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Python Training

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Zero to Master Program

Python Training

Python training provides an essential foundation for individuals looking to enter the world of programming and data science. It covers fundamental concepts such as variables, data types, and control structures, enabling learners to write basic scripts and solve problems. More advanced topics include object-oriented programming, libraries, and frameworks, which broaden the scope of what can be achieved with Python. Hands-on exercises and real-world projects help solidify understanding and improve practical skills. The training is suitable for beginners and those with some coding experience, making it accessible and versatile. Python's simplicity and readability make it an ideal choice for learning programming. As Python is widely used in various industries, acquiring proficiency in it opens numerous career opportunities.

Fee

COURSE FEE

₹15,000

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4.8

12K+ Learners enrolled

100 +

Duration(Hours)

350+

Problems

About

Curriculum

Companies

FAQ

This is where you embark on an amazing journey!

Most flexible program in the industry

Freedom to learn

Watch classes any time at your convenience

Cheat days

Catch up on the course when life is calling you elsewhere

Features that keep you going

A structured curriculum that makes learning easy

Practice code problems of varying difficulty

Engagement coach to keep you motivated

Compile & run in an integrated coding environment

Get doubts resolved in 30 mins

1:1 sessions over voice call & chat with our skilled teaching assistants

Industry leading mentors to help you grow

1:1 Mock interviews with resume and career guidance

Structured feedback to make you better

Get a chance to be referred to your mentors’ company

Experience a seamless job switch with hiring assistance

Skill-based hiring across all levels of experience

The results

110%

Average salary hike

7000+

Transitions to product companies

250+

Trusted placement partners

Frequently asked but seldom read questions

Yes, this course is entirely offline, and we offer some courses online as well.

Career Advancement:
Enhance your resume and skill set to increase your employability in various fields such as software development, data analysis, and automation.

Project Development:
Learn how to build and manage projects from scratch, including web applications, scripts for automation, and data processing tools.

Professional Growth:
Transition into roles that require Python programming skills, such as data scientist, machine learning engineer, or software developer.

Academic Progress:
Support academic pursuits with knowledge in Python, which is widely used in research and educational projects.

Personal Development:
Develop problem-solving skills and the ability to create useful tools and applications to simplify everyday tasks.

Submissions are evaluated based on criteria such as correctness, completeness, critical thinking, and adherence to instructions, typically using rubrics or grading guidelines provided by the instructor.
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Meet the faculty legends that will make you legendary

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Praveen Kumar

Founder & Instructor

Praveen has a full stack development experience and professional instructor and trainer for Flutter, Data Science, Machine Learning and Python Programming. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations.

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Lenin Prakash

Co-Founder & Instructor

He is an expert in JavaScript & React (Front-end) and has worked on open-source projects like Firebug and Zulip. He has also served as a GCI (Google Code-In) Mentor with Zulip. In his previous role as a Software Engineer he has worked for Goibibo-MMT.

Course curriculum for the curious

  • -Introduction to Programming
  • -R or Python?
  • -Why Python for Data Science?
  • -Different job roles with Python
  • -Different Python IDEs
  • -Downloading and setting up the Python environment
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  • -Python input and output operations
  • -Comments
  • -Variables, rules for naming variables
  • -Basic Data Types in Python
  • -Typecasting in Python
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  • -Arithmetic operators
  • -Assignment operators
  • -Comparison operators
  • -Logical operators
  • -Identity operators
  • -Membership operators
  • -Bitwise operators
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  • -Creating strings
  • -String formatting
  • -Indexing
  • -Slicing
  • -String methods
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  • -Creating lists
  • -Properties of lists
  • -List indexing
  • -List slicing
  • -List of lists
  • -List methods
  • -Adding, updating & removing elements from lists
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  • -Syntax to create tuples
  • -Tuple properties
  • -Indexing on tuples
  • -Slicing on tuples
  • -Tuple methods
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  • -The syntax for creating sets
  • -Updating sets
  • -Set operations and methods
  • -Difference between sets, lists, and tuples
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  • -The syntax for creating Dictionaries
  • -Storing data in Dictionaries
  • -Dictionaries keys and values
  • -Accessing the elements of Dictionaries
  • -Dictionary methods
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  • -Setting logic with conditional statements
  • -If statements
  • -If -else statements
  • -If-elif-else statements
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  • -Iterating with Python loops
  • -while loop
  • -for loop
  • -range
  • -break
  • -continue
  • -pass
  • -enumerate
  • -zip
  • -assert
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  • -Why List comprehension
  • -The syntax for list comprehension
  • -The syntax for dict comprehension
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  • -What are functions
  • -Modularity and code reusability
  • -Creating functions
  • -Calling functions
  • -Passing arguments
  • -Positional arguments
  • -Keyword arguments
  • -Variable-length arguments (*args)
  • -Variable keyword length arguments (**kargs)
  • -Return keyword in Python
  • -Passing function as an argument
  • -Passing function in return
  • -Global and local variables
  • -Recursion
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  • -Lambda
  • -Lambda with filter
  • -Lambda with map
  • -Lambda with reduce
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  • -Creating and using generators
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  • -Creating modules
  • -Importing functions from a different module
  • -Importing variables from different modules
  • -Python built-in modules
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  • -Creating packages
  • -Importing modules from the package
  • -Different ways of importing modules and packages
  • -Working on Numpy, Pandas, and Matplotlib
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  • -Syntax errors
  • -Logical errors
  • -Handling errors using try, except and finally
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  • -Creating classes & objects
  • -Attributes and methods
  • -Understanding __init__ constructor method
  • -Class and instance attributes
  • -Different types of methods
  • -Instance methods
  • -Class methods
  • -Static methods
  • -Inheritance
  • -Creating child and parent class
  • -Overriding parent methods
  • -The super() function
  • -Understanding types of inheritance
  • -Single inheritance
  • -Multiple inheritance
  • -Multilevel inheritance
  • -Polymorphism
  • -Operator overloading
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  • -Date module
  • -Time module
  • -Datetime module
  • -Time delta
  • -Formatting date and time
  • -strftime()
  • -strptime()
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  • -Understanding the use of regex
  • -re.search()
  • -re.compile()
  • -re.find()
  • -re.split()
  • -re.sub()
  • -Meta characters and their use
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  • -Opening file
  • -Opening different file types
  • -Read, write, close files
  • -Opening files in different modes
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  • -Installing BeautifulSoup
  • -Understanding web structures
  • -Chrome devtools
  • -request
  • -Scraping data from the web using Beautiful Soup
  • -Scraping static websites
  • -Scraping dynamic websites using Beautifulsoup
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  • -Accessing Database using MySQL
  • -Creating tables
  • -Insert Values
  • -Commit changes
  • -Query
  • -Update and Delete
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  • -Accessing Database using MySql
  • -Introduction and Working on Numpy-Multidimensional Arrays
  • -Working on Pandas – EDA Process
  • -Data Visualization
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  • -Public APIs
  • -Accessing data
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  • -Introduction to Python Web Framework Flask
  • -Installing Flask
  • -Working on GET, POST, PUT, METHODS using the Python Flask Framework
  • -Working on Templates, render_template function
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