Python is a high level, interpreted, and general-purpose programming language. It was created by Guido van Rossum and was released in 1991.
The design philosophy of Python emphasizes code readability with its great use of significant whitespace.
The approach of Python-like language construct and object-oriented aims at helping the professionals and programmers in writing clear and logical code for large as well as small scale projects.
ForecastWatch used Python to write a parser for collecting forecasts from various websites, in the website code to present the results in an integrated engine to collect the data.
Earlier PHP was used to develop the websites until it was realized that a single language is easier to work with. As per the article in Fast company Magazine, Facebook selected Python for the analysis of data in the year 2014 as the language was hiking globally.
Further, Data science has also emerged as a new requirement in the Python development Firms as well as in the python industry over the last decade and has completely revamped our thinking patterns.
The reason behind the widespread use of data science is that data analytics is not limited to one or two IT or software industries but can find use in all the industries. For example health care, security, intelligence, government, business, energy, and whatnot?
Why should you learn data science with Python?
Now, we are going to focus on why learning data science with Python is important for your career? Following are the pointers:
- There is a shortage of data scientists;
- Companies look forward to hiring more candidates for the post of data scientists but are unable to find right and qualified candidate;
- It can provide you with lucrative career choices;
- Python has many notable features that attract the data science community;
- Python is a data science tool that helps in exploring the concepts of machine learning in the best way;
- Python makes it easy to do with probability, stats, and mathematical optimization in machine learning.
Benefits of learning data science with Python
Following are the benefits of learning data science with Python:
User Friendly and Easy to learn language
Python is user friendly and easy to learn language. It is ahead of SAS and SQL and comes next to the language R with 35 % of data analysts using it. Python promotes an easy to understand syntax as compared to other data science languages.
One of the challenging things people face when they are about to choose data science as their career options are the lack of coding skills and the difficulty in learning the same.
However, you would not face this problem with Python. Even if someone doesn’t have any background in coding, it is still easy to learn Python for him or her.
Highly flexible and scalable
Python has emerged out as a highly flexible and scalable language in comparison with the language R. Further, it is faster to use than Matlab and Stata.
This language is used by skilled data scientists in various departments for developing different types of applications and software successfully. Even the social media giant, YouTube has shifted to Python for its extreme scalability.
Quick processing and development
Processing time or speed limits can be very irritating if it is not as fast as the user expects it to be. Speed is the key while dealing with a huge amount of data and information. Things can slow down if a language is slow in processing.
However, Python is an easy to handle and a clear language that requires only a few lines to code. Python cuts down the time required in coding. Earlier Python was also held back due to its slow execution. But even this complaint has been dealt with after the introduction of the Anaconda platform.
Powerful packages and Data science Libraries
The reason for the hike in the growth of Python is the availability of data science libraries for aspiring individuals.
Also, such libraries are continuously updated. The crisis faced by the developers a year ago is treated in a better way by Python today.
Besides other languages such as SQL and C that support the integration of Python, it comes with a huge range of packages such as NumPy, PyBrain, SciPy, Pandas, etc.
These packages make it simple to code complex data analytics problems by helping Python to make it more powerful. Python eventually offers various graphics options.
Following are some imperative packages to perform data analysis:
- NumPy: It is an important library for performing scientific computing with Python. Further, it includes a variety of high-level mathematical functions to operate on multidimensional matrices and arrays.
- Pandas: It delivers data structures and operations to change time series as well as numerical tables.
- SciPy: It works in collaboration with NumPy arrays. Further, it offers effective routines for up-gradation and numerical integration.
- Matplotlib: This one is a 2-D plotting library. It offers data visualizations in the form of power spectra, histograms, scatterplots as well as bar charts with minimal coding lines. It is the base for developing libraries like Pandas, ggplot, plotting, and Seaborn, etc.
Python Community support
Python has a strong community that makes it easy to understand and then learn. One of the reasons for the growth of Python is its ecosystem.
This community of Python is available 24*7 to help. Are you facing any problem? Contact the community! Code mentors as well as stack flow are available to get the authentic answers to your queries.
Further, there are many professionals in the Python community who are always busy developing in new libraries and packages for different users.
It helps in developing an advanced range of processes and tools in Python. And as such many of these packages are developed for the use of data scientists with the increase in popularity of Enhanced data visualization and Graphics.
With the help of data visualization packages and libraries, the professionals and developers can well understand the data; they can further develop charts, graphical plots, and develop web-ready plots.
Visualization can be called as a key for data scientists because it helps in understanding the data in a better way.
Again, Python can help in creating awesome visualizations by using libraries like NetworkX, ggplot, and Matplotlib and APIs like Plotly. Further, other big data visualization tools can also be integrated into Python adding to its worth for a data scientist.
Compatibility with Hadoop
Hadoop is one of the most popular and open sources of big data platforms. It makes great compatibility with Python. PyDoop, the Python package allows you to access the API for Hadoop.
Further, it allows you to write Hadoop programs with the help of Python. Also, it allows you to write codes for critical and complex problems and that too with no or very little effort.
Conclusion
So these are some of the benefits of learning data science with Python.
The basis lies here that understanding both these tools (data science as well as Python) and utilizing these according to their respective capabilities will refine you or anyone else aspiring to become a great data scientist.
Because it is imperative for a data scientist to be versatile in his field and to stay updated in his or her field.