In today’s digital environment, programming is becoming a crucial skill used by many professionals in various industries. One of the languages that have grown in popularity over time is Python for Data Analysis. According to estimates, there will be 8.2 million Python developers worldwide in 2022.
Python is becoming increasingly popular for a variety of reasons. One explanation is that the language has a relatively flat learning curve, enabling programmers to accomplish tasks with fewer lines of code.
Among the many professionals who use Python on a daily basis are data analysts. Let’s first define a data analyst before we examine why data analysts favor Python.
Who is a Data Analyst?
A data analyst analyses recent datasets using cutting-edge statistical methods to generate insightful findings and reports. The field of data science heavily relies on computational analysis and data from the real world to draw conclusions.
Data scientists and data engineers are two different professions in the field. The primary distinction between a data scientist and a data analyst is how the latter responds to questions or problems by using the data that is at their disposal. A data scientist, on the other hand, makes predictions based on the collected data.
On the other hand, data engineers create the infrastructure and architecture for the generation of data and are more likely to have a background in software development and engineering than in data analysis.
What makes Python more appealing to data analysts than other languages?
Python has grown in popularity over time as a result of a number of factors that make it appealing to both beginning and experienced developers. Python has undergone significant improvements since its 1991 debut to become the user-friendly and high-performance programming language it is today. Here are some of the reasons Python is so popular with data scientists and analysts.
1. Python is Simple and Flexible
Python is an approachable programming language for beginners that enables you to script websites and applications to be more individualized and user-friendly. Working with complicated and sizable datasets also requires this flexibility feature. With the aid of ML-powered algorithms, data analysts can use Python to create, edit, and customize data models. Similar to this, systematizing datasets and using data mining to accomplish a variety of tasks quickly are both possible.
2. Python is well-supported and open-source.
Python is open-source, cost-free, and has a strong, vibrant developer community. Python can easily switch between various platforms and runs in Linux and Windows environments. For data visualization, there are a number of open-source Python libraries that can be used, including matplotlib, seaborn, and plotly; for data engineering, data science, and machine learning, there are NumPy, pandas, sklearn, PyTorch, and TensorFlow.
These libraries are constantly being developed and improved thanks to the vibrant developer community. Both novice and seasoned data analysts who want to advance their analytic abilities may find this helpful.
3. Python Offers Support for Visualization and Graphics
Data analysts can work with a variety of data thanks to Python’s support for visuals like graphs and charts. Techniques for visual data representation are simple to comprehend and remember, making them the perfect option for experts who handle large amounts of data.
4. Knowing How to Use Python for Data Analysis
Python is an open-source programming language, as was previously mentioned, so all the resources are freely accessible to everyone. Both good and bad come from this. It’s advantageous because it lowers costs and quickens the process of developing applications. The fact that open-source software can introduce two different types of risk makes this a bad thing as well. The first is the risk to intellectual property from reciprocal and constrained licenses, and the second is the risk to security from malicious code.
For many developers and businesses, the security risk is a major issue, especially for those who are ignorant of open-source vulnerabilities. Python ranked fifth among the least secure programming languages, with 5% vulnerabilities, according to recent research. With 47% of vulnerabilities, C was deemed to be the least secure, followed by PHP (17%) and Java (10%). It is reasonable to assume from this research that Python is more secure than the other languages on the list, but it is still wise to take security measures.
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One of the best programming languages with applications in a variety of fields and work settings is unquestionably Python. You should be aware of IP risk and cybersecurity vulnerabilities as a data analyst using Python to interpret data.