Python, an object-oriented, multi-purpose programming language, today, is widely used by tech giants like Disney, Microsoft, and NVIDIA, as well as startups. Companies are always looking for professionals skilled in Python, widely applied in developing software, mobile applications, websites, and many more.
Python is also the mainstay in another field: Data Science and AI. It is used to build machine learning models for prediction, classification, segmentation, and complex deep learning models for tasks like forecasting website traffic, speech-to-text, audio classification, etc.
69% of machine-learning developers and data scientists are extensively using Python.
If you’re wondering why you should learn Python, its benefits, difficulty, or how long it takes, you’re in the right place! This article will address whether Python is worth learning, how to learn it quickly, and the proficiency levels needed. Let’s begin learning!
What is Python Programming?
Python is a versatile, high-level programming language used for general-purpose software engineering. As a server-side language, it handles processing logic for user inputs, interacts with databases, and communicates with other servers.
Python was developed in the late 1980s by Guido Van Rossum. It has coexisted for decades alongside other server-side languages like Java, and C. Van Rossum designed Python to be intuitive and easy to read and write, drawing inspiration from the English language and minimizing complex syntax.
Python, an open-source language, has seen a surge in popularity, particularly in data science. The language boasts a vibrant community focused on machine learning, data modeling, data analysis, and artificial intelligence (AI), with a wealth of resources and libraries to support these fields.
Fun Fact: Python is named after the British comedy group Monty Python.
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Top 7 Reasons to Learn Python
Learning Python offers numerous benefits and opportunities, from its versatile applications to its beginner-friendly nature. If you’re considering exploring programming or enhancing your skill set, Python is a powerful and valuable language to learn. Here are some compelling reasons why Python should be on your radar:
#1 High Demand & Rapid Growth
Python is one of the most popular and fastest-growing programming languages. It is extensively used in domains such as automation data science, AI, machine learning, deep learning, and web development, and across industries and sectors.
Some stalwarts that use Python are NASA, Google, Facebook, Netflix, Amazon, Dropbox, Spotify, Slack, Capital One, and Intel. Python developers are in high demand, and the demand for skilled Python resources will continue to grow.
Over 75% of hiring managers believe that Python programming language is crucial for data professionals, regardless of their experience level, according to the latest Data Science Skills Survey 2025 Report by TOP IT Academy. Hence, you can be sure there’ll be plenty of career opportunities for Python programming skills.
#2 Beginner-Friendly and Easy Comprehension
Python is a very user-friendly language, i.e., it is straightforward to read and learn. It follows the rule of thumb: “Write less, do more”. It has very concise syntax in plain English. You can understand this by an example below to print “Hi! Welcome to the Python course!”:
In Java:
class WelcomeToPythonCourse {
public static void main( String args[] ) {
System.out.println( “Hi! Welcome to the Python course!” );
}
In Python:
print(“Hi! Welcome to the Python course!”)
Such handy and simple lines of code improve the user’s learning curve and reduce Python learning time.
#3 Python is Versatile
Python is a very versatile and multifaceted language. It can perform diverse tasks ranging from small to complex and across different industries. It has use cases in data mining, data analysis, data science, AI, machine learning, web development, software development, gaming, graphic design applications, testing, automation, and scripting.
#4 Higher Salaries
Python offers very lucrative and high-paying jobs. As per Glassdoor.com, the average entry-level salary of a Python developer in India is₹4L — ₹8L/yr. There are immense job opportunities for people skilled in Python programming, which offers high growth and well-paying projects.
#5 Extensive Open-Source Libraries
Python provides a wide range of packages, libraries, and modules. A Python library has pre-written codes: functions and classes that can be reused without the need to write the code again.
These libraries, modules, and packages are open-source and can be easily installed using a shell command. These frameworks have wide applications, from data manipulation, visualization, statistical computations, machine learning, deep learning, neural networks, web processing, and software development.
Over 137,000 Python libraries exist in the Python Package Index (PyPI). You need not write every code from scratch in Python as you can leverage its libraries and modules. For a beginner, Python’s inbuilt libraries are a blessing. Some of the most popular and widely used Python libraries are listed below:
- Pandas and Numpy: They are used for importing, munging, manipulating, and analyzing relational and labeled data.
- SciPy: It is applied to scientific and technical computing. It consists of statistics, optimization, linear algebra, integration, signal, and image processing modules.
- Matplotlib, Seaborn, & Plotly: They are applicable for data visualization and plotting of the graphs.
- Scrapy, BeautifulSoup: The Scrapy library also allows you to do web crawling, web scraping, and data scraping.
- SciKit-Learn or SkLearn: Sklearn is an end-to-end library for machine learning from model building to model evaluation.
- Keras, TensorFlow, PyTorch: These libraries are one-stop solutions for deep learning projects.
#6 Supportive and Helpful Community
When adopting a new habit, whether exercising or learning a programming language, having support or a partner is essential. Python is no exception, boasting a vast, supportive community that caters to all expertise levels, from beginners to experts.
With nearly three million GitHub repositories, this robust community provides ample resources, documentation, and forums for discussing issues, sharing ideas, and developing software and web projects. Additionally, local and global meetups enable coders and developers to connect and collaborate.
#7 Portability and Extensibility
Python offers portability, allowing code written in Python to be compatible with various platforms, including Windows, Mac OS, and Linux, without needing any modifications.
Additionally, Python’s extensibility feature enables developers to perform cross-language tasks, integrating Python code with Java, .NET components, or C/C++ libraries. Python’s portability is made possible by its interpreted nature, converting source code into machine code during runtime.
With these features and versatile applications, Python is increasingly popular and a highly sought-after programming language to learn.
Learning Stages of Python
Python learning stages can be categorized based on difficulty levels, such as basic, intermediate, advanced, expert, or functional. Going forward, we’ll explore the Python learning levels based on functionality:
Stage 1: Basics of Python
The first step in learning Python is to install it on a Jupyter Notebook system and get comfortable with its environment. Jupyter Notebook is a popular Integrated Development Environment (IDE), but alternatives like Spyder, PyCharm, and Visual Studio Code exist.
Jupyter Notebook is especially favored by data scientists. It lets you write code alongside text, images, HTML, and LaTeX in a single document. You can create visualizations, equations, and more and see the output immediately after entering the code. Additionally, the Jupyter Notebook is easy to debug.
Google has also adopted Jupyter Notebook, offering Google Colaboratory as an alternative.
Now, the list of topics and basic concepts that a beginner must know are:
- Variables
- Data types
- Data Structures: lists, dictionaries, tuples, sets
- Indexing, Slicing
- File input-output operations (read, write to text files)
- Conditional statements
- Control flows (if-else-elif statements)
- Loops (For, While, and nested)
- Functions
- Methods
- Modules & Imports
- *args and **kwargs
- Iterables
- Python shell, basic arithmetic
- Exception handling
- Dictionary and lists comprehensions
- zip(), enumerate() functions
- Anonymous function: Lambda
- Regular Expressions
- Object-Oriented Programming
- Inheritance
- Generators, Decorators
Stage 2: Python for Data Analysis
The next phase in Python learning levels is Exploratory Data Analysis (EDA), which delves into the core of data science, focusing on data cleaning, munging, and visualization. To accomplish these tasks effectively, a solid understanding of the following libraries for data analysis and visualization is essential:
- Pandas
- NumPy
- Matplotlib, and
- Seaborn
Here, you will learn to do the following tasks:
- Import and clean data
- Conduct exploratory data analysis
- Use visualization techniques and
- Understand the best feature engineering practices
Stage 3: Python for Statistics & Math
After mastering the basics of Python, data cleaning, EDA, and visualization, it’s essential to explore the fundamental statistics of data science, especially using Python.
Statistics is the foundation for various tasks behind a model, including training and evaluation. You must be comfortable with:
- Inferential Statistics:Understanding sampling methods, confidence intervals, and hypothesis testing.
- Hypothesis Formulation and Testing:Formulating testable hypotheses and using statistical tests to validate or refute them.
- Handling Imbalanced Data:Techniques for skewed class distributions in machine learning models.
- Train-Test Split:Properly segmenting data into training and testing sets for model evaluation.
Invest time learning the mathematics and statistics behind these algorithms to understand how machine and deep learning algorithms work. You’ll also need to code them using Python libraries like Scipy and statsmodels.
Stage 4: Python for Machine Learning
The last phase in Python learning levels is building machine and deep learning models. There are various Python libraries based on your needs and area of interest. Key libraries include:
- NLTK and spaCy:For text data and natural language processing (NLP). Learning these libraries helps you tackle text data challenges like creating chatbots and building recommendation systems.
- TensorFlow and PyTorch:These deep learning libraries are computational frameworks for expressing complex algorithms.
- Scikit-Learn or sklearn:This machine-learning library offers a range of tools, including supervised and unsupervised learning algorithms, regression, dimensionality reduction, clustering, ensemble methods, parameter tuning, and feature extraction and selection.
How is Python Used for Data Science?
Data Science is an interdisciplinary domain that involves extracting useful insights from large amounts of structured and unstructured data. It uses various statistical tools, scientific approaches, machine and deep algorithms, and big data.
Python is immensely useful in every step of a data science project’s life cycle, from ingesting the data to building web applications. Following are the ways how Python is used in data science:
1) Integrate with SQL
Python can be connected with SQL to pull data from the respective database by writing a query.
2) Data Mining and Wrangling
Python Programming is incredibly useful for all data exploration processes. The Pandas library is essential for analyzing structured data, while NumPy supports scientific computing and data analysis. SciPy is invaluable for conducting statistical analysis and hypothesis testing.
For preprocessing and cleaning text data, Python offers tools like RegEx, NLTK, and spaCy for tokenization and text manipulation. Additionally, OpenCV and PIL are available for image preprocessing.
3) Data Visualization
Python offers various visualization libraries. These are extensively used to illustrate how the data looks for all univariate, bivariate, and multivariate analyses. Another handy library for EDA and visualization is Pandas Profiling, which allows you to generate reports in fewer codes instantaneously.
4) Model Building
Keras is preloaded with built-in functions for machine learning, sci-kit-learn, and deep learning, including data preprocessing and transformation. Import, and your model is ready! However, you would need to tweak and tune the parameters for better accuracy while minimizing error.
5) Build Applications using Flask
The Flask framework in Python allows you to implement machine learning or deep learning models. Flask is a Python API that allows you to construct web applications. It is used the same way as importing other Python modules and packages. This web framework is easy to use and offers tools, libraries, and technologies.
6) Frameworks for Machine and Deep Learning
Python has in-built specific modules, packages, and libraries for machine and deep learning algorithms. These packages come with pre-defined functions and codes; after importing them, your model is one step away from being executed!
7) Interactive and Shareable Format
Python Jupyter notebooks are very interactive. You can write notes and explain your codes; they are also very presentable. The notebooks can be saved and downloaded in HTML and readily shareable PDF formats. It is easy to share the model results and final analysis in respective formats with others.
Conclusion
Python continues to evolve, with its origins dating back to February 1991, when it was created by Guido van Rossum. Over the years, it has undergone numerous updates and improvements, making it a dynamic and versatile programming language. Learning and mastering any programming language is an ongoing process requiring consistent daily practice to progress.
When starting with Python, it’s not just about the time it takes to learn it; it’s about the dedication and effort you invest in pursuing your goals. You will deepen your understanding and expand your skills by actively engaging with Python and exploring its wide range of applications, from web development to data science. These factors, combined with perseverance, will significantly impact the time it takes to master Python.
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