas, Numpy, Tensorflow, and so on.

Python is Flexible:

It is very flexible as it not only lets you build software. But also allows you to deal with the analysis, numerical and logical data computing, and web development.

In addition to this, this language has become ever-present on the web, controlling different well-known websites using Web development frameworks. Such as TurboGears, Django, and Tornado.

Moreover, it is ideal for developers having the talent for web and app development

Best analytics platform

Data analytics is an important part of data science. Moreover, data analytics tools provide information about multiple frameworks important to assess the performance in any business. This programming language is the best choice for developing data analytics tools.

It can easily provide better knowledge and skills, get examples, and coordinate data from large datasets. In addition to this, the prog. Language is much noteworthy in self-service analytics.

Huge community base:

Python includes a large community base of engineers and data scientists The program language developers can transmit their problems and thoughts to the community. Here, the Python Package Index (PPI) is an exceptional place to explore the various skylines of this Prog Language. Besides, the Python developers are continually making improvements in the language that is helping it to emerge to be better over time.

Advantages of using Python for data analysis

This programming language definitely has a bright future in the area of data science, especially when used in coincidence with powerful tools like Jupyter Notebooks, etc. These have become much popular in the data analyst community. The value proposition of Notebooks is that they are very easy to build and perfect for running experiments faster. Learn More OnlineITGuru