Introduction

In today’s digital world, data analysis is vital in every field of life. When working with data or big data, data analysts widely use Python as a primary programming language.Now, the question arises as to why data analysts always need the Python programming language and why it is so famous and a favourite among the data analyst community. In the following paragraphs, we will explore the answer to this question. Using data insights after its analysis plays a vital role in the growth of a business by observing consumer behaviour, trends, and risks involved. Data analysis also plays a crucial role in every field of life if used wisely. For example, it can add value to business, production processes, cost reduction, and human resource development. If you are starting your journey of learning data analysis and are keen to know how to master data analysis, please follow this blog. 

Key fields where Data analysis plays a very vital and crucial role

The following is a list of primary and key fields in which data analysis plays a vital role. We will not discuss this here in detail, but a summarized list is under

• Finance: Used for risk management, credit scoring and algorithmic trading
• Health Care: Medical diagnosis, treatment optimization, public health, health care operations
• E-Commerce and Retail: customer trends, sales trends, customer segmentation, marketing planning, price strategies, logistics optimization, discount offers, and advertisement plans.
• Education: Student evaluation, curriculum development, teachers’ evaluations, learning trends in different subjects.
• Manufacturing: Logistics optimization, production process efficiency, cost and reduction, supply chain optimization, quality control, predictive maintenance.
• Government policies: Policies about the taxation system, taxation levy, public projects, health projects, urban projects, inflation control, interest risk control, and business sector analysis for contribution to the treasury.
• Environmental Control: Pollution tracking, wildlife conservation, disaster prediction, weather change prediction.
  In addition to this key field, data analysis is used in many other fields, such as aviation, transportation, service provision, telecommunication, and information technology.

Data scientists and analysts analyze data and fetch valuable insights for optimization and value addition in the above-described fields. Data analysts and scientists use significant tools and techniques to analyze, visualize, and report data insights, such as MS Excel, Power BI, Power Query, R programming language, Tableau, and Python as primary programming languages. So why do data analysts and scientists always need Python as a primary programming language? The key features that attract them to the Python language are described below,

Key features of Python programming language that attract data analysts and scientists towards Python programming language

Ease of use and simple syntax

Python and its syntax are straightforward, just like the standard English language. Coding in this language is complex or challenging, and syntax is not as complicated as in other languages. User focus turns towards productivity rather than consuming time on the language’s complex syntax. Due to this feature, it always attracts users.

Open source and huge community

Python is an open-source programming language, which means anyone can participate in its development and growth. A vast global community participates in its development and progress every hour. Due to this vast community, tons of documentation, tutorials, books, webinars, and projects are publicly available online. The language itself and its libraries are continuously updated.

Machine learning and artificial intelligence support

Python efficiently supports machine learning and artificial intelligence. Using Python makes future predictions easy for stakeholders to elaborate on and visualize. Tools powered by sci-kit, TensorFlow, and Keras libraries predict future outcomes from past data and build machine learning models.

Integration with third-party tools and databases

Data analysts and scientists working with data stored in third-party databases, cloud services, and external APIs can easily integrate this data with Python. The SQLAlchemy library allows interaction with databases using SQL commands, and PyODBC or psycopg2 can connect to relational databases like MySQL, PostgreSQL, and SQL Server.

Wide range of libraries and frameworks

Python’s rich and varied ecosystem, complete with libraries, allows it to analyze, visualize, and manipulate data effectively and efficiently. This is its core strength for users. Following are some libraries used by data analysts and scientists while working with data,

Pandas: it is used in cleaning, manipulating, filtering, aggregating, and re-shaping large data set
NumPy: while interacting with numbers, this library made it easy to play with numbers and solve complex data-related problems. Efficient array operations and mathematical functions are made easy with NumPy.
Matplotlib and Seaborn: Visualization of outcomes plays a vital role in communicating with stakeholders; these two libraries make it easy to visualize the result.
SciPy: This library is extensively used to work with scientific data and apply statistical analysis.
StatsModels: This library supports statistical modeling, regression analysis, hypothesis testing, and time series analysis.
Repetitive tasks automation: Data analysis is repetitive, and Python makes it easy to automate. Automation can help clean, update, and preprocess data. It helps automate tasks like sending emails while certain conditions are met, generating reports, etc. By automating tasks, time-saving is ensured, and the user can focus their attention on productivity rather than on repeating tasks each and every time, which is very time-consuming.
Data Manipulation, visualization, and transformation capabilities

Python and tools powered by its libraries make it easy to manipulate, visualize, and transform data very quickly and efficiently. Matplotlib, Seaborn, Plotly, and Bokeh are famous and widely used libraries for the abovementioned tasks. These libraries save tons of time, which users can use for real-time productivity and efficiency.

Conclusion

In short, Python is a versatile, easy-to-use programming language with a global community and rich ecosystem. It makes data analysis, visualization, manipulation, and reporting easy. Python has emerged as a powerful tool for accomplishing these tasks today and heavily relies on data-driven decisions. This is why data analysts and scientists are always attracted to Python programming.

The above article demonstrates why data analysts always need Python for data analysis.

Suggestions and feedback welcomed by the author; happy learning.