Role of Python in data science and data analysis: Can we think just once, What does a data analyst do? Data analysts are responsible for analyzing the data. They make the use of statistical techniques and helpful in generating reports. They mainly work on making various strategies. There are different types of tools that are available for data analytics like Hadoop, SAS, SQL and R programming. So the question is Why is Python for data analysis? Python is the most popular tool for analyzing data. It is easy to use. It is considered a Swiss Army Knife of the coding world. As it is helpful in structured programming, object-oriented programming, etc. According to Stack Overflow, Python is one of the popular languages in the programming world. In this blog, we will discuss the role of Python in data science and data analysis.
Why does Python work as the best fit for data science?
Python so many unique properties that makes it easy to use when we need to do quantitative and analytical computing. it has very good features it is used For creating applications that are developing data science and machine learning. The various advantages of python are in signal processing finance and other fields. Python has been used to make Google internal infrastructure strengthened. Moreover it helps in building several applications that are related to YouTube. mainly data science facts is a widely used tool and are freely available.
Uses of Python over other Data Science tools
Easy to use: Firstly it is easy to use Python is considered as the easiest and beginner language and student who has a little bit coding skills can also start working on Python they don’t need to spend their maximum time on coding and other purposes because Python is very simple as compared to other programming languages like C, C + + Java. it takes less implementation time so it is a more favorable language by all the people in different groups.
Scalability: It is very scalable because it can help the help to solve the problems that are unable to solve by using another programming languages so as compared to another programming languages it is as fast and scalable and many business people are making use of python to develop various kind of applications from it.
Different libraries: Python has so many inbuilt libraries in it that make it a good fit for Artificial Intelligence and machine learning. some of the TensorFlow libraries, sci-kit learn, matplot and many others.
Visualization: There are so many options for visualization that are available in Python. Its library includes matplotlib that provides a strong foundation in connection with other libraries like ggplot, Pandas plotting and others that are inbuilt these are used to create the charts graphical layouts and web-ready plots.
Use of Python in various stages of data science and data analytics
As we know that Python is so much useful language so let’s check how Python is used in every stage of data science and data analytic process.
The first stage involves the main understanding of data, I mean what kind of data we are using and this data consists of a very large volume. it means we need to process large chunks of data that can be in a row or column form this process can be a very challenging process so for saving time and effort.
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Usually, Data Scientists aren’t provided with data in every case. Various web scraping techniques are used for pulling out relevant data. For data mining purposes, python libraries like Scrapy and BeautifulSoup are used.
The extracted form of data is now ready to present in the visualization form. The visualization form can either be of charts, graphs. Various python libraries like Matplotlib and seaborn are preferred for making graphs.
The next step involves constructing complex Machine Learning models that need performing advanced functions like matrix multiplication, probability and calculus. Python has the presence of distinct libraries like Scikit-Learn that support Machine Learning operations.
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In this blog, we have discussed the role of Python in data science and data analysis. If you are having any doubt, feel free to ask me in the comment box.