Python Roadmap 2023

A programming language easy to learn and have good human readability is recognized as Python. This is why love to learn this is the first choice for beginners. It is one of the most popular programming languages today. It is the first programming language you should learn when talking about Multipurpose programming

Guido van Rossum is the father of python started working on it in 1980 and the first release in 1991 as python 0.9.0. Today, it is widely used by programmers for tech giant projects like AI, Machine Learning, Deep Learning, etc, and daily automation tasks. In this article, we’ll discover all about computer languages where to start, and Discover some Applications. 

Python Roadmap contents

Why learn Python?

Its ease of use helps those who are new to coding and programming and can be an excellent first step. It’s relatively easy to learn, making it a great way to start building your programming knowledge.

It’s simple syntax. Python is relatively easy to read and understand, as its syntax is more like English. Its straightforward layout means that you can work out what each line of code is doing. 

It’s a thriving community. As it’s an open-source language, anyone can use Python to code. What’s more, there is a community that supports and develops the ecosystem, adding their contributions and libraries. 

Its versatility. As we’ll explore in more detail, there are many uses for Python. Whether you’re interested in data visualization, artificial intelligence, or web development, you can find a use for the language. 

Highly demanded. Across a wide range of fields, there is a demand for those with Python skills. If you’re looking to start or change your career, it could be a vital skill to help you. 

It could lead to a well-paid career. Data suggests that the median annual salary for those with Python skills is around £65,000 in the UK. 

There will be many job opportunities. Given that it can be used in many emerging technologies, such as AI, machine learning, and data analytics, it’s likely a future-proof skill. Learning Python now could benefit you across your career

Major Application of Python in IT

It is a general-purpose and high-level-oriented language. It is used in building websites and software, automating tasks, and conducting data analysis. Python is utilized by everyone from data scientists to software developers even kids used it for their simple project programming.

Applications in different IT

  • Data Science
  • Ai & Machine Learning 
  • Cybersecurity 
  • Web Development
  • Game Development 

Python for Data Science 

It is one of the most popular programming languages among data scientists. The simplicity of learning and simple index makes it more utilized in the field of data science. It provides numerous libraries to maximize data science Applications. 

Some libraries for Data Science

  • TensorFlow
  • NumPy
  • SciPy
  • Pandas
  • Matplotlib
  • Keras
  • SciKit-Learn
  • Scrappy
  • BeautifulSoup


TensorFlow is a free and open-source software library for machine learning and artificial intelligence for data scientists. It helps them to implement best practices for data automation, model tracking, performance monitoring, and model retraining.


NumPy stands for numerical python and is one of the great Python libraries used for working with arrays. It also has functions for working in the domain of linear algebra, Fourier transform, and matrices.


SciPy provides algorithms for optimization, integration, interpolation, eigenvalue problems, algebraic equations, differential equations, statistics, and many other classes of problems. It wraps highly-optimized implementations written in low-level languages like Fortran, C, and C++. Enjoy the flexibility of Python with the speed of compiled code.


Pandas is a fast, powerful, flexible, and easy-to-use open-source data analysis and manipulation tool, built on top of the Python programming language.  It provides data structures for efficiently storing and manipulating large datasets, as well as tools for processing and analyzing data.

Some of the key data structures provided by Pandas are:

  • Series: A one-dimensional array-like object that can hold any data type, including numeric, string, and boolean data.
  • DataFrame: A two-dimensional table-like data structure, where each column can have a different data type. It can be thought of as a spreadsheet or SQL table.

Pandas provide many functions for reading and writing data in different file formats, such as CSV, Excel, SQL databases, and more. It also supports many data manipulation and cleaning operations, such as merging, grouping, filtering, sorting, and transforming data.


Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible.


Keras provides a Python interface for artificial neural networks and acts as an interface for the TensorFlow library. It is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides. 

SciKit- Learn 

Scikit-learn is an open-source data analysis library and the gold standard for Machine Learning (ML) in the Python ecosystem. Key concepts and features include Algorithmic decision-making methods, including Classification: identifying and categorizing data based on patterns.


Scrapy is a free and open-source web-crawling framework written in Python and developed in Cambuslang. Originally designed for web scraping, it can also be used to extract data using APIs or as a general-purpose web crawler. It is currently maintained by Site, a web-scraping development, and services company.


BeautifulSoup is a Python library for pulling data out of HTML and XML files. It works with your favorite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree. It commonly saves programmers hours or days of work.

Python for AI and Machine Learning

Jean Francois Puget, from IBM’s machine learning department, expressed his opinion that Python is the most popular language for AI and ML and based it on a trend search results on 

According to the graph from Francois Puget, Python for AI and ML is the major code language.

Why python is good for AI & ML?

1. Python has a great library ecosystem for Artificial intelligence and Machine learning

A great choice of libraries is one of the main reasons Python is the most popular programming language used for AI. A library is a module or a group of modules published by different sources like PyPi which include a pre-written piece of code that allows users to reach some functionality or perform different actions. Python libraries provide base-level items so developers don’t have to code them from the very beginning every time.

Some of the most widespread libraries you can use in Python for ML and AI:

Scikit-learn for handling basic ML algorithms like clustering, linear and logistic regressions, regression, classification, and others.

Pandas for high-level data structures and analysis. It allows the merging and filtering of data, as well as gathering it from other external sources like Excel, for instance.

Keras for deep learning. It allows fast calculations and prototyping, as it uses the GPU in addition to the CPU of the computer.

TensorFlow for working with deep learning by setting up, training, and utilizing artificial neural networks with massive datasets.

Matplotlib for creating 2D plots, histograms, charts, and other forms of visualization.

NLTK for working with computational linguistics, natural language recognition, and processing.

Scikit-image for image processing.

PyBrain for neural networks, unsupervised and reinforcement learning.

Caffe for deep learning that allows switching between the CPU and the GPU and processing 60+ mln images a day using a single NVIDIA K40 GPU.

StatsModels for statistical algorithms and data exploration

2. Low entry barriers 

Working in the ML and AI industry means dealing with a bunch of data that you need to process most conveniently and effectively. The low entry barrier allows more data scientists to quickly pick up Python and start using it for AI development without wasting too much effort on learning the language.

Python in Cybersecurity 

Cybersecurity experts use python for penetration testing as well as automating security processes. A cybersecurity analyst uses Python scripts to automate tasks, such as implementing penetration testing. A large number of cybersecurity applications and tools are based on and heavily rely on Python, which means they can be customized according to individual needs and requirements. 

Here are some Python Applications for Cybersecurity

1. Network Scanners: Python can be used to develop network scanners that can identify the vulnerabilities of network systems.

2. Password Crackers: Python can be used to develop password-cracking tools that can help identify and exploit weaknesses in password security.

3. Vulnerability Scanners: Python can be used to develop tools that can detect and identify vulnerabilities in network systems.

4. Web Application Security: Python can be used to develop tools that can scan web applications for vulnerabilities such as SQL injection and cross-site scripting.

5. Malware Analysis: Python can be used to develop tools that can detect and analyze malware, such as viruses and trojans.

6. Log Analysis: Python can be used to develop tools that can analyze log files to identify potential security threats.

7. Incident Response: Python can be used to develop tools that can help respond to security incidents, such as automated notifications or blocking suspicious IP addresses.  

8. Forensics: Python can be used to develop tools that can assist in digital forensics, such as analyzing disk images or decrypting encrypted files.

9. Data Analytics: Python can be used to develop tools that can analyze large amounts of security-related data to identify patterns and potential threats.

10. Penetration Testing: Python can be used to develop tools for performing penetration testing, such as tools for identifying open ports or performing brute force attacks.

Python for Web development

Python is a popular programming language for web development, thanks to its flexibility, readability, and extensive set of libraries and frameworks. There are many options for using Python for web development, including: 

1. Django: Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design. It includes an ORM, template system, admin interface, and other features that make it easy to build complex web applications.

2. Flask: Flask is a micro web framework that is lightweight and flexible, making it a good choice for small to medium-sized web applications. It provides extensions for adding functionality and is easy to use for beginners. 

3. Pyramid: Pyramid is a general-purpose web framework that is flexible and scalable. It supports a variety of databases and templating engines, making it a good choice for larger web applications. 

4. Tornado: Tornado is a Python web framework and asynchronous networking library. It is known for its ability to handle a large number of simultaneous connections, making it a good choice for building real-time applications. 

5. Bottle: Bottle is a fast and simple microweb framework for Python. It is easy to use and includes built-in support for templating, routing, and accessing databases.

Python can also be used with other web technologies, such as HTML, CSS, and JavaScript, to build dynamic and interactive web applications. With its vast ecosystem of libraries and frameworks, Python is a powerful tool for web development.

Python for Game development 

It is a powerful and versatile programming language that can be used for game development. Here are some libraries and frameworks that can be used for developing games in Python:

1. Pygame: Pygame is a popular game development library for Python. It provides functionality for creating games, handling user input, and rendering graphics and sounds.

2. PyOpenGL: PyOpenGL is a Python binding for OpenGL, a powerful 3D graphics library. It allows you to create games with 3D graphics and animations.

3. Panda3D: Panda3D is a game engine that is written in C++ but provides Python bindings. It is used for developing 3D games and simulations.

4. Arcade: Arcade is a simple and easy-to-use game development framework for Python. It provides built-in support for sprite rendering, sound effects, and user input handling.

5. Pyglet: Pyglet is a cross-platform multimedia library for Python. It provides support for windowing, user input handling, and multimedia playback. It can be used for game development, as well as other multimedia applications.

6. Kivy: Kivy is a cross-platform application development framework that is designed for building touch-based user interfaces. It can also be used for developing games that can be run on mobile devices.

A Career as a Python Developer

A career as a Python developer can be very rewarding and offer many opportunities. While, It is a popular programming language used in a wide range of fields, including web development, data analysis, machine learning, scientific computing, and more.

To become a Python developer, you should start by learning the basics of the language and programming concepts, such as variables, data types, loops, functions, and objects. You can learn Python through online courses, tutorials, and books, and by practicing coding on your own.

Once you have a good understanding of the language, you can start building your portfolio by working on personal projects, contributing to open-source projects, or participating in hackathons or coding competitions.

To improve your Some skills that are important for a successful career as a Python developer include problem-solving, attention to detail, communication skills, and a willingness to continuously learn and improve your skills.

In terms of job opportunities, Python developers are in high demand, and many companies are looking for professionals with Python skills. Some potential job titles include Python developer, web developer, data analyst, data scientist, machine learning engineer, software engineer, and many others.

Overall, a career as a Python developer can offer a wide range of opportunities and a promising future in the tech industry. chances of finding a job as a Python developer, you should also consider obtaining certifications or attending boot camps or specialized training programs. Networking with other developers, attending meetups or conferences, and keeping up with the latest industry trends can also be helpful.

Salaries of Python Developers

The salary for a Python developer can vary depending on various factors such as location, experience, industry, and company size. However, according to data from various sources, the average salary for a Python developer in the United States is around $110,000 per year.

In other countries, the average salary may differ, but there are usually similar trends. In Europe, for example, the average salary for a Python developer can range from €30,000 to €70,000 depending on the country and level of experience. In India, the average salary for a Python developer is around ₹500,000 to ₹1,000,000 per year.

It’s important to note that entry-level positions may offer lower salaries, while senior Python developers with several years of experience and specialized skills can earn much more. Also, salaries may vary depending on the industry, with industries like finance and healthcare typically offering higher salaries than others.

Overall, a career as a Python developer can offer a good salary and opportunities for growth and advancement. It’s also worth noting that the demand for Python developers is high, and this can create a favorable job market with more bargaining power for experienced professionals.

Top Learning Platform of Python

Several learning platforms offer courses in Python programming language. Here are some of the top learning platforms to consider:

1. Coursera: Offers a wide range of Python courses from beginner to advanced levels, with options to earn a certificate upon completion.

2. EdX: Similar to Coursera, edX offers Python courses taught by top universities and institutions worldwide.

3. Codecademy: Provides a hands-on learning experience with interactive exercises and projects for Python learners.

4. Udemy: Offers a vast collection of Python courses taught by industry experts, covering a wide range of topics.

5. Pluralsight: Provides courses on Python programming, data analysis, machine learning, and more.

6. DataCamp: Focused on data science and data analysis, It provides interactive coding exercises and courses on Python and other programming languages.

7. LinkedIn Learning: Provides courses on Python programming, web development, data analysis, and more, taught by industry professionals.

8. SoloLearn: Offers free Python courses, quizzes, and challenges with a gamified learning experience.

These platforms offer various types of courses, including video lectures, interactive coding exercises, quizzes, and projects. Choose the one that suits your learning style and goals.

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