For someone seeking an interesting fresh profession that offers tremendous growth and plenty of new things to learn, the data science industry is an excellent choice. Nowadays, all companies, be it small businesses or large organizations, depend upon the information they get through the data they have. They monitor their data to gain insights to plan the future. They also use this data to make significant decisions for the organization.
In numerous situations, Python is the first choice as a programming language for the everyday jobs that data scientists have to do. It is one of the primary data science tools adopted across companies. Now, why is that? Why is Python an ideal programming language for data scientists? There are several data science python courses for learning this language using practical approaches.
Following are seven reasons why Python is one of the best programming languages for data scientists:
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Simple
Python is comparatively easy than other programming languages in the industry. It has an easy-to-learn syntax that is beginner-friendly. It is an interpreted, high-level programming language and has many programmers who work with Python. Ruby or Perl do the same work. However, Python has an easier approach.
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Scalability
Python is unbeatable when it comes to scalability. It is way better if compared to R. It is also faster than some other efficient languages like Matlab and Stata. It promotes scale because it provides data scientists flexibility and numerous techniques to tackle unusual obstacles. This is one of the many reasons why YouTube has shifted to using Python instead of other conventional languages it was using previously. Python is quite popular among several industries. These concepts can be learned better with the help of some handpicked data science python courses.
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Libraries and Frameworks
Due to its demand and an enormous community of programmers, Python has hundreds of different libraries and frameworks that are helpful in data processing. These libraries are quite time-saving entities and reduce work marginally.
As a Data Scientist, you will know that these libraries are directed towards data science and machine learning. This should be one of the most influential factors for taking up Python as their programming language. These libraries can be learned better with the help of some handpicked data science python courses.
Some of these libraries are given below:
- Pandas: It is excellent for data analysis and data handling. It also helps with data manipulation control.
- NumPy: It is a free library for numerical computing. It gives high-level math functions simultaneously with data manipulations.
- SciPy: It is related to scientific and technical computing. SciPy is also used for data optimization and modification, algebra, special functions, etc.
Enormous Community
One of the reasons that Python is so popular and is so on point is a primary result of its community. Python being an open-source tool, has attracted a considerably large number of people over the years. As the data science community proceeds to embrace it, more users are volunteering day by day and are creating new data science libraries.
The community is robust and helps to seek the optimal solution for any problem. All you need to connect with the community is, interest in Python for data science.
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Automation
Prebuilt Python automation frameworks like PYunit are a blessing due to the following reasons:
- There is no need for installing any additional modules. All needed entities come within the box
- Python background is not necessary if you’re working with Unittest. It is quite easy to work on and has features similar to that of xUnit
- Singular experiments can be run easily
- The test reports are generated unexpectedly fast, i.e. within the period of milliseconds
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Graphics and visualization
Python happens to have numerous visualization alternatives. Matplotlib renders the solid foundation throughout which other libraries like Seaborn, pandas plotting, and ggplot have been developed. The visualization packages assist in building a sense of data, create charts, graphical plots.
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Web Development
If you wish you go for the easiest alternative when it comes to development, choose Python. There are countless Django and Flask libraries and frameworks that help you finish up faster and removes redundancy.
If you analyze PHP and compare it to Python, you’ll get to know that work that takes hours in PHP can be completed within a few minutes using Python. There are several data science python courses available that will clear all of your doubts.
Conclusion
The way market and industries are competing nowadays; it is quite tough to keep up with the pace. If you’re looking for a stable job that provides stable growth, opt for the data science industry and learn Python. This language has a long way and is efficient and easy to understand.
