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How Python can be used for Data Science & Data Analysis?

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Key takeaways:

  • Features of Python for data science.
  • How Python can be used for data science? 

Today, every company, whether small or large, generates 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 does. 

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 to a data scientist must know at the beginner level itself. One such 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 Python that makes it beginner-friendly. Not only this, but it also regards 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 include its platform-independent nature, flexibility, English-like structure, etc. 

Why is Python preferred worldwide by developers/data scientists?

The following reasons justify the popularity of Python worldwide.

  • Powerful and easy to use:

As 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. 

  • A vast number of libraries:

Libraries form a vital part of high-order programming. For machine learning, artificial intelligence, data science, etc., Python comprises a vast number of libraries for effective coding. 

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

  • 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 first 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 necessary data from the internet or from any other source. 

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

Stage 3:

Now, after the data extraction and data cleaning, data visualization is the next target in front of the data scientist. When there are numerous 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, machine learning models/algorithms are implemented on the data. 

For this purpose, the Scikit Learn library is put into use. 

So, all these 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, another library, you can use OpenCV.

So, this blog justifies the value of Python in data science.

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