COURSES / TECHNICAL COURSES
Introduction to Python, Control Flow and Functions, Data Structures, File Handling, Modules and Packages, Error Handling, Introduction to Numpy, Data Analysis with Pandas, Data Visualization with Matplotlib, Data Visualization with Seaborn, Exploratory Data Analysis (EDA), Introduction to Machine Learning, Supervised Learning, Unsupervised Learning.
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Topics - Introduction to Python, Control Flow and Functions, Data Structures, File Handling, Modules and Packages, Error Handling, Introduction to Numpy, Data Analysis with Pandas, Data Visualization with Matplotlib, Data Visualization with Seaborn, Exploratory Data Analysis (EDA), Introduction to Machine Learning, Supervised Learning, Unsupervised Learning, Final Project
Course Deatils
Daywise Topics & Activities
MODULE 1 - Introduction to Python Install Python and set up a development environment (IDEs like PyCharm, VSCode, or Jupyter Notebook). Write simple programs to understand variables, data types (integers, floats, strings, lists, tuples, dictionaries).
MODULE 2 - Basic Operations and Control Flow Write programs performing basic arithmetic operations. Create programs using if, elif, and else statements.
MODULE 3 - Loops Write programs using for and while loops to iterate over sequences. Practice using loops to solve problems (e.g., summing numbers, finding the factorial of a number).
MODULE 4 - Functions Write functions for various tasks (e.g., calculating the area of a circle, converting temperatures). Practice calling functions with different arguments.
MODULE 5 - Data Structures - Lists Write programs to create, access, and modify lists. Use list methods to perform operations on lists.
MODULE 6 - Data Structures - Tuples and Sets Write programs using tuples and sets. Perform operations like union, intersection, and difference on sets.
MODULE 7 - Data Structures - Dictionaries Write programs to create and manipulate dictionaries. Use dictionary methods to iterate over keys and values.
MODULE 8 - File Handling Write programs to read and write text files. Handle exceptions related to file operations.
MODULE 9 - Error and Exception Handling Write programs that include error handling. Handle common exceptions (e.g., file not found, division by zero).
MODULE 10 - Modules and Packages Use standard libraries like math, datetime, random. Write programs that utilize these libraries.
MODULE 11 - Comprehensions Write programs using list comprehensions to create and manipulate lists. Practice dictionary comprehensions for various tasks.
MODULE 12 - Object-Oriented Programming (OOP) Basics Define simple classes and create objects. Write methods and access object attributes.
MODULE 13 - Advanced OOP Concepts Create a class hierarchy using inheritance. Override methods in derived classes.
MODULE 14 - Working with Libraries Install and use Numpy for array manipulations. Use Pandas for basic data manipulation and analysis.
MODULE 15 - Final Project Choose a project that integrates the concepts learned. Possible projects: Simple text-based game, Data analysis project using a dataset, Basic web scraper. Present findings with a summary report or documentation.
Introduction Lesson
View DetailsBasics of HTML
View DetailsGetting Know about HTML
View DetailsTags and Attributes
View DetailsBasics of CSS
View DetailsGetting Familiar with CSS
View DetailsIntroduction to Bootstrap
View DetailsResponsive Design
View DetailsCanvas in HTML 5
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Python Topics - Introduction to Python, Basic Operations and Control Flow, Loops, Functions, Data Structures - Lists, Data Structures - Tuples and Sets, Data Structures - Dictionaries, File Handling, Error and Exception Handling, Modules and Packages Comprehensions, Object-Oriented Programming (OOP) Basics, Advanced OOP Concepts, Working with Libraries, Final Project
Introduction to Python, Control Flow and Functions, Data Structures, File Handling, Modules and Packages, Error Handling, Introduction to Numpy, Data Analysis with Pandas, Data Visualization with Matplotlib, Data Visualization with Seaborn, Exploratory Data Analysis (EDA), Introduction to Machine Learning, Supervised Learning, Unsupervised Learning.
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