Python for Data Science
In this article, you will learn about Python for Data Science. While there are so many languages, Python is a must-learn programming language for professionals in the Data Science domain. There is an increased demand for skilled Data Scientists in the IT industry, and Python has evolved into the most preferred programming language. Now, let’s look at the basic features of Python and its domain scenarios.
Why Python for Data Science
As you know, many programming languages provide the much-needed options to execute Data Science jobs. It has become challenging to handpick a specific language. But it is data that provides a peep into these languages that are making their way into the world of Data Science, i.e., nothing can be as compelling as the data unveiling the results of the comparison between different Data Science tools.
Python as a ‘Leader’
It is one of the fastest-growing programming languages and is relatively easy to learn. Python has widely used in software development, mobile app development, web development, and scientific and numeric data analysis and computing. It can run on any platform from Linux to Windows to Macintosh etc.
Why Is Python Preferred over Others
Codes in Python are written in a very ‘natural’ style; that’s why they are easy to read and understand. Its various features make it a popular language in Data Science, and some of its applications are:
Easy to Learn
Python is for anyone aspiring to learn because of its ease of learning and understanding. Python is a popular data science tool, which is ahead of SQL and SAS and comes next to R, with 35 percent of data analysts using it.
Python is a highly scalable language compared to other languages like R. It is much faster to use than Stata or MATLAB. Its scalable nature lies in its flexibility during problem-solving situations; even YouTube has migrated to Python. Python has become suitable for different usages in industries as many of our Data Scientists use this language to develop various applications successfully.
Availability of Data Science Libraries
Why Python for data science, is the availability of various Data Analytics /Data Science libraries like NumPy, SciPy, and Scikit-Learn, Pandas, and statsmodels, some of the well-known libraries available for aspirants in the Data Science community.
The constraints developers faced a year ago are addressed well by the Python Community with a robust solution addressing problems of a specific nature.
One of the significant factors behind the remarkable upsurge of Python in the industry is its ecosystem. Many volunteers are developing Python libraries as Python has extended its hands to the Data Science Community, which has led the way for the most modern tools and processing in Python. The community helps these Python aspirants with relevant solutions to their coding problems.
Graphics and Visualizations
Python provides various visualization and graphical options, which are very helpful for generating insights into the data available. Matplotlib is a plotting library in Python that provides a solid base around which other libraries like Seaborn, pandas, and ggplot have successfully built. These packages help you get a good sense of data and create charts, graphical plots, web-ready interactive plots, and much more.