Name of the Project
• Basic Data Manipulation-Visualization-Exercise-Python
Tool used for analysis
• Python
Packages
• NumPy • Pandas • Matplotlib • Seaborn
Discussion
Python for Data Science
As the world entered the era of big data in the last few decades, the need for better and efficient data storage became a significant challenge. The main focus of businesses using big data was on building frameworks that can store a large amount of data. Then, frameworks like Hadoop were created, which helped in storing massive amounts of data.
With the problem of storage solved, the focus then shifted to processing the data that is stored. This is where data science came in as the future for processing and analyzing data. Now, data science has become an integral part of all the businesses that deal with large amounts of data. Companies today hire data scientists and professionals who take the data and turn it into a meaningful resource.
When it comes to data science, we need some sort of programming language or tool, like Python. Although there are other tools for data science, like R and SAS, we will focus on Python and how it is beneficial for data science. Python as a programming language has become very popular in recent times. It has been used in data science, IoT, AI, and other technologies, which has added to its popularity. Python is used as a programming language for data science because it contains costly tools from a mathematical or statistical perspective. It is one of the significant reasons why data scientists around the world use Python. If you track the trends over the past few years, you will notice that Python has become the programming language of choice, particularly for data science. There are several other reasons why Python is one of the most used programming languages for data science,
including:
• Speed - Python is relatively faster than other programming languages
• Availability - There are a significant number of packages available that other users have developed, which can be reused
• Design goal - The syntax roles in Python are intuitive and easy to understand, thereby helping in building applications with a readable codebase