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.

Course Curriculum

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.

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 Tuple
  • 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.
  • Introduction to Lambda Functions: Understand the syntax and basic usage of anonymous functions using the lambda keyword in Python. Learn the differences between lambda functions and regular functions.
  • Use Cases and Applications: Explore practical examples and scenarios where lambda functions are most beneficial, such as sorting, filtering, and combining data using functions like map(), filter(), and reduce().
  • Advanced Techniques: Delve into more complex uses of lambda functions in combination with other functional programming tools in Python. Learn how to use lambda functions for concise and efficient code in list comprehensions, generator expressions, and within higher-order functions.
  • 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.

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.

  • 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.
  • 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.

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.

  • 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

Module 1: Introduction to Version Control and Git

  • Understanding Version Control Systems: Learn the basics and importance of version control.
  • Installing Git: Step-by-step guide to installing Git on various operating systems.
  • Basic Git Commands: Introduction to essential Git commands (git init, git add, git commit, git status).

Module 2: Working with Local Repositories

  • Creating and Managing Repositories: How to create a new repository and manage existing ones.
  • Tracking Changes: Understanding the staging area and committing changes.
  • Viewing Commit History: Using git log and other commands to view the commit history.
  • Undoing Changes: Techniques for undoing changes using git reset, git checkout, and git revert.

Module 3: Branching and Merging

  • Understanding Branches: Importance of branches in workflow and how to create and manage them.
  • Merging Branches: Different strategies for merging branches and resolving conflicts.
  • Best Practices for Branching: Tips and strategies for effective branching in Python projects.

Module 4: Introduction to GitHub

  • Setting Up a GitHub Account: Creating and configuring a GitHub account.
  • Creating and Managing Repositories on GitHub: How to create repositories and manage settings.
  • Cloning and Forking Repositories: Difference between cloning and forking, and how to perform each.

Module 5: Collaborating with GitHub

  • Using GitHub Issues: How to create and manage issues to track bugs and enhancements.
  • Pull Requests: Understanding and creating pull requests for collaborative work.
  • Code Reviews and Feedback: Best practices for code reviews and providing feedback on pull requests.

Module 6: Advanced Git and GitHub Topics

  • GitHub Actions and Automation: Introduction to GitHub Actions for CI/CD workflows.
  • Using Git Hooks: Automating tasks with Git hooks.
  • Rebasing and Advanced Merging: Understanding rebasing and advanced merging techniques.

Module 7: Integrating GitHub with Python Projects

  • Using .gitignore for Python Projects: Best practices for using .gitignore in Python projects.
  • Managing Dependencies: How to manage dependencies and virtual environments with Git and GitHub.
  • Continuous Integration with Python: Setting up CI workflows for Python projects using GitHub Actions.

Connecting to a MySQL database with Python involves using specific libraries to establish and manage the database connection. Python offers multiple libraries for MySQL connectivity, such as mysql-connector-python and PyMySQL. These libraries enable Python applications to interact with MySQL databases, allowing you to perform a variety of database operations such as querying data, inserting new records, updating existing records, and deleting records. The process typically begins with establishing a connection to the MySQL server using appropriate connection parameters like host, user, password, and database name. Once connected, you can create a cursor object to execute SQL commands and fetch results. Effective error handling and transaction management are essential to ensure data integrity and smooth operation. Using advanced techniques like prepared statements and stored procedures can further enhance the security and efficiency of database interactions. By mastering MySQL database connectivity in Python, developers can build robust and scalable applications that leverage the power of relational databases.

The Python course includes a series of projects and weekly assessments designed to reinforce learning and ensure a thorough understanding of Python programming. Each week, students undertake a project that builds on the skills introduced in the lessons. Starting with a simple calculator in the first week, students will grasp basic syntax, functions, user input/output, and control flow, and will be assessed through a quiz on these fundamentals. In the second week, the focus shifts to data structures with a contact management system project, utilizing lists and dictionaries, followed by coding exercises on these topics. The third week’s project is a to-do list application, emphasizing functions, modular programming, and error handling, with an assessment on these concepts. The fourth week delves into object-oriented programming (OOP) with a library management system project, covering classes, objects, inheritance, and encapsulation, and includes exercises to test OOP knowledge. These projects and assessments are designed to provide practical experience and validate the skills learned each week.

Internship Program

This internship is a part of the course curriculum to help you gain real experience in the Data Science domain.During this internship, you will go through various challenges which you allow to explore new skills and push your limits while learning something new during the projects.

Topics Covered :

Integration with python & SQL

Web Scrapping

Data Cleaning with Python

Use Cases

Git / Github Integration

End to End Projects

Interview Preparation

Datadrix offers top-notch placement opportunities. With strong industry ties and modern training, we excel in placing our candidates. Our results speak to our commitment to shaping successful careers. Our approach ensures to open pathway for learners to achieve good growth in the domain

Activities Covered :

Interview Pattern Preparation

Mock Interview Practice Sessions

Placement Ready Session for Working Professionals

Preparation as per Job Description

Technical Screening for technical strengthening

Screening for effective communication check

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Duration

Our 80+ hour Python course provides in-depth training and hands-on experience, covering basic to advanced programming, and preparing you for success in the tech-driven world.

Weekly Assesments

Weekly assessments, including quizzes and coding exercises, reinforce Python concepts and ensure comprehensive readiness for advanced programming topics.

In-depth Explaination

Python training covers core and advanced concepts through hands-on projects, ensuring a thorough understanding of Python’s capabilities and best practices.

Doubt Session

Doubt sessions offer students personalized guidance to address questions and challenges, ensuring a clear understanding of the course material and concepts.

Certification

Upon course completion, participants receive a certification that acknowledges their Python proficiency and understanding of core concepts and practical skills.

An Awesome Community

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

Meet our team and students

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

“Validate Your Expertise and Propel Your Career”

  • 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.
  • Certification: A testament to your skills and knowledge, certifications demonstrate your proficiency in specific areas of expertise, giving you a competitive edge in the job market.
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Deepak Chahal
Devansh Goel
Ishty Malhotra
Taneesha Agrawal
Mayank Rana
Shubham Naithani
Kartik Sharma
Upvansh Thakur
Naveen Negi
Amita Dwivedi
Abhinav Bishnoi
Himanshu Pal
Jay Verma
Neha Sharma
Rishabh Upadhyay

Key Features & Benefits

  • Practice problems of varying difficulty
  • Over more than 5000+ Questions
  • 1:1 Expert Doubt support
  • Mock interviews with career guidance
  • 12 + Years of exprienced Faculty
  • Deep Explaination of coding
  • Practical & Project Based Learning
  • Structured feedback to make you better
  • Resume Profile Building
  • Offline / Online Modes
  • Interview Preparation
  • Production Workflow
  • Secure Certification
  • Git Github Integration
  • 24/7 Support Team
  • Projects from the scratch

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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