*** PYTHON PROGRAMMING ***
>>> Basic to Professional (Data Analysis & Visualization Track) <<<
*** Industry-Ready | Job-Oriented | Comprehensive | Practical | Industry-Oriented ***
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*** MODULE 1: Python Programming Fundamentals
>> Introduction to Programming Concepts
>> Why Python? (Industry Demand & Career Scope)
>> Python Features & Advantages
>> Python Applications (IT, Data, Automation, AI, Web)
>> Installing Python Environment
>> Python Interpreter
>> Anaconda Distribution
>> Jupyter Notebook
>> VS Code
>> Python Program Structure
>> Python Syntax Rules
>> Indentation & Code Blocks
>> Comments (Single-line & Multi-line)
** Hands-on: **
Writing first Python program, basic input-output programs
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*** MODULE 2: Variables, Data Types & Operators ***
>> Variables & Memory
>> Variable declaration & assignment
>> Dynamic typing
>> Multiple assignment
>> Type casting
** Built-in Data Types
>> Numeric types (int, float, complex)
>> Boolean
>> String
>> None
** Operators **
>> Arithmetic operators
>> Relational operators
>> Logical operators
>> Assignment operators
>> Bitwise operators
>> Membership operators
>> Identity operators
** Hands-on:
*** Salary calculation, percentage system, logical problem solving
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*** MODULE 3: Control Flow & Looping Statements ***
** Decision Making
>> if statement
>> if–else
>> if–elif–else
>> Nested conditional statements
** Looping
>> for loop
>> while loop
>> Loop with else
>> break statement
>> continue statement
>> pass statement
** Logical Programs
>> Prime numbers
>> Armstrong number
>> Palindrome
>> Fibonacci series
>> Pattern printing
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*** MODULE 4: Python Core Data Structures (In-Depth) ***
** Strings
>> String creation
>> Indexing & slicing
>> String immutability
>> String methods
>> String formatting (format, f-string)
** Lists
>> Creating lists
>> Indexing & slicing
>> List methods
>> Nested lists
>> List comprehension
** Tuples
>> Tuple creation
>> Tuple operations
>> Tuple packing & unpacking
** Sets
>> Set creation
>> Set methods
>> Set operations (union, intersection, difference)
** Dictionaries
>> Dictionary structure
>> Accessing keys & values
>> Dictionary methods
>> Nested dictionaries
--- Hands-on:
>> Student database, shopping cart, inventory management
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*** MODULE 5: Functions & Modular Programming ***
>> Built-in functions
>> User-defined functions
>> Function declaration & calling
>> Function arguments
>> Positional arguments
>> Keyword arguments
>> Default arguments
>> *args & **kwargs
>> Return statement
>> Lambda (anonymous) functions
>> Recursion
>> Scope of variables (local, global)
>> Creating & using modules
>> Packages & imports
** Hands-on:
>> Menu-driven applications, reusable program modules
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*** MODULE 6: File Handling & Exception Handling ***
** File Handling
>> File object & file modes
>> Reading from text files
>> Writing & appending files
>> Working with CSV files
>> File pointer operations
>> OS & sys module basics
** Exception Handling
>> Types of errors
>> Exception handling flow
>> try, except, else, finally
>> Handling multiple exceptions
>> User-defined exceptions
** Hands-on:
>> File-based student & employee record system
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*** MODULE 7: Object-Oriented Programming (OOPs) ***
>> Object-Oriented concepts
>> Class & Object
>> Constructor (init)
>> Instance variables & methods
>> Class variables & methods
>> Inheritance
>> Single
>> Multilevel
>> Multiple
>> Polymorphism
>> Encapsulation
>> Abstraction
>> Method overriding
** Hands-on:
>> OOP-based management system project
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**** DATA ANALYSIS & VISUALIZATION (PRO LEVEL) ****
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*** MODULE 8: NumPy - Numerical Computing (Detailed) ***
>> Introduction to NumPy
>> NumPy array vs Python list
>> Creating arrays
>> 1D, 2D, 3D,4D,5D arrays
>> Array attributes (shape, size, dtype)
>> Reshaping arrays
>> Indexing & slicing
>> Mathematical operations
>> Statistical functions
>> Broadcasting
>> Universal functions (ufuncs)
>> Random number generation
>> Linear algebra basics
*** Hands-on:
>> Numerical data analysis & performance testing
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*** MODULE 9: Pandas – Data Analysis (Detailed) ***
** * Pandas Basics
>> Pandas Series
>> Pandas DataFrame
>> Reading data (CSV, Excel, SQL)
>> Inspecting datasets
*** Data Cleaning
>> Handling missing values
>> Data type conversion
>> Renaming columns
>> Removing duplicates
*** Data Manipulation
>> Indexing & slicing
>> Filtering data
>> Sorting
>> GroupBy operations
>> Aggregation functions
*** Data Integration
>> Merge
>> Join
>> Concatenate
>> Time Series Basics
>> Date & time handling
*** Hands-on:
>> Sales analysis, student performance analysis, real datasets
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*** MODULE 10: Matplotlib - Data Visualization (Detailed) ***
>> Introduction to Matplotlib
>> Plot structure & figure
>> Line plots
>> Bar charts
>> Histograms
>> Scatter plots
>> Pie charts
>> Subplots
>> Titles, labels & legends
>> Grid & customization
** Hands-on:
>> Visual analysis reports & charts
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*** MODULE 11: Seaborn - Advanced Visualization ***
>> Introduction to Seaborn
>> Seaborn vs Matplotlib
>> Relational plots
>> Categorical plots
>> Distribution plots
>> Heatmaps
>> Pair plots
>> Statistical visualization
** Hands-on:
>> Professional data dashboards & insight reports
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*** MODULE 12: Python with Database ***
>> Introduction to Databases
>> SQL basics for Python users
>> Connecting Python with MySQL / SQLite
>> CRUD operations
>> Fetching data for analysis
>> Database-driven projects
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*** MODULE 13: Projects, Certification & Career Preparation ***
** Projects
>> Student Management System
>> End-to-End Data Analysis Project
>> Data collection
>> Data cleaning
>> Analysis using Pandas
>> Visualization using Matplotlib & Seaborn
>> Final report
*** Career Support ***
>> Resume preparation
>> Git & GitHub basics
>> Interview questions (Python & Data Analysis)
>> Mock interviews
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*** COURSE OUTCOMES ***
** After completion, students can:
>> Write professional Python programs
>> Perform real-world data analysis
>> Create data visualizations & reports
>> Work with databases
>> Apply for Python Developer / Data Analyst roles
