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Python

Image by Brecht Corbeel

✅ Course Highlights

  • 100% Practical Training

  • Job-Oriented Curriculum

  • Interview Questions

  • Beginner to Advanced Coverage
     

Duration: 45 Days
contact :9491882804

Module 1: Introduction & Setup
 

  • Course overview & common student confusions

  • Installing Python

  • Installing PyCharm IDE

  • PyCharm basics (interface, theme, font settings)

  • Introduction to Jupyter Notebook

  • Installing Anaconda / Jupyter Notebook setup

  • Jupyter Notebook interface & features

  • Creating and running first notebook (.ipynb)

  • Markdown vs Code cells

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Module 2: Python Fundamentals
 

  • Variables and their usage

  • Data types in Python

  • Naming rules for variables

  • Type conversion (constructors)

  • Print function (complete understanding)
     

Module 3: Strings & Operations
 

  • String basics & accessing values

  • Slicing and multiple value access

  • Reverse & modified reverse methods

  • Split method

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Module 4: Operators & Input
 

  • Arithmetic operators

  • Assignment operators

  • Comparison operators

  • Logical operators

  • BODMAS rule (operator precedence)

  • Input function (deep dive)

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Module 5: Conditional Statements
 

  • IF statements

  • IF–ELIF ladder

  • Nested IF conditions

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Module 6: Loops (Core Programming)
 

  • For loops

  • While loops

  • Nested loops
     

​Module 7: Lists (Data Handling Core)

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  • List introduction

  • Modify, add, append values

  • Remove/move elements

  • Looping through lists

  • Sorting, join, reverse operations

Python

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Module 8: Tuples

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  • Tuple basics

  • Add/change values (concept clarity)

  • Looping in tuples

  • Index & count methods

  • List vs Tuple differences

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Module 9: Error Handling & Functions

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  • Error handling (try-except)

  • User-defined functions

  • Types of functions

  • Passing multiple parameters

  • Variable scope (local vs global)

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Module 10: Dictionaries

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  • Dictionary introduction

  • Access keys & values

  • Modify data

  • Remove, delete, clear operations

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Module 11: Sets
 

  • Set introduction

  • Set operations & methods

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Module 12: Introduction to Pandas
 

  • What is Pandas?

  • PIP concept (package installation)

  • Reading CSV files

  • Reading Excel files

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Module 13: DataFrame Operations
 

  • Column extraction (head, tail)

  • Rename columns

  • Drop/delete columns

  • Insert new columns

  • Change data types (astype, to_datetime)

  • Data size optimization techniques
     

Module 14: Advanced Pandas
 

  • Looping in DataFrames

  • Concatenate datasets

  • Merge (lookup operations like SQL joins)

  • Data filtering (between, isin)

  • loc & iloc usage

  • Remove duplicates

  • Handle missing values (dropna, fillna)

  • Update/change values

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Module 15: Data Analysis & Reporting
 

  • Aggregation functions: Sum, Count, Unique

  • nlargest, nsmallest

  • Value counts

  • GroupBy (report generation)

  • Looping in GroupBy
     

Module 16: NumPy
 

  • Arrays & indexing

  • Mathematical operations

Aggregations (sum, mean, min, max)

Module 17: Data Visualization

 

  • Matplotlib (Line, Bar, Pie charts)

  • Seaborn (advanced visualizations)

  • Creating analytical reports

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Module 18 : Python Excel Automation
 

  • OpenPyXL

  • Reading and writing Excel files

  • Working with cells and ranges

  • Formatting Excel worksheets

  • Creating and applying formulas

  • Working with multiple worksheets

  • Combining multiple Excel files

  • Pandas + Excel integration

  • Automated MIS report generation

  • Data cleaning and transformation with Pandas

  • Real-world Excel Automation Project

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Module 19: SQL + Python

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  • Connect Python with databases

  • Execute SQL queries

  • Read SQL data into Pandas

  • Data analysis and reporting
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Module 20: Real-Time Projects

  • Sales Data Analysis

  • HR Analytics

  • Finance Dashboard

  • End-to-End Project Implementation

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