Accredited & Recognized Excellence

Program Key Benefits

Master Skills: Industry-relevant skills with hands-on training from top corporate professionals.

Eligibility: Open to Any Graduates and individuals with Career Gaps.

Expert Trainers: Led by Mr. Siva • Mr. Mani • Mr. Sharif (15+ Yrs experience), successfully completing 50+ batches.

Program Duration: Comprehensive 2.5 Months training schedule.

Lifetime Access: Class Recorded Videos provided for lifetime review.

Doubt Clarification: Support via Live Classes, WhatsApp, and LMS.

Assessments: Regular Interview Questions and Code Assessments included.

Career Support: Resume Preparation, LinkedIn Profile Creation, and Portfolio Website Development.

Certificates: Free Training Completion Certificate (Soft Copy) provided.

One-on-One Demo: Get Personalized Training & Clarifications before getting started.

Tools & Technologies Covered

Core Python Programming Advanced Python Programming Data Structures and Algorithms (DSA) NumPy Pandas Data Visualization with Matplotlib & Seaborn SQL Generative AI Final Projects and Interview Preparation ChatGPT Gemini Copilot

Complete Course Syllabus

Introduction to Programming and Python
History, Features, Advantages, and Applications of Python
Python vs. Other Programming Languages
Installing Python and Setting Up the Environment
Python Interpreter and Execution Process
Python Interactive Mode and Script Mode
IDEs: IDLE, VS Code, and PyCharm
Writing and Executing the First Python Program

Variables, Keywords, and Identifiers
Naming Conventions and Comments
Literals and Types of Literals
Numeric, String, Boolean, and None Literals
Collection Literals: List, Tuple, Set, and Dictionary
Python Built-in Data Types
Type Checking Using type() and Object Identity Using id()
Type Conversion and Type Casting
Built-in Conversion Functions: int(), float(), str(), bool()
Collection Conversions: list(), tuple(), set(), and dict()
User Input Using input()
Output Using print() and Parameters sep and end
String Formatting: f-strings, format(), and % Formatting
Multiple Assignment and Variable Unpacking
Comments, Indentation, and Basic Python Syntax

Arithmetic Operators
Comparison and Logical Operators
Assignment and Compound Assignment Operators
Bitwise Operators
Membership Operators: in and not in
Identity Operators: is and is not
Difference Between == and is
Operator Precedence and Associativity
Practical Programs Using Python Operators

Introduction to Decision-Making
if Statement
if-else Statement
if-elif-else Statement
Nested if Statements
Conditional Expressions (Ternary Operator)
match-case Statement (Python 3.10+)
Truth Values and Conditions
Real-Time Decision-Making Programs

Introduction to Iteration and Loops
while Loop and for Loop
range() Function and Its Variations
Nested Loops
break, continue, and pass Statements
else with for and while Loops
Loop Control and Infinite Loops
Pattern Programming
Number and Sequence-Based Programs
Real-Time Loop Examples

String Creation and String Literals
Indexing, Negative Indexing, and Slicing
String Immutability and String Operators
String Concatenation and Repetition
String Searching Methods: find(), rfind(), index(), rindex()
Case Conversion Methods: upper(), lower(), title(), capitalize(), swapcase()
String Validation Methods: isalpha(), isdigit(), isalnum(), isspace(), islower(), isupper()
String Cleaning Methods: strip(), lstrip(), rstrip()
String Replacement and Translation Methods
String Splitting and Joining: split(), rsplit(), splitlines(), join()
String Partitioning: partition() and rpartition()
String Prefix and Suffix Checking: startswith() and endswith()
String Formatting: format(), format_map(), and f-strings
String Alignment: center(), ljust(), and rjust()
Raw Strings and Escape Sequences
String Validation, Manipulation, and Processing Programs

Introduction to Python Collections
Lists: Creation, Indexing, Slicing, and Methods
List Operations: append(), extend(), insert(), remove(), pop(), clear()
List Searching and Sorting: index(), count(), sort(), reverse()
List Copying and Nested Lists
List Comprehensions and Conditional Comprehensions
Tuples: Creation, Indexing, Slicing, Packing, and Unpacking
Tuple Methods: count() and index()
Sets: Creation, Uniqueness, and Set Operations
Set Methods and Mathematical Set Operations
Frozensets: Concepts and Operations
Dictionaries: Keys, Values, Items, and Methods
Dictionary Methods: get(), update(), pop(), setdefault(), and items()
Dictionary Iteration and Dictionary Comprehensions
Nested Collections and Collection Unpacking
Mutable vs. Immutable Objects
Collection Conversion and Common Collection Operations

Introduction to Functions and Modular Programming
Defining and Calling Functions Using def
Parameters, Arguments, and Function Calls
Positional, Keyword, and Default Arguments
Variable-Length Arguments: *args and **kwargs
Return Statements and Returning Multiple Values
Local and Global Variables
Variable Scope and the LEGB Rule
Lambda Functions
Recursive Functions and Recursion Examples
map(), filter(), and reduce() Functions
Docstrings and Function Annotations
Function Documentation Using __doc__ and help()
Practical and Real-Time Function Examples

Introduction to Modules and Packages
Built-in and User-Defined Modules
Importing Modules Using import and from...import
Aliases Using as
math Module: Mathematical Functions and Constants
random Module: Random Numbers, choice(), choices(), sample(), and shuffle()
time Module: Time Values, sleep(), and Basic Time Operations
datetime Module: date, time, datetime, and timedelta
Date and Time Formatting, Parsing, Comparison, and Calculations
Creating Custom Modules and Package Structures
__name__ == "__main__" and Script Execution
Installing and Managing Packages Using pip
Reusing Code Through Modules and Packages

Introduction to Errors and Exceptions
Syntax Errors vs. Runtime Exceptions
Common Python Exceptions: ValueError, TypeError, NameError, IndexError, KeyError, ZeroDivisionError
try and except Blocks
Handling Specific and Multiple Exceptions
else and finally Blocks
Raising Exceptions Using raise
Custom Exception Classes: Introduction
Exception Handling in Real-Time Programs

Introduction to File Handling
Opening Files Using open()
File Modes: r, w, a, x, b, and +
Reading Files Using read(), readline(), and readlines()
Writing and Appending Data to Text Files
File Object Methods: write(), writelines(), seek(), and tell()
Using the with Statement for Safe File Handling
CSV File Handling Using the csv Module
JSON File Handling Using the json Module
File and Directory Operations
Handling File-Related Exceptions

Common Built-in Functions: len(), type(), id(), and help()
Numeric Functions: abs(), round(), pow(), sum(), min(), and max()
Sorting and Reversing: sorted() and reversed()
Boolean Testing Functions: any() and all()
Iteration Helpers: enumerate() and zip()
Object and Sequence Functions: isinstance(), range(), and slice()
Practical Examples Using Built-in Functions

Nested Functions and Enclosing Scope
First-Class Functions and Functions as Objects
Passing Functions as Arguments
Higher-Order Functions
Returning Functions from Other Functions
Closures and Variable Scope
Decorators and @ Decorator Syntax
Practical Applications of Function Composition and Reuse

Introduction to Regular Expressions and the re Module
Regex Patterns and Raw Strings
Pattern Matching Using match(), search(), and fullmatch()
Finding Matches Using findall() and finditer()
Text Replacement Using sub() and subn()
Splitting Text Using split()
Special Sequences: \d, \D, \s, \S, \w, and \W
Word Boundaries and Anchors: \b, \B, \A, and \Z
Character Sets, Ranges, and Negation
Quantifiers, Groups, and Alternation
Positive and Negative Lookaheads
Password Validation Using Regular Expressions
Email, Phone Number, and Text Validation Examples

Classes, Objects, and Constructors (__init__)
Instance, Class, and Static Methods
Encapsulation, Inheritance, and Polymorphism
Method Overriding, Abstraction, and Abstract Classes
Magic or Dunder Methods

First-Class Functions and Nested Functions
Closures and Function Decorators
Decorators with Arguments & Multiple Decorators
Practical Logging and Timing Examples

Iterables vs. Iterators (iter() and next())
Creating Custom Iterators
Generator Functions and the yield Keyword
Generator Expressions & Lazy Evaluation

Patterns, Metacharacters, and Character Classes
match(), search(), findall(), and sub() functions
Groups, Lookaheads, Lookbehinds, and Word Boundaries
Email, Phone Number, and Password Validation

Comprehensions (List, Set, Dictionary)
Shallow Copy vs. Deep Copy
Context Managers, enumerate(), and zip()
Working with collections, itertools, and Type Hints

Introduction to Data Structures and Algorithms
Linear vs. Non-Linear Data Structures
Time and Space Complexity Analysis & Big O Notation

Linear Search & Binary Search
Searching in Sorted Collections & Time Complexity Comparison

Bubble Sort, Selection Sort, and Insertion Sort
Merge Sort and Quick Sort
Built-in Sorting and Time Complexity Comparisons

Arrays and Python Lists
Stacks, Queues, and Circular Queues
Singly and Doubly Linked Lists

Trees, Binary Trees, and Binary Search Trees (BST)
Tree Traversal Techniques & Heaps/Priority Queues
Graphs, BFS, and DFS Traversal

Recursion, Backtracking, Two-Pointer, and Sliding Window
Hashing, Greedy Algorithms, and Dynamic Programming
Coding Challenges and Interview Problems

What is NumPy? & NumPy Arrays vs. Python Lists
Creating 1D and Multidimensional Arrays
Array Attributes (shape, size, ndim, dtype)

Indexing, Slicing, Reshaping, and Transposing Arrays
Joining, Splitting, Copying, and Viewing Arrays
Arithmetic Operations, Broadcasting, and ufuncs

Mathematical & Statistical Functions (Mean, Median, Std Dev, Variance)
Random Number Generation & Basic Matrix Operations
Linear Algebra Fundamentals

Series and DataFrames Creation
DataFrame Attributes, Methods, and Data Inspection

Reading and Writing CSV, Excel, and JSON Files
Setting and Resetting DataFrame Indexes

Using loc[] and iloc[] for Data Selection
Filtering, Sorting, Adding, and Renaming Columns
Vectorized Operations and Applying Functions

Identifying and Handling Missing Values
Removing Duplicates and Changing Data Types
String Operations and Handling Dates/Times

Aggregate Functions and groupby() Operations
pivot_table(), Merging, Joining, and Concatenating DataFrames

Importance of Visualization in Data Analysis
Introduction to pyplot, Figures, and Axes

Line, Bar, and Horizontal Bar Charts
Scatter Plots, Histograms, Pie Charts, and Box Plots

Titles, Axis Labels, Legends, and Grid Lines
Colors, Markers, Line Styles, and Annotations
Saving Charts as Image Files

Subplots, Multiple Axes, and Pandas DataFrame Plotting
Visualizing Trends and Handling Dates on Charts

Student Marks and Sales Trend Analysis
Product Comparisons and Salary Distribution Datasets

Introduction to Seaborn and Statistical Data Visualization
Creating Bar Plots, Count Plots, and Histograms
Scatter Plots, Line Plots, and Box Plots
Distribution Plots and Pair Plots
Heatmaps and Correlation Visualization
Customizing Plot Styles, Colors, Labels, and Legends
Visualizing Relationships and Patterns in Datasets
Practical Data Visualization Using Real-World Datasets

Database Fundamentals

Introduction to databases and DBMS
Database vs. DBMS vs. RDBMS
Introduction to SQL and SQL Server
SQL command categories: DDL, DML, DQL, DCL and TCL

SQL Server Setup

Installing SQL Server and SQL Server Management Studio (SSMS)
Creating and selecting databases
Understanding tables, rows and columns
Executing SQL queries

Data Types and Table Creation

Numeric, string, date and time data types
CREATE DATABASE and CREATE TABLE
Primary key and foreign key
AUTO_INCREMENT

Constraints and Table Operations

NOT NULL, UNIQUE and DEFAULT
CHECK constraints
ALTER TABLE and RENAME TABLE
DELETE vs. TRUNCATE vs. DROP

CRUD Operations

INSERT, SELECT, UPDATE and DELETE
SELECT DISTINCT and column aliases
WHERE clause
Comparison operators

Filtering and Sorting

AND, OR and NOT
BETWEEN, IN and LIKE
IS NULL and IS NOT NULL
ORDER BY, LIMIT and SQL comments

Common Functions

COUNT, SUM, AVG, MIN and MAX
CONCAT, UPPER and LOWER
LENGTH and SUBSTRING
ROUND, CEIL and FLOOR

Date and Conditional Functions

NOW and CURDATE
DATE_FORMAT and DATEDIFF
IFNULL and COALESCE
CASE expressions

GROUP BY and HAVING clauses
Understanding WHERE vs. HAVING
Aggregate functions with grouped data
Grouping records by department, category and month
Calculating totals, averages and counts
Business reporting and summary queries

Types of Joins

Understanding table relationships
INNER JOIN
LEFT JOIN and RIGHT JOIN
CROSS JOIN and SELF JOIN

Practical Queries

Joining multiple tables
Primary key and foreign key relationships
Employee and department queries
Customers, products and orders queries

Single-row and multiple-row subqueries
Correlated subqueries
IN, EXISTS and NOT EXISTS
UNION and UNION ALL
Common Table Expressions (CTEs)
Introduction to window functions
ROW_NUMBER, RANK and DENSE_RANK

Database design fundamentals
One-to-one, one-to-many and many-to-many relationships
Entity Relationship (ER) diagrams
Database normalization: 1NF, 2NF and 3NF
Designing tables for web applications
Maintaining data integrity

Python Database Connectivity

Installing a Python SQL Server connector
Establishing and closing database connections
Executing SQL queries from Python
Fetching one or multiple records

Database CRUD Operations

INSERT, SELECT, UPDATE and DELETE using Python
Parameterized queries
Handling database exceptions
Commit, rollback and transaction basics

Configuring SQL Server in Django settings
Connecting Flask applications to SQL Server
Introduction to Django ORM
Models, fields and database migrations
Creating, retrieving, updating and deleting ORM records
Saving HTML form data to SQL Server
Displaying database records on web pages
User registration and login data storage
Building database-backed web application features

Indexes and basic query optimization
Understanding EXPLAIN
Transactions, COMMIT and ROLLBACK
ACID properties
Database users and permissions
Preventing SQL injection with parameterized queries
Database backup and restore fundamentals

Database Projects

Student registration and management system
Employee and department management
Product and inventory management
E-commerce customers and orders database

Full Stack Integration

Python and SQL Server CRUD application
Django or Flask application with SQL Server
Forms, validation and database operations
Database-driven reporting features

Use Generative AI tools to improve coding productivity, problem-solving and application development.

Introduction to ChatGPT, Google Gemini and Microsoft Copilot
Prompt engineering for Python programming, DSA and data analytics tasks
Generating, explaining and improving Python code using Generative AI
AI-assisted debugging, refactoring and optimization of Python programs
Using AI to understand OOP concepts, classes, objects and inheritance
AI-assisted problem-solving for data structures, algorithms and coding challenges
Generating NumPy array operations and Pandas data manipulation solutions
Using AI to create Matplotlib and Seaborn visualizations and interpret results
Generating SQL Server queries, joins, subqueries and aggregate functions with AI assistance
Debugging SQL queries and understanding database errors using Generative AI
Using AI to clean datasets, analyze data and identify patterns
Generating sample datasets, test cases and practice exercises
Reviewing AI-generated Python, DSA, data analysis and SQL code for correctness
Using AI for project planning, documentation and technical explanations
Validating AI-generated solutions through independent coding and testing
Responsible AI usage, data privacy and protection of confidential code

Strengthen programming logic, analytical thinking and problem-solving through regular coding practice.

Practice Python coding problems on HackerRank, LeetCode and CodeChef
Python fundamentals, functions, exception handling and problem-solving techniques
Object-Oriented Programming (OOP): classes, objects, inheritance, polymorphism and encapsulation
Data Structures and Algorithms (DSA): lists, stacks, queues, linked lists, searching and sorting
Recursion, algorithmic techniques and time and space complexity fundamentals
NumPy arrays, array operations, indexing, slicing and mathematical computations
Pandas Series, DataFrames, data cleaning, filtering, grouping and data manipulation
Data visualization using Matplotlib: line charts, bar charts, histograms and scatter plots
Data visualization using Seaborn: statistical plots, distribution plots, pair plots and heatmaps
SQL Server: SQL queries, filtering, sorting, joins, aggregate functions and subqueries
SQL Server data analysis using GROUP BY, HAVING, views and window functions
Debugging Python programs, handling errors, testing edge cases and improving code efficiency
Using ChatGPT, Gemini and GitHub Copilot for coding hints, explanations and debugging assistance
Using Generative AI to understand algorithms, generate test cases and explore alternative solutions
Writing independent solutions and validating AI-generated Python, data analysis and SQL code
Integrated practice combining Python, NumPy, Pandas, visualization and SQL Server
Interview-oriented coding exercises, data analysis problems and timed programming practice

On Reviews — What Our Students Say

★★★★★

"Siva sir explains Core Python concepts in a very clear and practical way. The real-time examples make it easy to understand programming. The Generative AI and project guidance are valuable additions to the course."