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Python for Data Science - Learn the Uses of Python in Data Science

Last Updated : 06 Aug, 2025

In this Python for Data Science guide, we'll explore the exciting world of Python and its wide-ranging applications in data science. We will also explore a variety of data science techniques used in data science using the Python programming language.

We all know that data Science is applied to gather multiple data sets to collect information, project the insight, and interpret it to make an effective business decision. However, being a data scientist requires you to learn some of the best and most highly used programming languages, such as Java , C++ , R , Python, etc . Among these, Python has been considered the preferred choice among data scientists throughout the globe.

Python has been in demand for the past few years and the recent survey also suggested the same, Python leads the chart among the top programming languages in both the TIOBE index & PYPL Index. However, to support this, there are 5 concrete reasons behind this,

1. Easy To Learn: Being an open-source platform, Python has a simple and intuitive syntax that is easy to learn and read. This makes it a great language for beginners to learn data science.
2. Cross-Platform: Being a developer, you don't need to worry about the data types. The reason is, Python allows developers to run the code on Windows, Mac OS X, UNIX, and Linux.
3. Portable: Being an easy & beginner's friendly programming language, Python is highly portable in nature which means that a developer can run their code on different machines without making any further changes.
4. Extensive Library : Python has several powerful libraries that make data analysis and visualization easy. Pandas is a library for data manipulation and analysis, NumPy is a library for numerical computation, and Matplotlib is a library for data visualization.
5. Community Support: Python has a large and active community that supports and contributes to the development of various libraries and tools for data science. This community has created many useful libraries, including Pandas, NumPy, matplotlib, and SciPy, which are widely used in data science.

However, there are a lot more reasons to opt for Python for Data Science such as OOP, expressive language, the ability to allocate memory dynamically, etc. and that's the reason for using Python Programming Language for Data Science applications.

In summary, Python is a popular language for data science because it is easy to learn, has a large and active community, offers powerful libraries for data analysis and visualization, and has excellent machine-learning libraries.

In terms of application areas, Data scientists prefer Python for the following modules:

1. Data Analysis
2. Data Visualizations
3. Machine Learning
4. Deep Learning
5. Image processing
6. Computer Vision
7. Natural Language Processing (NLP)

Also, Read:

• How to Learn Python from Scratch in 2023?
• Python Projects - Beginners to Advanced

In terms of application areas, Data scientists prefer Python for Data Analysis , Data visualizations , Machine Learning , Deep Learning , Image processing , computer vision , and natural language processing (NLP) .

Engineers coming from academia and industry are saying deep learning frameworks the scientific packages available with Python have made Python incredibly productive and versatile. Python deep learning frameworks have undergone significant evolution, and they are quickly evolving.

Explore list of useful resources to learn Python if you are seeking your career in data science.

• Getting Started with Python:
  • Why Python?
  • Python installation
  • Python Interpreter
  • Virtual Environment
  • Ipython
  • Jupyter Notebook
  • Variables in Python
  • Comments in Python
  • Python Keywords
• Python Operators:
  • Arithmetic Operators
  • Comparison Operators
  • Logical Operators
  • Bitwise Operators
  • Assignment Operators
  • Identity Operators and Membership Operators
• Python Data Types:
  • Python Data Types
  • Numbers
    • Int()
    • float()
    • complex()
  • String
    • String Slicing
    • String lower() and upper() methods
    • Various String methods
    • String Concatenation
    • String Format()
    • Escape Sequences in Python
    • Universal Character set(Unicode) Strings
    • Byte Objects vs String in Python
  • Booleans Data Types
  • Type Conversion in Python
  • input() method in Python
• Conditional Statement in Python:
  • if & else
  • Nested if
• Loops in Python:
  • for Loop
  • while loops
  • range()
  • enumerate()
  • Continue
  • Break
  • Nested Loop
• Data structure in Python
  • Python Data Structures
  • Lists
  • List Slicing
  • Tuples
  • Sets
  • Dictionary
• Reading and writing files with Python:
  • File handling with Python
  • Reading data from text-file
  • Writing data to text-file
  • JSON with Python
  • Reading and Writing JSON to a File in Python
• Functions in Python
  • Create functions in Python
  • In-built functions in Python
    • Python Built-in Functions
    • Lambda
    • map()
• Object-Oriented Programming (OOPs)
  • Introduction of OOPs
  • Python OOPs Concepts
  • Python Classes and Objects
  • Encapsulation in Python
  • Class Instance Attributes in Python
  • Python Class Members
  • Class method vs Static method
  • Python Inheritance
  • Types of Inheritance Python
  • Inheritance, examples of object, issubclass and super
  • Polymorphism in Python
  • Abstract Classes in Python
  • Exception Handling
  • Python Decorators

• Numpy Tutorial
• Pandas Tutorial
• Scipy Tutorial
• Statsmodel Tutorial
• SweetViz

• Matplotlib Tutorial
• Seaborn Tutorial
• Plotly Tutorial

• OpenCV Tutorial

• Sckit-Learn Tutorial
• Deep Learning Tutorial:
  • Tensorflow Tutorial
  • PyTorch Tutorial

Also, Read:

• How to Learn Python in 21 Days?
• Data Science with Python Tutorial

Well, this is the end of this Python for data science article, I hope you're full with excitement about the possibilities of Python in the world data science. Python is very powerful language and it opened doors to a world of insights, predictions, and informed decision-making. But remember, the journey is just beginning!

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