Python Programming Online Course

The Major objective of the Python Online Training Course is to deliver the best understanding of core Python concepts and how this programming language can be used for real-world apps.

You will also gain relevant knowledge in designing or developing any application effectively.

The Python Classes Online are structured in such a way that you will get a depth understanding of the latest industry standards.

If you are a beginner and know nothing about Python, then you will be able to build and deploy your own Python app at the end of this Python Classes Online.

For intermediates and advanced workforce, the course is beneficial to improve their existing knowledge base and helps them become Python pros.

os-This module provides various functions for interacting with the operating system. Along with this, it facilitates file and directory operations along with path manipulation. Furthermore, this solution also provides environment variable access and also deals with process management

sys- The purpose of this module is to provides access to system-specific parameters and functions. Along with his, it facilitates command-line arguments along with system exit. Furthermore, this solution helps in tasks like path manipulation and also provides great platform-specific information.

math- This module provides mathematical functions for various calculations. Along with this, its key features consist of trigonometric functions like sin, cos, tan, etc and logarithmic functions like log, exp. Furthermore, it is also beneficial for managing the mathematical operations.

random- Its primary purpose is to generate the random numbers and performs random sampling. Along with this, it also facilitates number generation and also helps in sampling from sequences. Furthermore, it is also useful for shuffling sequences.

datetime- It is useful for providing the classes for working with dates and times. Furthermore, it helps in managing the date and time objects and also facilitates the timezone handling. Above all, this solution also facilitates the date and time calculations.

time- The primary purpose of this module is to provide the functions for measuring time and performing time-related tasks. Along with this, its key features include time measurement (sleep, clock) and time conversions (time to string, string to time).

re- the primary purpose of this module is to provide regular expression matching operations. Along with this, it also facilitates pattern matching and also provides features of Search and replace. Furthermore, this solution also assists in grouping and capturing.

json- This solution provides functions for encoding and decoding JSON data. Alng with this, it facilitates the process of loading JSON data from files or strings. Along with this, it also helps in dumping Python objects to JSON format. Furthermore, this solution is useful for customizing encoding and decoding behavior.

csv- The primary use of this module is to provide various functions for reading and writing CSV files. Along with this, it also helps in reading CSV data into lists or dictionaries. This solution also facilitates writing the data to CSV files. Above all, it also customizes the CSV dialect and formatting

urllib- This is useful for providing the functions for working with URLs. Along with this, it is useful for opening URLs and retrieving data. Furthermore, this solution helps in parsing URLs and handling the HTTP requests and responses.

collections- The primary purpose of this module is to specialize the container data types. Along with this, it includes defaultdict which is dictionaries with default values. Furthermore, it also consists of OrderedDict which is dictionaries that preserve insertion order.

functools- These are useful for providing the higher-order functions for functional programming. Along with this, their key features include the partial application for creating new functions with some arguments pre-filled. It also helps in reducing the functions for combining elements of a sequence.

itertools- This module is useful for providing the tools for creating iterators. Along with this, its key features consist of Infinite iterators such as count, cycle, repeat and combinatoric iterators like product, permutations, combinations. Furthermore, this solution also helps in slicing and splitting iterators.

hashlib- The primary purpose of this module is to provides functions for computing cryptographic hashes of data. Along with this, it helps in hashing algorithms such as MD5, SHA1, SHA256, SHA384, SHA512. It also provides HMAC which refers to keyed-hash message authentication code.

subprocess- This module allows users to spawn new processes and interact with them. Along with this, it helps in running the external commands and capturing standard output and error. Furthermore, it is also useful for piping input and output between processes

logging- The primary purpose of logging module is to provide the functions for logging messages to files or the console. Along with this, ts key features consists of Log levels like DEBUG, INFO, WARNING, ERROR, CRITICAL. It also facilitates log formatting and manages this custom logging handlers.

argparse- This is a popular command-line argument parser useful for parsing command-line arguments. Along with this, it also helps in defining the arguments with types, help messages, and default values.

threading- The primary purpose of threading is to provide various tools for creating and managing threads in Python. Along with this, it also facilitates thread creation, thread synchronization and thread communication.

socket- It is useful for providing the functions for network communication. Along with this, it also helps in creating sockets and connecting to remote hosts. Along with it, this solution also helps in sending and receiving data.

multiprocessing- This module provides various tools for parallel processing using multiple processes. Along with this, its key features include process creation, process synchronization and process communication.

queue- The primary purpose of this solution is to provides thread-safe data structures for queues and stacks. Along with this, its key features consist of Queue (FIFO) data structure, LifoQueue (LIFO) data structure and Priority queues.

copy- This module is useful for provides functions for creating shallow and deep copies of objects. Along with this, it skey features consists of shallow copy for creating a new object with references to the original object's values. Furthermore, it also facilitates Deep copy for creating a new object with copies of the original object's values.

pickle- It is useful for providing the functions for serializing and deserializing Python objects. Furthermore, it offers various features like saving Python objects to files and loading Python objects from files.

email- This is useful for provides various tools for working with email messages. Along with this, it also helps in parsing email messages and creating email messages. Above all, this module helps in sending the email messages

sqlite3- The primary purpose of this module is to provides a built-in SQLite database interface. Furthermore, this also helps in creating and managing SQLite databases. Along with this, it also helps in executing SQL queries and retrieving and manipulating data

xml- This module provides various tools for working with XML data. Along with this, its key features include parsing XML documents and creating XML documents. Furthermore, this solution also helps in transforming XML data using XSLT.

decimal- The primary purpose of this module is to provide support for decimal floating-point arithmetic. Along with this, it provides arbitrary precision decimal numbers and offers great rounding modes. Furthermore, this module also facilitates great mathematical operations.

fractions- It is useful for providing support for rational numbers. Along with this, its significant features include fraction objects and Mathematical operations on fractions. To further know about it, one can visit Python Course Online.

base64- This is useful for providing the functions for encoding and decoding data in base64 format. Along with this, it facilitates encoding binary data to base64 along with decoding base64 data to binary.

shutil- It is useful for providing functions for copying, moving, and deleting files and directories. Along with this, it includes file and directory operations and also helps in copying files and directories recursively.

configparser- This is useful for providing the functions for parsing and manipulating configuration files. Along with this, it also helps in reading and writing configuration files in INI format. FSection and option handling

tkinter- This is useful for providing a GUI toolkit for creating graphical user interfaces. Along with this, it comes with various features such as widgets like buttons, labels, text boxes, etc.). Furthermore, this solution also facilitates great event handling and layout management.

http.server- The primary purpose of this solution is to provide a simple HTTP server for serving static files. Along with this, it also helps in serving the files from a specified directory. Furthermore, it is useful for handling HTTP requests and responses.

nittest- This is useful for providing a framework for unit testing in Python. Along with this, its key features include test cases, test suites, test fixtures and assertions.

doctest- This is useful for extracting the tests from docstrings and running them. Along with this, its features are docstring-based tests along with the example-based testing.

pdb- This is a popular Python debugger for interactive debugging. Along with this, it is useful for stepping through code and setting breakpoints. Furthermore, it is also useful for inspecting variables.

inspect- Its primary purpose is to provide functions for inspecting Python objects. Furthermore, it helps in getting information about functions, classes, and modules.

weakref- This is useful for providing the tools for creating weak references to objects. Along with this, it helps in preventing circular references along with tracking the object's lifetimes.

socketserver- Its primary purpose is to provide a framework for creating network servers. Along with this, its key features consist of TCP and UDP servers and it also helps in handling multiple clients

signal- It is useful for providing the functions for handling signals in Python programs. Along with this, it helps in registering signal handlers and is also useful for sending signals.

NumPy- The Numpy is a popular fundamental Python library for numerical computing. Using its library provides you with efficient multi-dimensional arrays and matrices. Furthermore, it comes with a vast collection of mathematical functions for operations like linear algebra, Fourier transforms, and random number generation. Using the NumPy library in Python Online Course is essential for scientific computing, data analysis, and machine learning tasks.

Pandas- This is a powerful data manipulation and analysis library built on top of NumPy. Using it provides you great data structures like Series and Data Frames which makes it an ideal solution for working with structured data. Along with this, it offers various solutions for data cleaning, filtering, grouping, merging, and reshaping. Thus, making it a valuable tool for data scientists and analysts.

Matplotlib- It refers to a comprehensive plotting library useful for creating a wide variety of static, animated, and interactive visualizations in Python. Along with it, this library offers extensive customization options and supports various plot types. Examples are line plots, scatter plots, histograms, bar charts, and more. Above all, Matplotlib is a popular choice for data visualization and exploration.

Seaborn- This is a popular and high-level data visualization library built on top of Matplotlib. Using this library provides you with a more user-friendly interface and offers a wide range of statistical plots. Thus, making it much easier to create attractive and informative visualizations. Along with this, Seaborn is also useful for exploring relationships between variables and understanding data distributions.

SciPy- This library includes scientific and technical computing tools in Python. Along with this,i it also consists of various modules for optimization, integration, interpolation, special functions, linear algebra, and more. Using SciPy is beneficial for conjunction with NumPy for advanced numerical computations and scientific simulations.

TensorFlow- This is a popular open-source machine learning framework developed by Google. Using this solution helps in building and training various types of neural networks. Along with this, TensorFlow provides a flexible architecture and supports a wide range of machine learning tasks, from image and speech recognition to natural language processing.

Keras- It is a popular high-level API built on top of TensorFlow. Using this solution helps in simplifies the process of building and training neural networks, making it a popular choice for machine learning practitioners. Keras provides a user-friendly interface and offers pre-trained models for common tasks like image classification and natural language processing.

PyTorch- This refers to a popular open-source machine learning framework developed by Facebook. This solution offers great flexibility along with an ease to use. Thus, making it a great solution for researchers and developers. PyTorch provides businesses with a dynamic computational graph. Along with this, it provides a more intuitive and flexible model building. This Python Online Course solution is widely useful for managing the deep learning tasks.

Scikit-learn- This is a popular Python library built on top of NumPy and SciPy. Using this library provides businesses with a collection of algorithms for machine learning tasks. Along with this, it also offers various tools for classification, regression, clustering, dimensionality reduction, and more. This library is well known for its efficient implementations of popular machine learning algorithms.

OpenCV- It refers to Open-Source Computer Vision Library and it is a powerful library for computer vision tasks. It provides a vast collection of functions for image processing, object detection, face recognition, and more. OpenCV is used in various applications, including robotics, surveillance, and augmented reality.

NLTK- It stands for the Natural Language Toolkit and it is a popular Python library for natural language processing tasks. Using it provides you with various tools for text processing, tokenization, stemming, tagging, parsing, and more. Along with this, NLTK is widely used for tasks like sentiment analysis, text classification, and machine translation.

BeautifulSoup- This is a popular Python library useful for parsing HTML and XML documents. In addition, this library provides you a simple API for navigating and searching through HTML or XML structures. Along with this, BeautifulSoup is often used for web scraping, extracting data from web pages, and creating custom HTML parsers.

Requests- It is a popular Python library useful for making the HTTP requests. Along with this, using it siplifyies the process of sending HTTP requests to web servers. Furthermore, it easily helps in interacting with the APIs and retrieving the data from the internet. Above all, it is a popular choice for web scraping, automation, and integration with external services.

Flask- It is a popular lightweight Python web framework useful for building web applications. Along with this, it offers essential features and ensures that developers can easily customize and extend the framework as per their requirements. Flask provides businesses with great simplicity, flexibility, and ease of use. Thus, making it an ideal solution for building web applications of various sizes and complexities.

Django- This full-stack web framework is written in Python and it provides a comprehensive set of tools and features for building web applications. Along with this, it is highly scalable and secure which makes it a suitable solution for developing large-scale web projects. Furthermore, this solution is also useful for building content management systems, e-commerce platforms, and social networking sites.

SQLAlchemy- This refers to a popular Python SQL toolkit and ORM that provides an abstraction layer over SQL databases. Thus, making it an ideal solution for interact with databases using Python code. Along with this, SQLAlchemy supports a wide range of databases, including PostgreSQL, MySQL, SQLite, and Oracle. Furthermore, this solution offers businesses with various features like object-relational mapping, query building, and database migrations.

Jupyter Notebook- This is an interactive computing environment which includes code execution, documentation, and visualization. Along with this, it is popularly useful for tasks like data analysis, machine learning, and prototyping. This solution provides support for various programming languages and is used by professionals like data scientists, researchers, and educators.

Pytest- It refers to a popular testing framework for Python that provides flexible and extensible approach to writing and running tests. Along with this, Pytest supports unit testing, functional testing, and integration testing. This solution offers great simplicity, eadability, and extensibility.

Selenium- This includes a suite of tools useful for automating web browsers. Along with this, it helps in testing the web applications, automating the tasks, and performing the web scraping. Furthermore, Selenium supports multiple programming languages, including Python.

Plotly- This is a popular Python library for creating interactive and customizable visualizations. It supports a wide range of plot types, including line plots, scatter plots, bar charts, histograms, and more. Along with this, Plotly is also useful for creating the interactive dashboards, exploring the data, and communicating the insights effectively.

Data Structures (Lists, Tuples, Dictionaries, Sets)- Lists are the ordered and mutable sequences of elements and Tuples are the ordered, immutable sequences of elements. The Dictionaries are the unordered key-value pairs and the sets are also the unordered collections of unique elements.

Object-Oriented Programming (OOP)- This includes classes and objects that are useful for creating custom data types and instances. Along with this, it also facilitates inheritance which is useful for creating new classes based on existing ones. It also ensures encapsulation for hiding internal implementation details.

Functions and Lambdas- The functions refer to the reusable blocks of code with parameters and return values. On the other hand, lambdas are the anonymous functions defined inline.

Exception Handling- It includes try-except blocks for handling errors and preventing program crashes. Along with this, it also helps in raising exceptions along with creating custom exceptions.

File Handling- This is useful for reading and writing files along with working with text and binary files. Furthermore, it consists of the file modes for opening files in different modes (read, write, append).

Regular Expressions- This includes pattern matching for finding and extracting text patterns. Along with this, it also provides validation and checks if the input data matches a specific pattern.

Iterators and Generators- These are the objects that can be iterated over using a for loop. Furthermore, the generators are the functions that return iterators and they are often used for efficient memory management.

Decorators- These are useful for modifying functions along with adding behaviour to existing functions without changing their code. Along with this, its common use cases include caching, timing, logging, and more.

Multithreading- This is useful for executing multiple tasks concurrently within a single process. Along with this, it includes various features such as thread synchronization (locks, semaphores), and thread communication (queues, pipes).

Web Frameworks (Flask, Django)- Flask is a popular lightweight and flexible web framework useful for building web applications. Furthermore, Django is a full-featured web framework that comes with built-in features for authentication, ORM, and more.

Data Analysis (Pandas, NumPy)- The Pandas is a powerful data manipulation and analysis library. On the other hand, NumPy is a fundamental library for numerical computing.

Data Visualization (Matplotlib, Seaborn)- Matplotlib is a popular general-purpose plotting library. On the other hand, Seaborn is a higher-level data visualization library built on top of Matplotlib.

Machine Learning (Scikit-learn, TensorFlow)- Scikit-learn is a popular machine learning library that comes with a wide range of algorithms. Coming to TensorFlow is a popular deep-learning framework.

APIs and Web Scraping (Requests, BeautifulSoup)- Requests is a library for making HTTP requests to web servers. On the other hand, BeautifulSoup is a library for parsing HTML and XML documents.

Testing (Unit Test, Pytest)- Unit Test is a built-in module for writing unit tests. Along with this, the Pytest is a popular third-party testing framework.

Packaging and Deployment (pip, setuptools)- pip is a package installer for Python and setuptools is a popular tool for building Python packages.

Version Control (Git)- This is useful for tracking changes to files and collaborating with others. It includes various features like git repositories, commits, branches, merging and pull requests.

Databases (SQLite, SQLAlchemy)- SQLite is a lightweight and embedded SQL database. On the other hand, SQLAlchemy is an ORM for interacting with databases.

Networking (Sockets)- This is useful for creating network connections and communicating with other computers, along with this, it includes features like TCP and UDP sockets along with client-server communication

Asynchronous Programming (asyncio)- This is useful for executing tasks concurrently without blocking the main thread. Its features include coroutines, event loops and asyncio-compatible libraries

Python is one of the top programming languages to learn today with amazing career opportunities and a sustainable future scope.

There is a 47% increase in demand for Python specialists

The average salary of a Python programmer at the entry-level is $74K per annum which is quite attractive when compared to other programming languages.

More than 43K Python jobs were posted last year in the USA alone.

As part of Python Online Classes we offer plenty of learning resources in terms of assignments, PPTs, MCQs, real-world projects, e-books, whitepapers, etc.

Upon the completion of the Python certification course, you will find yourself more eligible to apply for the global certification exam and gain credentials that are valid worldwide.

Make yourself industry-ready and work on multiple projects and assignments as part of the Python placement training. Our project-based learning helps you to grow in the Python space immensely.

Python Developer- These professionals are responsible for developing software applications. Along with this, they have to design, write, and test Python code. Furthermore, these professionals work on building web applications, APIs, and data analysis tools.

Data Scientist- They have to work on extracting the insights from data using statistical and machine learning techniques. Along with this, these professionals also work on collecting, cleaning, and preparing the data. Above all, they also analyze data using Python libraries like NumPy, Pandas, and Matplotlib. Etc.

Machine Learning Engineer- The primary job role of these professionals is to develop and deploy the machine learning models at scale. Along with this, they also have to design and implement machine-learning algorithms. These professionals also optimize models for performance and efficiency.

Data Analyst- As a data analyst, you will be responsible for analyzing the data to uncover trends, insights, and patterns. Furthermore, they have to work on collecting and cleaning the data. Along with this, they use statistical methods and data visualization tools.

Software Engineer- These professionals are responsible for developing software applications using various programming languages. Furthermore, they also work on work on designing, writing, and testing software. Above all, they also collaborate with other team members

Web Developer (Python/Django/Flask)- Their job role is to develop web applications using Python frameworks. Furthermore, they also create dynamic and interactive web pages. Along with this, they also build RESTful APIs and integrate with databases and other backend services.

Automation Engineer- They have to work on developing automated scripts and tools to improve efficiency and reduce manual tasks. Furthermore, these professionals also work on automated testing processes along with deployment and configuration tasks.

DevOps Engineer- Their primary job role is to bridge the gap between development and operations teams. Along with this, they also automate infrastructure management and implement CI/CD pipelines. Furthermore, these professionals ensure application reliability and performance.

Artificial Intelligence Engineer- Their primary job role is to develop and implement AI models and algorithms. Along with this, they also work on tasks like machine learning, natural language processing, and computer vision. Furthermore, these professionals work on training and deploying the AI models.

Backend Developer- As a backend developer, you will have to develop the server-side logic of web applications. Enroll in the Python Course Online to learn the skills to become a backend developer. Furthermore, these also handle database interactions and implement business logic.

Cloud Architect (Python-focused)- These professionals are responsible for designing and implementing cloud-based solutions using Python. Along with this, these professionals have to architect scalable and secure cloud infrastructure. Moreover, they also integrate cloud services with existing systems.

Python Tester/QA Engineer- They have to ensure the quality of Python applications through testing. Furthermore, these professionals also write and execute test cases and also identify and report defects. Moreover, they also have to perform automation testing.

Game Developer (Python-based)- These professionals have to develop games using Python-based game engines. Furthermore, they have to design game mechanics and create game graphics and sound. Along with this, they have to implement the game logic.

Bioinformatics Engineer- These professionals also have to apply computational techniques to biological data. Furthermore, they have to analyze genomic data and work on developing algorithms for biological problems. Along with this, they also use Python libraries like Biopython.

Quantitative - These professionals have to use quantitative methods to analyze financial data. Along with this, they have to develop financial models and perform risk analysis. Furthermore, they use Python for data analysis and modelling.

4,00,000 - 10,00,000 per annum (entry to mid-level)

6,00,000 - 20,00,000 per annum (depending on experience)

6,00,000 - 18,00,000 per annum (mid-level to senior roles)

3,50,000 - 8,00,000 per annum (depending on experience)

4,00,000 - 12,00,000 per annum (entry to mid-level)

3,50,000 - 9,00,000 per annum (depending on experience)

5,00,000 - 12,00,000 per annum (mid-level to senior roles)

6,00,000 - 15,00,000 per annum (depending on skillset and experience)

7,00,000 - 20,00,000 per annum (based on seniority and specialization)

4,00,000 - 12,00,000 per annum (depending on the level of experience)

15,00,000 - 30,00,000 per annum (senior-level)

4,00,000 - 10,00,000 per annum (depending on the experience)

5,00,000 - 12,00,000 per annum (depending on experience and industry)

5,00,000 - 12,00,000 per annum (varies depending on research or industry roles)

10,00,000 - 25,00,000 per annum (senior-level roles can earn much more depending on expertise)

You must know how to use server-side logic.

You must know how to build software for asset management.

A hands-on knowledge of writing and implementing software solutions to integrate different systems.

You must know how to write testable and usable code that can be used again.

You must know how to support new projects and implement solutions for the same.

You should know about integrating data storage solutions.

Know how to implement data security and protection.

PCEP (Certified Entry-Level Python Programmer)- This is a popular introductory certification and gaining it validates your basic understanding of Python programming concepts. Along with it, this certification covers topics like data types, variables, control flow, functions, and modules.

PCAP (Certified Associate in Python Programming)- Gaining this credential demonstrates your proficiency in Python programming. Aong with this, it also showcases your ability to write and debug Python code. Furthermore, this certification covers various topics like object-oriented programming, file I/O, and exception handling.

PCPP-32-1- It stands for certified professional in Python programming and this certification focuses on intermediate-level Python programming skills. Along with this, it covers topics like regular expressions, modules, and packages.

PCPP-32-2- This credential is Certified Professional in Python Programming 2. Gaining this credential helps in validating your advanced Python programming skills. Along with this, it also covers your various topics such as database interaction, web development, and data analysis.

CEPP- This stand for Certified Expert in Python Programming and this is one of the highest levels of Python certification. Gaining this credential showcases your mastery of the language and its applications. Along with this, it validates you have a combination of technical skills and practical experience.

PCAT- This credential refers to Certified Associate in Testing with Python and it focuses on using Python language for testing and quality assurance. Along with this, it also covers topics like unit testing, integration testing, and automation testing.

PCAD- It refers to the Certified Associate in Data Analytics with Python. Gaining this credential showcases your skills in using Python for data analysis. Along with this, it also covers various topics like data cleaning, data visualization, and statistical analysis.

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