The Python programming language has found a home within the financial industry to help crunch data and find cross-sell opportunities, healthcare to predict disease prognosis, the weather industry to facilitate environmental predictions and more; but why exactly is Python used for Data Science? As a general-purpose language it can be leveraged for web and desktop applications in addition to numeric and scientific applications, however, there are 3 key components of the language that made it especially useful for Data Analysts and Data Scientists.
3 Reasons Why Python Is Used For Data Analysis
1. Python Is Easy To Learn
The Python language employs a simple syntax that makes learning it especially easy for non-engineers. Additionally when compared to other languages, Python uses less code to perform similar tasks. Many people come into the data science community from academia and they don’t necessarily have extensive development or engineering experience, so Python’s ease of use makes it ideal for folks of all backgrounds to adapt.
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Our Software Developer Foundations course provides the introductory skills in Python that you need to build a successful Data Science career.
2. Python’s Libraries Were Made For Data Science
Python offers a rich set of libraries and tools that are each designed to ease the daily tasks of a Data Scientist. Since Data Scientists work with extremely large data sets on the regular, these libraries can be a huge time saver. Additionally, Data Scientists spend a good portion of their days in repetitive data crunching and manipulation so Python’s ability to automate those tasks are definitely welcome. Some of the most popular Python libraries for data science include:
- Scikit Learn
3. Large Community Base
Python is an open-source tool and has a large community base that is made up of both developers and data scientists. This community allows users to bring problems and questions to a group of diverse and skilled minds to uncover solutions faster.
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