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In order to understand if Python is a good fit for data analysis, it is important to know the exact role of a data analyst. With a clear understanding of the job role, one can make better choices of the Python library tools that will fit in each requirement of data analysis.

Role of a data analyst

In day-to-day life, we tend to make decisions based on our previous experiences. The role of a data analyst is quite similar; the only difference is they do it from the perspective of a business house. Data analysts are basically responsible for taking down the data and analyzing the results with the use of statistical techniques and preparing reports. They are also responsible for obtaining data from primary or secondary data sources and thereby maintaining a database. Their prime intention is to extract useful information from different data and take decisions based on the analysis.

An idea about Python

Python is a programming language of high-level, used for web development at the server end, app development, software development, system scripting, and mathematics. It is basically a programming language used for general purposes. Python allows you to focus on the core functionality of an application by taking care of the common programming tasks. It was designed by Guido van Rossum and released in the year 1991.

Python’s popularity is growing in several industries like retail banking, aerospace, insurance, finance, healthcare, etc., particularly in Machine Learning projects. It is a dynamic language supporting both object-oriented programming and structured programming. In this present IT scenario, to learn Python is probably the easiest thing and its huge library makes the tasks a lot simpler.

What makes Python ideal for data analysis?

The following points prove why Python is ideal for data analysis:

  • Data can be present in different forms. Assuming data is present in huge excel sheets with a huge number of columns and rows it is a challenge to search for a particular type of data from that sheet. It can be really time consuming and cumbersome. But with Python’s libraries like Pandas and Numpy that uses parallel processing, the task can be accomplished with ease.
  • Acquiring data is another challenge. It is not always the situation that the data is readily available. At times one needs to dig into the data from the internet, which can be a challenge. In this case, the two libraries of Python Scrapy and Beautifulsoup prove to be helpful.
  • The next stage is a pictographic representation of the data. The best way of representing the data in the visualization mode is through bar graphs, pie-charts, and histograms. Python has solutions for this too. For this, libraries like Seaborn and Matplotlib give the ultimate results.
  • Now comes the most important part, i.e. machine learning. This involves substantial mathematics like probability, calculus and matrix operations including thousands of columns and rows. All these turn out to be very simple with the help of the Python library Scikit-learn.
  • When the data is in the form of images, Python has a solution for that too. These images can be operated with the help of an open-source library of Python named Opencv.

Conclusion

So, we can see that Python forms a valuable part of the toolbox of a data analyst. With its help, data analysts can freely handle some of the toughest parts of their job making it interesting and attaining rewarding results. Thanks to the wide variety of Python libraries available. So it would be a wise choice to learn python which would help you in understanding the Data Analytics Courses.

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