The data science as a field is emerging fast as the future of AI and modern technology. As they say, it all lies in the data and great discoveries can be made by scouring through hundreds of thousands of lines of the saved history of virtually anything. If information is power then the ability to accurately understand and re-evaluate the past is the key to the future.
The many programming tools used for Data analysis are mainly Python, R programming, SAS, Excel, Apache Spark, KNIME, QlikView and RapidMiner. But R programming remains the most commonly used tool in data science.
What is Python?
Many developers know Python by heart and are expected to work in data science as it requires extensive coding skills. As this unique situation is growing in frequency, there is a question worth asking; is Python a good coding program for Data sciences. Python has fast become the language to conduct Data Science operations and is now outpacing R programming as well in this regard.
Python is considered one solution to all of the programming needs that a person may have. It can work on structured programming as well as object-oriented-programming and is also used in the functional programming processes.
StackOverflow survey was conducted in 2018 that showed that Python is right now, the most used programming language globally and due to its flexibility and universal inclusion, it is ideal for developing data science tools and applications.
There are many ways to use Python focused data science techniques for mining of useful information.
The modern banking system uses Python for data management, visualization, and data processing so that a greater pattern in consumer behavior can be driven for further fiscal planning.
This is another significant process that occurs every day. Weather forecasting in the modern world is heavily dependent on data science and uses the previous data to make incredibly precise weather predictions.
Now comes the million-dollar question, why is Python preferred over other programs used for coding when it comes to data sciences. Here are a few reasons why it is the preferred form of programming.
Python has a width that covers the entire programming sphere. It can be used to make one simple blog post and it can also be used to make a complete game. This width makes it an ideal candidate for data science mining because you may have a small and simple assignment one day and need to scour through hundreds of data to find the original conclusion.
Python is comprehensive and you do not need to go to another software program to take care of any portion of the process. That is why there is no debugging required in these kinds of data science processes.
Less Time Consuming:
It is less time consuming than the other programs because Python basically works with a massive number of prepared building blocks that can be stacked any way the programmer likes. As these are already prepared building blocks, it is easier and quicker to get this process done.
Python has an endless list of libraries that can be used for effective artificial intelligence and machine learning such as TensorFlow, Pytorch, Seaborn, Scikit Learn, Matplotlib among many others.
Python is a preferred choice for many novice data scientists because only basics have to be learned beforehand and the rest of complex processes can be easily learned online through freely available course modules.
Data visualization is a significant part of data sciences and that part is not well achieved in the other programming option because they are not focused on visualization. But Python has the exclusive or near-exclusive benefit that it is highly easy to use for visualization.
The program can be used for extensive visualization to develop connecting and high-quality graphics that will elaborate the findings to non-technical professionals in a highly engaging and interactive manner. This is a major benefit that many individuals find highly compelling and is the main reason for using Python.
In conclusion, there is a great benefit to using Python as the language to understand and evaluate the data that is available to a professional data scientist. This is a programming language that is extensive, comprehensive and exceptionally agile. The language is certainly worth learning for data science mining.
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