DATADRIX : Python Programming

Unlocking the Power of Programming with Python

Discover the boundless potential of Best Python Training in delhi through our comprehensive course. Python, known for its simplicity and versatility, is a programming language that opens doors to endless possibilities. In our program, you’ll embark on a journey to grasp the fundamental concepts of Python, equipping yourself with a solid foundation. With hands-on projects and real-world applications, you’ll develop practical skills that empower you to create solutions for a wide range of challenges. Whether you’re a novice or experienced programmer, our course is designed to accommodate learners at all levels. Python’s elegant syntax and vast library ecosystem make it a powerful tool for web development, data analysis, artificial intelligence, and more.

  • Over more than 5000+ Questions
  • 1:1 Expert Doubt support
  • Resume Profile Building
  • Deep Explaination of coding
  • Practice problems of varying difficulty
  • Practical & Project Based Learning
  • Structured feedback to make you better
  • Mock interviews with career guidance
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Python Programming

Building the Future of Technology From Scratch

Our Best Python Training Course in Delhi is designed to provide participants with a comprehensive understanding of Python programming language and its applications. Whether you are a beginner or have some programming experience, this course will equip you with the knowledge and skills needed to develop Python-based solutions. By the end of the Python training course, you will have a solid foundation in Python programming, allowing you to develop scripts, build applications, and explore specialized domains like web development, data analysis, and more. Join us on this learning journey and unlock the full potential of Python programming for your career advancement.

Python, a programming language renowned for its simplicity and power, serves as an ideal launchpad for aspiring developers and a versatile tool for seasoned professionals in the ever-evolving landscape of technology. At DATADRIX, Best training in institute in delhi for python programming we offer a nurturing environment for both freshers and experienced students to embark on an exciting journey with Python.

Embark on your Python journey with the best Python training in Delhi, offered in our first module. Delve into the history of Python, installation procedures, configurations, and foundational Python programming basics. Our training adheres to international industry standards, covering the latest trends and techniques. Datadrix provides an unparalleled IT infrastructure and a conducive learning environment. Enroll in our courses to become market-ready and excel in Python coding. Datadrix, your gateway to the best Python training in Delhi, welcomes you to a transformative learning experience.

Datadrix stands out as the unequivocal leader, securing its position as the best Python Training Institute in Delhi. With over 10 years of industry experience and expertise in IT and IoT, Datadrix is at the forefront of the latest industry trends. What sets Datadrix apart is the practical approach to learning, facilitated by industry professionals and veterans with extensive experience as trainers. Our flexible and adaptive curriculum ensures alignment with the ever-changing needs of the industry, making Datadrix the ultimate destination for the best Python training in Delhi.

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Course Curriculam :

A syllabus is a meticulously crafted document that serves as a comprehensive roadmap for the training program. It plays a pivotal role in guiding candidate along their learning journey, offering a structured framework for acquiring knowledge and honing skills.

A well-designed syllabus offers a bird’s-eye view of the course’s content, complete with a meticulous breakdown of modules or units, well-crafted lesson plans, and a timeline for completion. It doesn’t merely outline what candidate will learn but also lays out the precise learning outcomes and objectives that they’re expected to master by the course’s conclusion.

Embedded within the syllabus are detailed descriptions of each topic, accompanied by a treasure trove of learning resources, suggested readings, and insights into the methods by which knowledge will be assessed. It’s not just a map, it’s a compass guiding candidate through a labyrinth of assignments, projects, exams, and a transparent grading system, making their educational journey as informed and rewarding as possible.

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Module 1

Data types are fundamental categories used to classify and represent different types of data, such as numbers, text, and more. Understanding Python data types is essential for effectively working with data and variables in your programs.

Variables are fundamental elements in Python and other programming languages that are used to store, manipulate, and manage data.

  • if Statement: The if statement in Python is used for conditional execution. It allows you to execute a block of code if a specified condition is true. If the condition is false, the code block is skipped.
  • else Statement: The else statement is often used in conjunction with an if statement. It provides an alternative block of code to execute when the if condition is false.
  • elif Statement: The elif (short for “else if”) statement allows you to test multiple conditions sequentially. It is used when you have multiple conditions to check and want to execute a specific block of code based on the first true condition encountered.

A for loop in Python is a control flow statement that is used for iterating over a sequence (such as a list, tuple, string, or range) or other iterable objects. It allows you to perform a block of code repeatedly for each item in the sequence.

A While Loop in Python is a control flow statement used to repeatedly execute a block of code as long as a specified condition remains true. The loop continues iterating as long as the condition is evaluated to True

Python Nested Loops involve the use of one or more loops inside another loop. Nested loops are a fundamental concept in programming and are used to iterate over multiple sets of data or create complex patterns and structures.

Python Pattern Programming involves creating various patterns or shapes using loops and print statements in Python. These patterns can be simple or intricate and serve both educational and aesthetic purposes

Break and continue are two control flow statements used in loops to alter the program’s execution. They are typically used within loops like for and while control the flow of the loop.

  • Introduction to Lists
  • List Operations
  • Iterating Through Lists
  • List Methods
  • List Comprehensions
  • List Manipulation
  • Introduction to Dictionaries
  • Dictionary Operations
  • Iterating Through Dictionaries
  • Dictionary Methods
  • Nested Dictionaries
  • Advanced Dictionary Topics
  • Introduction to Tuples
  • Tuple Operations
  • Tuple Methods
  • Iterating through Tuples
  • Tuple Packing and Unpacking
  • Immutability of Tuples
  • Introduction to Booleans
  • Boolean Operators
  • Comparison Operators
  • Conditional Statements
  • Boolean Expressions
  • Boolean Expressions
  • Conditional Loops

Sets are a built-in data type in Python used to store unique and unordered collections of items. Sets are mutable, and you can add or remove elements after creating them. Frozensets, on the other hand, are similar but are immutable, meaning you cannot change their elements once they are created.

Module 2

Introduction to functions, Working of function calling, Variables and its scope, Pass by value

Python functions are reusable code blocks defined with def, facilitating modularity and reusability.

File handling in Python involves reading and writing files using functions like open(), essential for data manipulation and persistence.

Exception handling in Python involves using try, except, else, and finally blocks to gracefully handle errors and exceptions that may occur during program execution. It allows you to catch and manage unexpected situations, preventing program crashes.

Threading in Python refers to the ability to run multiple threads (smaller units of a program) concurrently within a single process. It’s used for parallelism and can improve program performance for tasks like multitasking, I/O-bound operations, and concurrent processing. Python’s threading module provides the tools for working with threads in a high-level, user-friendly way.

Decorator in Python is a special type of function that can be used to modify or enhance the behavior of other functions or methods. Decorators are often used to add functionalities such as logging, authentication, or access control to existing functions without modifying their source code, making code more modular and maintainable.

Generators in Python are a way to create iterators, which allow you to iterate over a potentially large sequence of items without storing them all in memory at once. They are defined using functions with the yield keyword, which suspends the function’s state and allows it to resume where it left off when iterated. This makes generators useful for efficient processing of large datasets and for implementing lazy evaluation.

Searching in Python refers to the process of finding specific elements or patterns within data structures, such as lists, strings, or files. It is a fundamental operation for many applications, including data analysis, text processing, and information retrieval. Python offers several methods and techniques for searching, depending on the type of data and the nature of the search.

Sorting is a fundamental operation in programming that arranges a collection of items in a specific order, such as ascending or descending. In Python, there are various methods and functions available for sorting different data structures, primarily lists.

Module 3

Object-Oriented Programming (OOP) in Python involves organising code into objects, which are instances of classes. Here are the key points for Python OOP

Decorator in Python is a special type of function that can be used to modify or enhance the behavior of other functions or methods. Decorators are often used to add functionalities such as logging, authentication, or access control to existing functions without modifying their source code, making code more modular and maintainable.

Generators in Python are a way to create iterators, which allow you to iterate over a potentially large sequence of items without storing them all in memory at once. They are defined using functions with the yield keyword, which suspends the function’s state and allows it to resume where it left off when iterated. This makes generators useful for efficient processing of large datasets and for implementing lazy evaluation.

Generators in Python are a way to create iterators, which allow you to iterate over a potentially large sequence of items without storing them all in memory at once. They are defined using functions with the yield keyword, which suspends the function’s state and allows it to resume where it left off when iterated. This makes generators useful for efficient processing of large datasets and for implementing lazy evaluation.

Generators in Python are a way to create iterators, which allow you to iterate over a potentially large sequence of items without storing them all in memory at once. They are defined using functions with the yield keyword, which suspends the function’s state and allows it to resume where it left off when iterated. This makes generators useful for efficient processing of large datasets and for implementing lazy evaluation.

Generators in Python are a way to create iterators, which allow you to iterate over a potentially large sequence of items without storing them all in memory at once. They are defined using functions with the yield keyword, which suspends the function’s state and allows it to resume where it left off when iterated. This makes generators useful for efficient processing of large datasets and for implementing lazy evaluation.

Generators in Python are a way to create iterators, which allow you to iterate over a potentially large sequence of items without storing them all in memory at once. They are defined using functions with the yield keyword, which suspends the function’s state and allows it to resume where it left off when iterated. This makes generators useful for efficient processing of large datasets and for implementing lazy evaluation.

Generators in Python are a way to create iterators, which allow you to iterate over a potentially large sequence of items without storing them all in memory at once. They are defined using functions with the yield keyword, which suspends the function’s state and allows it to resume where it left off when iterated. This makes generators useful for efficient processing of large datasets and for implementing lazy evaluation.

Python’s OOP features enable developers to structure and manage complex programs more efficiently, facilitating code reusability, maintainability, and scalability.

  • Data Manipulation: Pandas is a powerful data manipulation library that provides data structures like DataFrames and Series, which allow you to efficiently store, manipulate, and analyze structured data.
  • Data Cleaning and Preprocessing: It offers a wide range of functions for cleaning and preprocessing data, including handling missing values, reshaping data, and merging datasets.
  • Data Analysis: Pandas simplifies data analysis tasks such as data aggregation, filtering, grouping, and statistical analysis, making it a go-to choice for data scientists and analysts.
  • Integration with Data Sources: You can easily read and write data from/to various file formats like CSV, Excel, SQL databases, and more, as well as fetch data from the web using pandas.
  • Wide Adoption: Pandas is widely adopted in the data science and analysis communities due to its flexibility and ease of use, making it a fundamental tool for data-related tasks in Python.
  • Multidimensional Arrays: NumPy provides ndarray (n-dimensional array) objects, which are more efficient and convenient than Python lists for handling large datasets and performing element-wise operations.
  • Mathematical Functions: It offers a wide range of mathematical functions and operations, including arithmetic, statistical, and linear algebra functions, making it a go-to library for scientific computing.
  • Broadcasting: NumPy allows for element-wise operations between arrays of different shapes and sizes through broadcasting, making it easier to work with data of varying dimensions.
  • Memory Efficiency: NumPy arrays are memory-efficient and provide better performance compared to traditional Python lists, as they allow for vectorized operations that take advantage of optimized C and Fortran code.
  • Integration with Data Science: NumPy is a foundational library in the Python data science ecosystem and is commonly used alongside libraries like Pandas, SciPy, and Matplotlib for tasks like data manipulation, analysis, and visualization.

Flask is a lightweight and popular web framework for building web applications in Python.

  • Microframework: Flask is a microframework, which means it provides only the essential tools and components for web development. This allows developers to choose and integrate additional libraries and components as needed, giving them flexibility in building web applications.
  • Routing: Flask uses simple and intuitive routing mechanisms, allowing you to map URLs to specific functions (view functions) in your code. This makes defining and managing routes straightforward.
  • Extensible: Flask is highly extensible and has a rich ecosystem of extensions that provide additional functionality, such as authentication, database integration (e.g., SQLAlchemy), form handling, and more.
  • Jinja2 Templating: Flask uses the Jinja2 templating engine, which allows you to easily generate dynamic HTML and other content by embedding Python code within HTML templates. This separation of concerns makes it easier to manage front-end and back-end logic.
  • Werkzeug Integration: Flask is built on top of the Werkzeug WSGI library, which provides a solid foundation for handling HTTP requests and responses. This integration ensures reliable and efficient web application development.

These points highlight Flask’s simplicity, flexibility, and ease of use, making it a popular choice for building web applications, APIs, and prototypes in Python.

Module 4

  • Data Manipulation: Pandas is a powerful data manipulation library that provides data structures like DataFrames and Series, which allow you to efficiently store, manipulate, and analyze structured data.
  • Data Cleaning and Preprocessing: It offers a wide range of functions for cleaning and preprocessing data, including handling missing values, reshaping data, and merging datasets.
  • Data Analysis: Pandas simplifies data analysis tasks such as data aggregation, filtering, grouping, and statistical analysis, making it a go-to choice for data scientists and analysts.
  • Integration with Data Sources: You can easily read and write data from/to various file formats like CSV, Excel, SQL databases, and more, as well as fetch data from the web using pandas.
  • Wide Adoption: Pandas is widely adopted in the data science and analysis communities due to its flexibility and ease of use, making it a fundamental tool for data-related tasks in Python.
  • Multidimensional Arrays: NumPy provides ndarray (n-dimensional array) objects, which are more efficient and convenient than Python lists for handling large datasets and performing element-wise operations.
  • Mathematical Functions: It offers a wide range of mathematical functions and operations, including arithmetic, statistical, and linear algebra functions, making it a go-to library for scientific computing.
  • Broadcasting: NumPy allows for element-wise operations between arrays of different shapes and sizes through broadcasting, making it easier to work with data of varying dimensions.
  • Memory Efficiency: NumPy arrays are memory-efficient and provide better performance compared to traditional Python lists, as they allow for vectorized operations that take advantage of optimized C and Fortran code.
  • Integration with Data Science: NumPy is a foundational library in the Python data science ecosystem and is commonly used alongside libraries like Pandas, SciPy, and Matplotlib for tasks like data manipulation, analysis, and visualization.

Flask is a lightweight and popular web framework for building web applications in Python.

  • Microframework: Flask is a microframework, which means it provides only the essential tools and components for web development. This allows developers to choose and integrate additional libraries and components as needed, giving them flexibility in building web applications.
  • Routing: Flask uses simple and intuitive routing mechanisms, allowing you to map URLs to specific functions (view functions) in your code. This makes defining and managing routes straightforward.
  • Extensible: Flask is highly extensible and has a rich ecosystem of extensions that provide additional functionality, such as authentication, database integration (e.g., SQLAlchemy), form handling, and more.
  • Jinja2 Templating: Flask uses the Jinja2 templating engine, which allows you to easily generate dynamic HTML and other content by embedding Python code within HTML templates. This separation of concerns makes it easier to manage front-end and back-end logic.
  • Werkzeug Integration: Flask is built on top of the Werkzeug WSGI library, which provides a solid foundation for handling HTTP requests and responses. This integration ensures reliable and efficient web application development.

These points highlight Flask’s simplicity, flexibility, and ease of use, making it a popular choice for building web applications, APIs, and prototypes in Python.

Our Python training course offers a comprehensive journey through the fundamentals and advanced concepts of Python programming. Throughout the course, students will engage in hands-on projects and assessments designed to solidify their understanding and enhance their problem-solving skills. Projects will cover a range of real-world applications, from data analysis and visualization using libraries like Pandas and Matplotlib to web scraping with BeautifulSoup and Selenium. Assessments will include coding challenges, debugging exercises, and the implementation of algorithms, ensuring students can apply their knowledge effectively. By the end of the course, participants will have a robust portfolio of Python projects and a deep understanding of the language, preparing them for a variety of roles in the tech industry.

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Our students, instructors and mentors come from different colleges, companies, and walks of life.

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Joining DATADRIX means you’ll create an amazing network, make new connections,
and leverage diverse opportunities.

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  • Expand Opportunities: Certifications to unlock new career opportunities, gain credibility with employers, and open doors to higher-level positions.
  • Continuous Growth: Certifications not only validate your current skills but also encourage continuous learning and professional development, allowing you to stay updated with the latest industry trends and advancements.
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Frequently Asked Questions

Who can make career transition into Python ?

Graduate or working professional with a passion for technology & desire to broaden their skillset can transition into a Python career.

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What are the key features of Python ?

Offering clear and concise syntax that emphasizes readability. Additionally, its versatile because its library & community support.

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Best Python Training in Delhi – DATADRIX

Python training at the leading institute in Delhi. Hands-on projects & 100% job assistance ensure successful Python journey.

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Duration and scope of python language

Friendly syntax, allows quick proficiency, making it an ideal to learn in a short duration. Vast scope in the tech industry.

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