Python is a powerful, flexible, open source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. Python is unique as it is both of capable general purpose programming as well as an analytical and quantitative programming.
Since its inception in 1991, programmers, who want to delve into data analysis or apply statistical techniques, have preferred using Python. Engineers prefer using Python as it is easily manageable and it focuses on simplicity. Had it been not for Python, big data analytics would have never found one of its favourite programming languages.
Python for data science is rapidly claiming a more dominant position. In a comparison between R and Python the latter one often gets the upper hand because it is a general purpose and integrates well within the whole ecosystem for end-to-end analytics implementation. The fact that Python is open source contributes a lot in its rising popularity. It is easier and cheaper to install and use Python and learning it also easy. It is a great language for companies which cannot afford to spend a fortune on SAS technologies.
Python is greatly used when data analysis tasks need to be integrated with web apps or if statistical code needs to be incorporated into a production database. It is a great tool to implement algorithms for production purposes.
Let us take a short look on the benefits of using Python –
- The Python Notebook makes it easier to work and can be easily shared with colleagues, without needing them to installing anything.
- It is a general purpose language with a relatively flat learning curve and it its built-in, low barrier-to-entry testing framework helps you make sure that your code is reasonable and dependable.
- Python features some decent visualization libraries, predictive modelling and machine learning.
While learning Python, you should keep in mind that it is not necessary to become proficient in building good software in Python but it is essential to be able to data analysis productively. It is of primary importance to learn the 5 Python libraries: Python Notebook, NumPy, Pandas, Matplotlib.
SO, after undergoing a beginners’ course on Python from any good online facility, you can learn coding and then start with the Python libraries. Following these easy steps will help you become proficient in Python big data analytics.
Analysts in increasing numbers are opting for Python because simple usage and the ease with which it can be integrated with web applications. It can provide support for big data processing and generating quick, valuable insights. In many Linux distributions, Python comes as a default installed package.
It is a growing trend and gradually encompassing large chunks of a wide range of industries. The applications of Python may be found in the internal infrastructure of Google and Youtube. Companies like Sony, Dreamworks, Disney, make substantial use of Python. It is one of the most successful languages for big data analytics applications. Along with its growing popularity ample job opportunities are being created in this discipline. Even if you are a regular user of SAS language or R for that matter it is highly suggestible that you consider adding Python to your repertoire; it can take an instrumental role in your progressive journey through the big data analytics industry.