The evolution of the digital era with modern technologies, smart devices, and live solutions has added a deep value for the term “Data”, as it is an essential aspect of any industry and business. It is very crucial to collect, process, and analyze the data flow and to do that as quickly and accurately as possible. Due to the digital transformation that took place the data volume can be large, which makes information handling time-consuming and expensive. Due to this primary reason, the data science and data analytics industry is growing at a rapid pace creating new vacancies and possibilities.
Python as per its developer’s view is a high-level programming language with dynamic semantics. Its high-level built-in data structures have brought it as a powerful scripting tool combined with dynamic typing and dynamic binding make it very attractive for dynamic application development. Data analytics is the process of interpreting data and analyzing the results utilizing statistical techniques and providing ongoing reports. Data analysts are responsible for the development and implementation of data analysis, collection, and other strategies that optimize statistical efficiency, quality and also acquire data from primary or secondary data sources and maintaining databases.
Why Data Scientists Love Python
Python has been known as a simple programming language to pick up, and that is the sole reason for Python being the preferred language for data scientists, as they need an adaptable language that has decent library availability. The numbers don’t lie, Projects that have inactive communities are usually less likely to maintain or update their platforms, which is not the case with Python.
Data Scientists don’t want to be working with complicated programming requirements. They want to use programming languages that are simple like Python and Ruby to perform tasks hassle-free. Python being a general-purpose programming language allows them to create CSV output for easy data reading in a spreadsheet. And also more complicated file outputs that can be ingested by machine learning clusters for computation. Python has a valuable role in a data analyst’s toolbox and it is tailor-made for carrying out repetitive tasks and data manipulation.
Python being an Open Source Software, it is flexible, easy to learn, and also is supported by a wider forum. A large variety of other Python libraries are available out there in these communities. Libraries such as NumPy, Pandas, and Matplotlib, help the data analyst to carry out his or her functions and should be looked at once they have Python’s basics known to them.