The Ultimate Python Roadmap 2025

Last Updated on March 10, 2025

Python is a powerful and easy-to-learn programming language. It has efficient high-level data structures and a simple but effective object-oriented programming system. Python’s elegant syntax and dynamic typing, along with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas, for most platforms.

The Python interpreter and extensive standard library are freely available in source code and binary form for most platforms from the Python web, https://www.python.org/, and can be freely distributed. The same site contains distributions and addresses to many third-party Python modules, programs, tools, and additional documentation.

The Python interpreter is easily extensible with functions and data types implemented in C or C ++ (or other languages ​​that allow it to be called from C). Python is also suitable as a language for extending modifiable applications.

Why should you learn Python?

Python is a useful language for almost all types of development. An excellent language for developing back-end tools and other types of software and AI objects. Let’s see why we need to learn Python.

Easy to Learn

As easy as learning Python code. It sounds like English. Three lines of java or C code can be replaced with three words of Python code. Python is a completely free open-source platform. You don’t need a complex language to handle Python.

Python Extensibility

As a versatile programming language, Python doesn’t impose strict guidelines or hard code rules to follow. You can easily perform all kinds of operations. It is absolutely not necessary to choose the ideal platform that is supported on most platforms, from Windows to Linux. Easily integrate Java and .net components (including these components).

Test

The main advantage is that Python has a built-in testing framework that covers error correction and workflow. There are tools like selenium that make these test possible in cross browsers and other platforms. Testing is a very difficult task and Python is a perfect language to help you.

The Role of Python in Big Data and Automation

Python supports the manipulation of large data. Some functions support the processing of various types of information. Added support for parallel computing using Python. Using Python with Big Data makes it easier to use. As Python can also be used as a text language, it is equally important in automation.

These are a few things you should know in Python programming:

How to Install Python?

Once you have installed your GNU/Linux distribution, you will already have Python installed on your system. To check, you open a terminal (press Alt + F4 and then type in the field of search gnome-terminal) and type python as shown below:

eugenia @ cochito: ~ $ python

Python 2.7.2+ (default, Oct 4 2011, 20:03:08)

on linux2

Type “help”, “copyright”, “credits” or “license” for more information.

What you will see on the screen is the Python interactive shell. To get out of the shell

interactive, press the Ctrl + D keys.

eugenia @ cochito: ~ $ python

Python 2.7.2+ (default, Oct 4 2011, 20:03:08)

on linux2

Type “help”, “copyright”, “credits” or “license” for more information.

>>> print “Hello World!”

Hello World!

>>>

If instead of the interactive shell, you see an error message similar to “python: command not

found ”, you must follow the following steps to install it:

Update the list of repositories:

eugenia @ cochito: ~ $ sudo apt-get update

Update the Operating System:

eugenia @ cochito: ~ $ sudo apt-get upgrade

Install Python:

eugenia @ cochito: ~ $ sudo apt-get install python2.7

About The Commands

Sudo: makes you a super user. Only user who has permissions to install packages on your operating system. apt-get: is the utility to manage packages in distributions GNU / Linux based on Debian. Alternatively, you can use the aptitude command instead of apt-get. 

Update: apt-get option that synchronizes the index files of packages with the official repositories. (in other words, gets an update index)

Upgrade: apt-get option that upgrades the system. install: is the apt-get option indicating that one will be installed or more packages.

Language Elements

As in most high-level programming languages, Python uses It is made up of a series of elements that feed its structure. Between them, we can find the following:

Variables

A variable is a space to store modifiable data, in the memory of a computer. In Python, a variable is defined with the syntax:

name_of_variable = value_of_variable

Each variable has a name and a value, which defines the data type of the variable. There is a type of “variable”, called constant, which is used to define values fixed, that do not require to be modified.

WBS 8: variables

Use descriptive and lowercase names. For names compounds, separate words by underscores. Before and after the = sign, there must be one (and only one) space in white.

Correct: my_var = 12

Incorrect: MyVariable = 12 | myvariable = 12 | my_variable = 12 |

my_variable = 12

PEP 8: constants

Use descriptive and uppercase names separating

words by underscores.

Example: MY_CONSTANT = 12

To print value to the screen, in Python, use the print keyword:

my_variable = 15

print my_variable

The above will print the value of the variable my_variable on the screen.

Type of Data

A variable (or constant) can contain values of various types. Among them:

Text string (string):

my_string = “Hello World!”

my_multiline_string = “” “

This is a chain

of several lines

“” “

Whole number:

age = 35

Octal integer:

age = 043

Hexadecimal integer:

age = 0x23

Real number:

price = 7435.28

Boolean (true / false):

true = true

false = False

There are also other more complex types of data, which we will see later.

Python Roadmap for Beginners

Beginner

Data types

Lists

Strings

Tuples

Sets

Floats

Ints

Booleans

Dictionaries

Flow / loop control

for curls

while loops

if / elif / else

Arithmetic and expressions

I / O (input / output)

System module

Standard input / output

Read / write files

The functions

Exceptions and error handling

Basics of Object Oriented Programming (OOP) (simple classes)

Intermediate

Recursion

Regular expressions

More advanced OOP

Heritage

Polymorphism

Encapsulation

Method overload

Data structures

Linked lists

Batteries, queues

Binary search trees

AVL trees

Graphics

Trees covering minimum

Hash Cards

Algorithms

Linear search

Binary search

Hashing

Quick sort

Sort by insertion / selection

Sort by merge

Tri Radix

First in-depth search

First width search

Prim’s algorithm

Dijkstra’s algorithm

Algorithmic complexity

Big O notation

Advanced – A.I. / Machine Learning / Data Science

Statistics

Probability

Brute force research

Heuristic research

Distance from Manhattan

Acceptable and informed heuristics

Climbing

Simulated annealing

A search

Contradictory research

Minimax

Alpha-Beta Size

Greedy algorithms

Dynamic programming

Genetic algorithms

Artificial neural networks

Backpropagation

Natural language processing

Convolutional neural networks

Recurrent neural networks

Generative networks of adversaries

Advanced – Full Stack Web Development

Computer networks (no need to go into details but an understanding is necessary) Backend web development tools (This is for application logic and interfacing with databases etc.).

Ball

Django

Front-end framework (this is to communicate with the backend)

Angular 6+

React / Redux (These are libraries and not frameworks but most see them as a framework)

With frontend you will also need

HTML

CSS

JavaScript (also good for learning typography which is used in Angular. It makes JavaScript writing more enjoyable).

Relational Database (pick one and learn it, they’re all pretty similar)

MySQL

PostgreSQL

Non-relational

MongoDB

Apache Cassandra (wide column)

Caching systems

MemcacheD

Redis

Cloud Computing (knowledge of cloud computing is important these days)

AWS (offers the most services and has a one-year free tier)

Google cloud

Azure

Other ‘Must Knows’

Version Control

Git (start using Github right away, it’s your portfolio !!!)

SQL (relational and non-relational databases – Almost everywhere these days)

Code quality / Engineering best practices

Cloud Computing Basics – Pick a Vendor and Start Learning (AWS Recommended – Largest Choice of Services)

Scalable Systems Design – (https://github.com/donnemartin/system-design-primer)

Resources

Mid-level resources

Algorithms and data structures in Python by Goldwasser and Goodrich

Web Development with Python

Well known we find Django, the free and open-source web application framework written in Python. It is also not a newcomer and is used in production by companies with Instagram, Pinterest, or The New York Times.

To this Python framework, we can add some as interesting as the minimalist Flask or Pyramid. In addition to these frameworks, we can highlight the importance of creating Restful APIs or graphql with libraries like Graphene.

Python for Devops

The accessibility and flexibility of Python is also one of the reasons to prefer this language in DevOps. It’s great for scripting and automating the process. The fact that tools like Ansible and SaltStak are written in Python demonstrates the language’s capabilities for automation and orchestration tasks. As we talk about data science or machine learning, we can also highlight learning for System Admin using Python 3.

Python applied to Data Science

Here we find one of the main keys to the exponential growth of interest in Python in recent years. Hand in hand with Data Science as one of the best valued professions, whose base is based on mathematical languages ​​such as R and with Python thanks to libraries and frameworks such as PyBrain, NumPy or PyMySQL .

Using these tools we can do much more than collect and classify information, creating scripts to automate processes, in addition to preparing a dashboard with that information.

Machine learning from the hand of Python

The rise of Deep Learning with some frameworks like Tensor Flow has also motivated many developers to learn Python.

The exploratory nature of machine learning fits perfectly with Python, so we can find libraries such as Keras, PyBrain or scikit -learn to perform classification, regression, clustering, and preprocessing or algorithm model generation tasks.

As with the roadmap proposed to start with data science, in machine learning it happens quite similar, although we can use Java or Scala, Python is still a dominant language in the academic field since it fits perfectly when implementing the bases of machine learning.

In Conclusion

Python is a great first language as if it’s your second, third, or nth language. Its learning curve is less harsh than others, it has thousands of libraries that allow us to do what we propose in a few lines of code. It allows you to evolve quickly, in addition to delving into more complex tasks, as you gain fluency.

Obviously recommending a programming language is complicated. It depends on many factors such as the use that you are going to give it. Nor is it the same to recommend a language to someone who is just starting to program as to another programmer with extensive experience in various programming languages.

As we said above: this is not a language war but due to the current momentum of Python, you should hire RedBlink’s team of experts in Python, since it may be the language that helps you in your next project.

Now a days, data suggests low-code/no-code tools are actually opening doors for such non-developers. 60% to 70% of companies said non-developers in their company already build tools for internal business use, and nearly 70% – 80% predict to see more of this trend in 2021.