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How Python can be used for Data Science?

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How Python can be used for Data Science? Are you wondering about this? So, this article is for you. Today, every company, whether small or large, generate tons of data. This data contains both, valuable information and not so valuable information. 

It is important to extract some valuable insights from the data that are very beneficial for the company’s future and market value. But how? So, this is what a data scientist usually do. 

A data scientist deals with tons of the company’s data and extracting useful insights from it. So, data science is a multidisciplinary field that comprises the usage of scientific methods, algorithms, and tools for data cleaning and extraction. 

There are many key aspects of a data scientist must know at the beginner level itself. One such a thing is the knowledge of a particular programming language. Now, just like tons of data, there are tons of languages too. 

But, the vote for the clearest and the simplest language to start with goes to Python. Don’t know why? So, here it is. 

Python is a general high-level multi-purpose language with a simple syntax. There is nothing complex in the python that makes it beginner-friendly. Not only this, we regard it as the Swiss army knife of coding. 

According to a survey, Python is the most widely used language for data analysis and data science. 

Other benefits of working with Python includes its platform-independent nature, flexibility, English-like structure, etc. 

Why Python is preferred worldwide by developers/data scientists?

The following reasons justify the popularity of Python worldwide.

1.Powerful and easy to use:

Compared to its counterparts like C/C++, R, Java, Ruby, etc., Python is much simpler and powerful. While using it, the programmer gets more time to concentrate on the algorithms rather than brainstorming over the code. 

2.A vast number of libraries

Libraries form a vital part of high-order programming. For Machine Learning, Artificial Intelligence, Data Science, etc., Python comprises of a vast number of libraries for effective coding. 

Some libraries are NumPy, Pandas, Matplotlib, Tensorflow, etc.

3.Scalability

Python is highly scalable in comparison to other popular programming languages.

How Python is used in Data Science?

It is a clear fact that data science comprises various stages and success at every stage is the priority. So, Python plays a significant role in making it possible. 

Now, let’s study how Python is put into use at every stage of data science.

Stage 1:

In this stage, the key thing to know is what type of data is present and in which form. Usually, a data scientist deals with data present in Excel sheets, with thousands of rows and columns. Out of them, not every row/column is of use. 

So, it becomes very difficult to deal with it through manual coding for every row/column. Here, NumPy and Pandas, two of the well-known libraries of Python come to your fore. 

Using their functions, you can easily deal with every column.

Stage 2:

This stage is important from the data extraction point of view. As data never appears in the readable form, it is important for the scientist to scrap the data from the Internet or from any other source. 

For this purpose, Scrapy and BeautifulSoup are put into use. 

Stage 3:

Now, after data extraction and data cleaning, data visualization is the next target in front of the data scientist. When there are many numbers on the screen to represent visually, one can’t do it manually. 

For this purpose, Python’s library, Matplotlib, is used. Using this library, the data scientists represent data in the form of pie charts, line/bar charts, histograms, etc.

Stage 4:

This is the last stage of Data Science and so far, the most complex one. Here, you implement machine learning models/algorithms on the data. 

For this purpose, Scikit Learn we put library into use. 

So, all 4 stages determine the popularity and efficiency of Python towards data science. 

Now, one more thing to consider here is that all these libraries work well with only textual data. So, if you are dealing with images/pictures, it uses another library.

So, this blog justifies the value of Python in Data Science. 

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