Introduction to Python for Data Science: Why Python?

Introduction to Python for Data Science: Why Python?


11 min read

In the dynamic world of data science, one language has consistently emerged as a go-to tool for beginners and experts alike: Python. Python for data science has become a powerful combination, offering a simple yet robust approach to analyzing and interpreting complex data.

Whether you're a seasoned data scientist or a curious beginner, understanding Python's role in data science can open up new opportunities and enhance your data analysis skills.

In this blog post, we'll explore why Python has become so popular in the data science community, how it compares to other languages like SAS and R, and delve into the rich ecosystem of Python libraries that make data analysis more accessible and efficient.

Python's Journey: A Brief History and Evolution of Python Programming Language

Python, a widely-used general-purpose, high-level programming language, was initially designed by Guido van Rossum in 1991 and developed by the Python Software Foundation.

The language was conceived in the late 1980s, and its implementation began in December 1989 by Guido van Rossum at CWI in the Netherlands as a successor to the ABC language capable of exception handling and interfacing with the Amoeba operating system.

Over the years, Python has seen a steady evolution with the release of several versions, each introducing new features and improvements.

The first version of Python was released by Guido van Rossum in 1991. Since then, there have been major releases roughly every 18 months, with Python 2.0 coming out in 2000 and Python 3.0 in 2008.

Each version brought significant changes and improvements to the language, making it more powerful, flexible, and user-friendly.

Python's popularity has grown exponentially over the years, thanks to its simplicity, readability, and the vast array of libraries and frameworks it offers.

Today, Python is one of the most popular coding languages, widely used by programmers worldwide, including big tech companies, small businesses, start-ups, and freelancers.

For more detailed information about the history and evolution of Python, you can visit the History of Python page on Wikipedia.

Why Python for Data Science?

Python's popularity in the realm of data science can be attributed to several key factors:

  1. Ease of Learning: Python's syntax is designed to be readable and straightforward, which makes it an excellent language for beginners.

  2. Versatility: Python is a general-purpose language, meaning it can be used for much more than just data analysis. From web development to machine learning, Python's versatility is a major advantage.

  3. Powerful Libraries: Python's strength lies in its rich ecosystem of libraries, especially for data science. Libraries like Pandas for data manipulation, NumPy for numerical computations, Matplotlib for visualization, and Scikit-learn for machine learning make Python a powerful tool for data science.

  4. Community and Support: Python has a large and active community of users who contribute to a vast number of resources, tutorials, and forums online. This means that if you're stuck with a problem, there's a high chance that someone else has encountered it before and a solution is available.

  5. Integration Capability: Python can easily integrate with other languages and tools, and it's capable of handling different data formats, making it a flexible choice for diverse data environments.

Python for data science: a 5w1h framework overview

Comparing SAS, Python, and R for Data Analytics

While Python is a popular choice for data science, it's not the only game in town. SAS and R are two other languages commonly used in the field. Let's see how they stack up.


SAS (Statistical Analysis System) is a software suite developed by the SAS Institute for advanced analytics, business intelligence, data management, and predictive analytics. It is widely used in the corporate world, especially in sectors like healthcare and finance.


  • Comprehensive and robust software suite.

  • Excellent technical support.

  • Good for complex data manipulation.


  • Excessive cost.

  • Steep learning curve.

  • Less versatile than Python.


R is a language and environment for statistical computing and graphics. It's a favorite among statisticians and has a wide array of packages for specialized analysis.


  • Excellent for statistical analysis and visualization.

  • Large package ecosystem.

  • Open-source and free.


  • Steeper learning curve than Python.

  • Less versatile for tasks outside of data analysis and statistics.


As we've already discussed, Python is a versatile, easy-to-learn language with powerful libraries for data analysis.


  • Easy to learn.

  • Versatile.

  • Powerful libraries for data analysis.


  • Less built-in statistical functionality than R (though this can be supplemented with libraries).

  • Not as comprehensive as SAS for enterprise-level analytics.

Python in Data Science: A Statistical Overview

Python has seen a significant rise in popularity over the years, especially in data science. According to a 2023 report from Statista, Python is one of the most widely used programming languages among developers worldwide.

Python's usage among developers stands at 48.07%, making it a top choice for many. This popularity is driven by Python's simplicity, versatility, and robust set of libraries and frameworks it offers for data analysis and machine learning, such as pandas, NumPy, and scikit-learn.

The growth of Python is also evident in the open-source community. The year-on-year increase in open-source downloads for Python was the highest among all ecosystems in 2022, indicating a thriving community and active development.

In the realm of data science, Python is a key player. The most used technologies in the data science tech stack worldwide in 2022 included Python-based tools and libraries, further cementing Python's role in this field.

The rise of Python has also impacted the job market. Knowledge of Python opens up opportunities for various roles, including data analysts, data scientists, machine learning engineers, and more. As Python continues to dominate, these trends are expected to continue, making Python a valuable skill for anyone looking to break into the field of data science.

Career Opportunities in Data Science with Python: Diverse Paths to Explore

Learning Python opens a plethora of career opportunities, especially in the realm of data science. Here are a few job roles you might consider:

  1. Data Scientist: This is the most obvious role. Data scientists use Python to analyze and interpret complex data, helping businesses make decisions. They use Python libraries like Pandas, NumPy, and Scikit-learn to process data, perform statistical analysis, and implement machine learning models.

  2. Data Analyst: Data analysts use Python to collect, analyze, and interpret raw data. They use Python libraries like Pandas and Matplotlib to clean data, perform exploratory data analysis, and create visualizations. The insights derived by data analysts are often used to drive business decisions.

  3. Machine Learning Engineer: Machine learning engineers use Python to design, build, and deploy machine learning models. They use libraries like Scikit-learn, TensorFlow, and PyTorch to implement algorithms, train models, and make predictions.

  4. Data Engineer: Data engineers use Python to build and maintain data architectures. They use tools like Apache Hadoop and Apache Spark to process large datasets, and they often use Python to write scripts to automate data processing tasks.

  5. Bioinformatics Specialist: In the field of biology and medicine, Python is used for genome analysis, drug discovery, and bioinformatics research. Python's ability to handle large datasets and perform complex computations makes it ideal for these tasks.

  6. Quantitative Analyst: In finance, quantitative analysts use Python to develop complex financial models. Python libraries like NumPy, Pandas, and Scikit-learn are used for tasks ranging from data analysis to predictive modelling.

Each of these roles leverages Python's capabilities in diverse ways, but all require a solid understanding of Python and its data science libraries. By learning Python, you're equipping yourself with a versatile skill set that can open doors to these and many other exciting career paths.

Exploring Salary Prospects for Python-Based Data Science Roles in India

The salary for data scientists using Python in India varies based on experience, skills, and the specific role. Here are some average salaries for various roles based on data from 2023:

  1. Data Scientist: The average salary for a Data Scientist in India is ₹910,238. This can range from ₹353,000 to ₹2,000,000, with additional bonuses and profit sharing adding to the total compensation (source).

  2. Entry-Level Data Scientist: For those with less than one year of experience, the average salary is around ₹500,000 per year (source).

  3. Early Career Data Scientist: Data scientists with 1 to 4 years of experience can expect to earn around ₹610,811 per year (source).

  4. Machine Learning Engineer: While specific figures for 2023 are not readily available, machine learning engineers earn similar salaries to data scientists, with potentially higher earnings for those with experience or specialized skills.

  5. Data Analyst: Data Analysts typically earn less than data scientists, reflecting the lower level of technical expertise required for the role. However, experienced data analysts with Python skills can still command attractive salaries.

  6. Data Engineer: Data Engineers, who focus more on the design and construction of data architectures, can earn salaries comparable to data scientists, especially at senior levels.

Python Libraries for Data Science

Python's rich ecosystem of libraries and frameworks is one of the reasons it's a top choice for data science. Here are a few key ones:

  1. NumPy: Short for 'Numerical Python', NumPy is the foundational package for mathematical computing in Python. It provides support for arrays, matrices, and high-level mathematical functions to operate on these arrays. For example, you can use NumPy to perform complex mathematical operations like Fourier transforms and matrix multiplication.

  2. Pandas: This library provides high-performance, easy-to-use data structures and data analysis tools. With Pandas, you can manipulate data frames and perform operations like grouping, merging, reshaping, etc. For instance, you can use Pandas to read a CSV file into a Data Frame and perform operations like filtering data based on conditions, aggregating data, etc.

  3. Matplotlib: This is a plotting library for creating static, animated, and interactive visualizations in Python. You can generate plots, histograms, power spectra, bar charts, error charts, scatterplots, etc., with just a few lines of code.

  4. Scikit-learn: This is one of the most popular machine-learning libraries in Python. It provides simple and efficient tools for data mining and data analysis. It's built on NumPy, SciPy, and matplotlib. You can implement various algorithms like linear and logistic regression, decision trees, clustering, etc., using Scikit-learn.

  5. TensorFlow: Developed by Google Brain, TensorFlow is a powerful library for numerical computation and large-scale machine learning. TensorFlow bundles together a slew of machine learning and deep learning models and algorithms. It uses Python to provide a convenient front-end API for building applications with the framework while executing those applications in high-performance C++.

  6. Keras: It is a high-level neural networks API, capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

  7. PyTorch: Developed by Facebook's artificial intelligence research group, PyTorch is a deep learning framework that provides maximum flexibility and speed during implementing and building deep neural network architectures.

These libraries and frameworks, among others, make Python a powerful tool for data science.

The Power of Community: Python's Global Network of Enthusiasts

Python's large and active community is a key strength of the language. Comprising developers, data scientists, and enthusiasts worldwide, this community contributes to Python's growth by creating libraries, answering queries, and contributing to open-source projects. They also organize events like PyCon for knowledge sharing and networking.

This vibrant community makes Python more than just a programming language - it's a platform for collaboration and learning.

Ready to dive into Python for data science? Here are some resources to get you started:

  1. The official Python website. You can download Python and find the official documentation here.

  2. Pandas Documentation: The official documentation for the Pandas library, a powerful

tool for data manipulation in Python.

  1. NumPy Documentation: The official documentation for NumPy, a library for numerical computations in Python.

  2. Matplotlib Documentation: The official documentation for Matplotlib, a library for creating static, animated, and interactive visualizations in Python.

  3. Python Data Science Handbook: A free online book that covers a variety of tools and techniques for data science in Python.

  4. Kaggle: A platform for data science and machine learning competitions. It's a great place to practice your skills and learn from other data scientists.

  5. DataCamp: An online learning platform for data science. They offer a variety of courses on Python for data analytics.

Further Resources:

Beginner-Friendly Courses for Learning Data Science with Python

Here are some beginner-friendly courses on data science with Python that you might find interesting:

  1. Introduction to Python and Programming for Data Science and Machine Learning by Learn Ventures: This course briefly introduces programming using Python, preparing you for data analysis and machine learning. It covers topics like data types, variables, control flow, functions, packages and libraries.

  2. Guided Project: Predict World Cup Soccer Results with ML by IBM: This direct project allows you to develop practical Python, pandas, NumPy, sklearn, seaborn, matplotlib, seaborn, LIME, and SHAP skills to process data using the 2022 World Cup teams' data. Then, you'll train a model to predict the outcome of the group stages.

  3. Python Programming: Basic Skills by Codio: This course is designed for learners with no coding experience, providing a solid foundation of not just Python, but core Computer Science and software development topics that can be transferred to other languages. The modules in this course cover printing, operators, iteration (i.e., loops), selection (i.e., conditionals), and lists.


In conclusion, Python for data science is a powerful combination that offers a wide range of tools and capabilities for data analysis. Its simplicity, versatility, and robust set of libraries make Python an excellent choice for both beginners and seasoned professionals in the field of data science.

Whether you're analyzing complex datasets, visualizing data trends, or implementing machine learning algorithms, Python provides the tools you need to succeed.

As we've seen, Python's advantages over other languages like SAS and R, its rich ecosystem of libraries, and the strong community support make it a standout choice.

So, if you're considering a career in data science or looking to enhance your data analysis skills, Python is a great place to start. Happy coding!

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