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Week - 1
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Introduction to Programming: What is an algorithm, why is it important, explanation with real-life examples. Installing Python. Preparing the work environment. Simple “Hello Python” example. |
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Week - 2
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Basic Data Types and Operators: Introduction to int, float, string, and boolean data types. Type conversions. Explanation of arithmetic, comparison, and logical operators with practical examples. |
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Week - 3
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Conditional Statements: Establishing the program's decision-making mechanism with if-else structures. Complex conditions using comparison and logical operators. Nested conditions. Mini applications. |
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Week - 4
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Loops I: Repeating operations with for and while loops. Example applications. |
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Week - 5
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Loops II: Using range, enumerate, break, and continue. Example applications. |
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Week - 6
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Data Structures: Lists, tuples. Basic operations: insertion, deletion, sorting, slicing. Application areas and performance differences. Dictionaries (dict) and nested data structures. Key-value logic. |
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Week - 7
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General Methods: Mathematical methods and chain methods. Examples of use. |
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Week - 8
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Functions: Function definition, parameters, and return values. Default parameters. Calling by value and by reference. Examples of real-life function applications. |
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Week - 9
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File Operations: Opening/reading/writing files (txt, CSV). Secure file operations using the with statement. Reading and writing data from CSV files. |
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Week - 10
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Errors and Exceptions: try-except structure, multiple except blocks. finally block. Custom error messages. Real-life example: Catching errors when a file cannot be found. |
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Week - 11
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Introduction to Object-Oriented Programming: The concepts of class and object. Method, property, inheritance. Encapsulation and its advantages. Small example: Defining a Student class. |
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Week - 12
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Modules and Libraries: Standard libraries (math, random, datetime). Package management with pip. Third-party library installation and simple usage examples. |
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Week - 13
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Python Libraries for Data Analysis: Arrays and basic operations with NumPy. Creating DataFrames, reading and filtering data with Pandas. Simple graphing with Matplotlib. |
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Week - 14
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A Small Project Data Analysis: Reading, cleaning, analyzing, and presenting data with graphs on a real data set (e.g., traffic or weather data). |
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