πŸŽ‰ Celebrating 13+ Years! Anniversary Special Offer: Get 50% OFF on All Courses – Limited Time Only!

Offline / Online Job-Oriented Programs

Comprehensive training for Graduates, Final Year students, and those with career gaps, preparing you for in-demand tech jobs.

Image: People working in a modern office.

Learn the code from expert trainers

Our trainers have over 15+ years of industry experience, bringing extensive knowledge and expertise to our training programs, ensuring practical and relevant learning.

Image: A trainer teaching code to students.

No 1 Training with Trainers from Top MNCs

Learn directly from industry experts with experience at Amazon, Google, Microsoft, and Infosys, gaining insights into real-world applications and best practices.

Image: Logos of Amazon, Google, Microsoft, and Infosys.

Experienced Trainers From Leading Tech Companies

Gain real-world skills from our trainers, who bring extensive experience from top multinational corporations. Their expertise ensures you receive industry-relevant training.

SIVASOFT TECHNOLOGIES PRIVATE LIMITED - Leading IT Training (2012-)

India's premier provider of classroom and online training in cutting-edge technologies.

πŸš€ 100% Classroom and Online:

Master Python (AI + ML + DSA) with Numpy, Pandas, SQL - MySQL, NoSQL - MongoDB, AI Tools Training Course

  • πŸš€ Sharpen Your Skills with Real-World Coding Practice on HackerRank, LeetCode & CodeChef
  • πŸ€– Learn Smarter with AI Tools like ChatGPT, Gemini & Copilot
Python Training Course

πŸŽ“ Eligibility: Any Graduates / Career Gap

πŸ‘¨β€πŸ« Trainer (120+ Batches): Mr. Siva(15+Yrs)

🌟 Offer: Valid only for 7 days

πŸ‘¨β€πŸ« Group Training Fee: β‚Ή20,000/- ➑️ Offer: β‚Ή10,000/-

πŸ§‘β€πŸ’» One-On-One Training Fee: β‚Ή60,000/- ➑️ Offer: β‚Ή30,000/-

πŸ“… Duration: 3 Months Training

πŸ“š Learning & Support
πŸ“š Lifetime Access: Live Classes & Video Recordings
⏰ Lab: Unlimited
❓ Doubt Clarification: 7:00 AM - 10:00 PM (Live, WhatsApp, LMS, Zoom, On-site)
πŸ“ Assignments, Quizzes, Interview Questions, Code Assessments and 10+ Mock Interviews
🏒 Hiring Partners: 370+ Companies Hiring from SivaSoft
πŸ“œ Certificates: Free Training Completion Certificate
✨ One-on-One Demo: Get Personalized Training & Clarifications

Course Curriculum

  • 1. Core Python
  • 2. Advanced Python
  • 3. Data Structures & Algorithms (DSA)
  • 4. NumPy
  • 5. Pandas
  • 6. SQL (MySQL)
  • 7. NoSQL (MongoDB)
  • 8. Artifical Intelligence
  • 9. Machine Learning
  • 10. πŸ€– Learn Smarter with AI Tools like ChatGPT, Gemini & Copilot
  • 11. πŸš€Sharpen Your Skills with Real-World Coding Practice on HackerRank, LeetCode & CodeChef
1. Core Python
  • Python Get Started
    • Introduction to Python: History, Features, and Uses
    • Installing Python and IDE Setup (VS Code, PyCharm, Jupyter Notebook)
    • Running Python Code: Interactive Mode and Script Mode
    • Python Versions and Differences (2.x vs 3.x)
  • Python Syntax
    • Indentation and Code Blocks
    • Statements and Expressions
    • Python Keywords and Naming Rules
    • PEP 8 – Python Code Style Guidelines
  • Python Comments
    • Single-line Comments using #
    • Multi-line Comments using ''' ''' or """ """
    • Importance of Comments for Code Readability
  • Python Variables
    • Python Variables: Declaration and Initialization
    • Variable Names: Rules and Conventions
    • Assign Multiple Values to Multiple Variables
    • Output Variables using print()
    • Global and Local Variables Scope
    • Deleting Variables with del
  • Python Data Types
    • Numeric Types: int, float, complex
    • Text Type: str
    • Boolean Type: bool
    • Type Casting and Conversion
    • Checking Data Type using type()
  • Python Strings
    • Creating Strings and Accessing Characters
    • Slicing Strings and Substrings
    • Modifying Strings
    • Concatenating and Repeating Strings
    • String Formatting: f-strings, % Formatting, format()
    • Escape Characters (\n, \t, \\, \")
    • String Methods: upper(), lower(), split(), replace()
    • Multiline Strings and Raw Strings
  • Python Booleans
    • Boolean Values: True / False
    • Boolean Operators: and, or, not
    • Truthy and Falsy Values
    • Using Booleans in Conditional Statements
  • Python Operators
    • Arithmetic Operators: +, -, *, /, %, **, //
    • Comparison Operators: ==, !=, >, <,>=, <=< /li>
    • Logical Operators: and, or, not
    • Assignment Operators: =, +=, -=, *=, /=, %=, **=
    • Membership Operators: in, not in
    • Identity Operators: is, is not
  • Control Flow
    • Python If...Else
      • Simple if Statements
      • if-else Statements
      • if-elif-else Ladder
      • Nested if Statements
      • Conditional Expressions (Ternary Operator)
    • Python While Loops
      • While Loop Syntax
      • Using break, continue, and pass
      • Infinite Loops and Loop Control
    • Python For Loops
      • For Loop Syntax and Iterables
      • Looping Through Lists, Tuples, Strings, and Dictionaries
      • Nested Loops
      • Using break, continue, and pass in Loops
  • Python Collections
    • Python Lists
      • Python Lists
      • Access List Items
      • Change List Items
      • Add List Items
      • Remove List Items
      • Loop Lists
      • List Comprehension
      • Sort Lists
      • Copy Lists
      • Join Lists
      • List Methods
    • Python Tuples
      • Python Tuples
      • Access Tuples
      • Update Tuples
      • Unpack Tuples
      • Loop Tuples
      • Join Tuples
      • Tuple Methods
    • Python Sets
      • Python Sets
      • Access Set Items
      • Add Set Items
      • Remove Set Items
      • Loop Sets
      • Join Sets
      • Set Methods
    • Python Dictionaries
      • Python Dictionaries
      • Access Items
      • Change Items
      • Add Items
      • Remove Items
      • Loop Dictionaries
      • Copy Dictionaries
      • Nested Dictionaries
      • Dictionary Methods
  • Python Lambda
    • Anonymous Functions using lambda
    • Lambda with map(), filter(), reduce()
    • Difference between normal function and lambda
  • Python Arrays
    • Difference Between Arrays and Lists
    • Creating Arrays with array Module
    • Array Operations and Methods
  • Python Scope
    • Local vs Global Scope
    • Global Keyword Usage
    • Nonlocal Variables
  • Python Modules
    • Importing Modules and Packages
    • Using Python Standard Library
    • Creating and Using Custom Modules
    • Using from ... import ... for selective imports
2. Advanced Python
  • Python Classes/Objects
    • Introduction to Object-Oriented Programming (OOP)
    • Creating Classes and Objects
    • Class Attributes vs Instance Attributes
    • Methods: Instance Methods, Class Methods, Static Methods
    • Constructors (__init__) and Destructors (__del__)
    • Encapsulation: Private and Protected Members
    • Polymorphism: Method Overloading & Method Overriding
    • Special / Magic Methods (__str__, __repr__, __len__, etc.)
  • Python Inheritance
    • Single Inheritance
    • Multiple Inheritance
    • Multilevel Inheritance
    • Hierarchical Inheritance
    • Using super() to Call Parent Methods
    • Method Resolution Order (MRO)
  • Python Iterators
    • Iterable vs Iterator
    • Creating Iterators using iter() and next()
    • Custom Iterators: Implementing __iter__ and __next__
    • Using Generators for Memory-Efficient Iteration
  • Python Dates
    • Python datetime Module
    • Creating Date and Time Objects
    • Date and Time Formatting (strftime(), strptime())
    • Date Arithmetic: Adding/Subtracting Days, Weeks
    • Comparing Dates
  • Python Math
    • Python math Module Functions: sqrt(), factorial(), ceil(), floor()
    • Random Numbers using random Module
    • Trigonometric Functions: sin(), cos(), tan()
    • Logarithmic and Exponential Functions
  • Python JSON
    • JSON Basics: What is JSON?
    • Converting Python Objects to JSON (json.dumps())
    • Converting JSON to Python Objects (json.loads())
    • Reading and Writing JSON Files (json.dump(), json.load())
  • Python RegEx
    • Introduction to Regular Expressions (re Module)
    • Basic Patterns: ^, $, ., *, +, ?
    • Character Sets, Groups, and Ranges
    • Search, Match, Findall, Split, Sub
    • Practical Examples: Email, Phone Number Validation
  • Python PIP
    • What is PIP?
    • Installing Packages using pip install package_name
    • Upgrading and Uninstalling Packages
    • Using Virtual Environments
  • Python Try...Except
    • Exception Handling Basics
    • Try, Except, Finally Blocks
    • Handling Multiple Exceptions
    • Raising Exceptions using raise
    • Custom Exception Classes
  • Python String Formatting
    • Old Style (%) Formatting
    • New Style (format()) Formatting
    • f-strings (Python 3.6+)
    • Padding, Alignment, Precision
    • Dynamic Formatting in Loops
  • File Handling
    • Python File Handling
      • Opening Files in Different Modes: 'r', 'w', 'a', 'rb', 'wb'
      • Reading Files: read(), readline(), readlines()
      • Writing / Creating Files: write(), writelines()
      • Deleting Files using os.remove()
      • Working with File Paths (os.path)
      • Using with Statement for Auto File Closure
3. Data Structures & Algorithms (DSA)
  • Introduction to DSA
  • Time & Space Complexity
  • Arrays
    • Introduction to Arrays
    • Array Operations
    • Two-dimensional Arrays
  • Linked Lists
    • Singly Linked Lists
    • Doubly Linked Lists
    • Circular Linked Lists
    • Linked List Operations
  • Stacks
    • Introduction to Stacks
    • Stack Operations
    • Stack Implementation
  • Queues
    • Introduction to Queues
    • Queue Operations
    • Queue Implementation
    • Circular Queues
    • Priority Queues
  • Trees
    • Introduction to Trees
    • Binary Trees
    • Binary Search Trees (BST)
    • Tree Traversals
    • AVL Trees
  • Graphs
    • Introduction to Graphs
    • Graph Representation (Adjacency Matrix, Adjacency List)
    • Graph Traversals (BFS, DFS)
    • Shortest Path Algorithms
  • Hashing
    • Introduction to Hashing
    • Hash Functions
    • Hash Tables & Collision Resolution
  • Sorting Algorithms
    • Bubble Sort
    • Selection Sort
    • Insertion Sort
    • Merge Sort
    • Quick Sort
    • Heap Sort
  • Searching Algorithms
    • Linear Search
    • Binary Search
  • Recursion & Backtracking
    • Introduction to Recursion
    • Recursive Functions
    • Backtracking Algorithms
  • Dynamic Programming
    • Introduction to DP
    • Memoization & Tabulation
    • DP Problems
  • Greedy Algorithms
  • Complexity Analysis & Big-O Notation
4. NumPy
  • Introduction to NumPy
    • What is NumPy and Why Use It?
    • Installation: pip install numpy
    • Importing NumPy: import numpy as np
    • Advantages over Python Lists
  • NumPy Arrays
    • Creating Arrays: array(), arange(), linspace(), zeros(), ones(), full(), eye()
    • Array Attributes: shape, ndim, size, dtype, itemsize
    • Indexing and Slicing Arrays
    • Negative Indexing, Step Slicing
    • Fancy Indexing & Boolean Indexing
  • Array Operations
    • Mathematical Operations: Addition, Subtraction, Multiplication, Division
    • Universal Functions (ufuncs): np.sin(), np.cos(), np.exp(), etc.
    • Aggregate Functions: sum(), min(), max(), mean(), median(), std(), var()
    • Axis-wise Operations
  • Reshaping Arrays
    • Reshape: reshape()
    • Flattening: flatten(), ravel()
    • Transposing Arrays: transpose(), .T
    • Concatenation & Splitting: hstack(), vstack(), split()
  • Copy vs View
    • Understanding Shallow and Deep Copies
    • copy() vs view()
  • Random Module in NumPy
    • Random Numbers: rand(), randn(), randint()
    • Setting Seed for Reproducibility: np.random.seed()
  • Linear Algebra with NumPy
    • Matrix Operations: dot(), matmul()
    • Determinant, Inverse, Eigenvalues, Eigenvectors
    • Solving Linear Equations: np.linalg.solve()
  • Practical Examples
    • Using NumPy with Real Datasets
    • Data Cleaning and Manipulation with NumPy Arrays
    • Performance Comparison: Python Lists vs NumPy Arrays
5. Pandas
  • Introduction to Pandas
    • What is Pandas and Why Use It?
    • Installation: pip install pandas
    • Importing Pandas: import pandas as pd
    • Comparison with NumPy and Python Lists
  • Pandas Data Structures
    • Series: One-dimensional labeled array
    • Creating Series from lists, dictionaries, and NumPy arrays
    • Accessing and Modifying Series
    • DataFrame: Two-dimensional labeled data structure
    • Creating DataFrames from dictionaries, lists, CSV, Excel, JSON
    • Accessing Columns and Rows using loc and iloc
  • Data Exploration
    • Viewing data: head(), tail(), sample()
    • Getting information: info(), describe(), shape, columns
    • Checking missing values: isnull(), notnull()
    • Unique values and value counts: unique(), nunique(), value_counts()
  • Data Selection and Indexing
    • Selecting columns and rows
    • Conditional selection with Boolean indexing
    • Setting and resetting index
    • Multi-indexing
  • Data Cleaning
    • Handling missing data: fillna(), dropna(), replace()
    • Renaming columns and index: rename()
    • Changing data types: astype()
    • Removing duplicates: drop_duplicates()
  • Data Transformation
    • Sorting: sort_values(), sort_index()
    • Applying functions: apply(), map(), applymap()
    • Renaming columns and mapping values
    • Adding and deleting columns
  • Data Aggregation and Grouping
    • GroupBy operations
    • Aggregation functions: sum(), mean(), count(), min(), max()
    • Pivot tables: pivot_table()
    • Cross tabulations: crosstab()
  • Combining Datasets
    • Concatenation: concat()
    • Merging and Joining: merge(), join()
    • Appending rows: append()
  • Handling Time Series Data
    • Converting strings to datetime: to_datetime()
    • Date indexing and slicing
    • Resampling and frequency conversion: resample()
    • Rolling and expanding windows: rolling(), expanding()
  • Input and Output
    • Reading and writing CSV files: read_csv(), to_csv()
    • Reading and writing Excel files: read_excel(), to_excel()
    • Reading JSON files: read_json(), to_json()
    • Reading SQL database tables using read_sql()
  • Practical Examples
    • Exploratory Data Analysis (EDA) on sample datasets
    • Data cleaning, transformation, and aggregation workflow
    • Real-world datasets analysis: sales, stock prices, healthcare, etc.
6. SQL (MySQL)
  • MySQL Overview
    • Introduction to Databases and MySQL.
    • Understanding Relational Database Management Systems (RDBMS).
    • MySQL Architecture and Components.
    • MySQL Installation and Configuration.
    • Connecting MySQL using Command Line and Workbench.
    • Understanding SQL Syntax and Statement Execution Flow.
  • MySQL Language Types
    • DDL (Data Definition Language) – Defines and modifies database structure.
      Commands: CREATE, ALTER, DROP, TRUNCATE, RENAME
    • DML (Data Manipulation Language) – Manages data stored in tables.
      Commands: INSERT, UPDATE, DELETE
    • DQL (Data Query Language) – Retrieves data from the database.
      Command: SELECT
    • DCL (Data Control Language) – Controls user permissions and privileges.
      Commands: GRANT, REVOKE
    • TCL (Transaction Control Language) – Manages database transactions.
      Commands: COMMIT, ROLLBACK, SAVEPOINT, SET TRANSACTION
  • MySQL Get Started
    • Understanding Database, Tables, Rows, and Columns.
    • Creating and Selecting Databases.
    • MySQL Data Types (INT, VARCHAR, DATE, FLOAT, etc.).
    • Using MySQL Workbench for Query Execution.
    • Creating Users and Managing Connections.
  • MySQL Create Database (DDL)
    • Creating a new database using CREATE DATABASE.
    • Viewing available databases using SHOW DATABASES;.
    • Switching between databases using USE.
    • Example: CREATE DATABASE studentdb;
  • MySQL Create Table (DDL)
    • Creating tables with appropriate data types.
    • Adding constraints: PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, DEFAULT.
    • Example: CREATE TABLE students (id INT AUTO_INCREMENT PRIMARY KEY, name VARCHAR(50), age INT);
  • MySQL Alter Table (DDL)
    • Adding new columns to an existing table.
    • Modifying column data types or names.
    • Dropping or renaming columns.
    • Example: ALTER TABLE students ADD email VARCHAR(100);
  • MySQL Insert (DML)
    • Inserting records into a table using INSERT INTO.
    • Inserting multiple rows at once.
    • Example: INSERT INTO students (name, age) VALUES ('Supriya', 22), ('Kiran', 24);
  • MySQL Select (DQL)
    • Retrieving records using SELECT.
    • Using column aliases and calculated fields.
    • Example: SELECT id, name, age FROM students;
  • MySQL Where (DQL)
    • Filtering rows using conditions.
    • Operators: =, <>, >, <, BETWEEN, IN, LIKE, IS NULL.
    • Example: SELECT * FROM students WHERE age >= 20;
  • MySQL Order By (DQL)
    • Sorting query results in ascending or descending order.
    • Example: SELECT * FROM students ORDER BY name ASC;
  • MySQL Delete (DML)
    • Deleting specific rows using DELETE FROM.
    • Deleting all rows using DELETE or TRUNCATE.
    • Example: DELETE FROM students WHERE id = 1;
  • MySQL Drop Table (DDL)
    • Removing an entire table from the database permanently.
    • Example: DROP TABLE students;
  • MySQL Update (DML)
    • Updating existing records using UPDATE and SET.
    • Example: UPDATE students SET age = 25 WHERE name = 'Supriya';
  • MySQL Limit (DQL)
    • Limiting the number of rows returned by a query.
    • Example: SELECT * FROM students LIMIT 5;
    • Using OFFSET for pagination. SELECT * FROM students LIMIT 5 OFFSET 10;
  • MySQL Join (DQL)
    • Combining data from multiple tables based on relationships.
    • Types of Joins:
      • INNER JOIN – Returns matching records from both tables.
      • LEFT JOIN – Returns all records from left table and matching from right.
      • RIGHT JOIN – Returns all records from right table and matching from left.
      • FULL JOIN – Returns all records when a match exists in any table.
    • Example: SELECT s.name, c.course_name FROM students s INNER JOIN courses c ON s.id = c.student_id;
  • MySQL Constraints (DDL)
    • PRIMARY KEY – Ensures unique identification of records.
    • FOREIGN KEY – Links tables and maintains referential integrity.
    • UNIQUE – Prevents duplicate entries in a column.
    • NOT NULL – Ensures a column cannot have NULL value.
    • DEFAULT – Provides a default value if no value is supplied.
  • MySQL Aggregate Functions
    • COUNT(), SUM(), AVG(), MIN(), MAX() – for summary statistics.
    • Example: SELECT COUNT(*) FROM students;
  • MySQL Group By and Having
    • Grouping data based on columns using GROUP BY.
    • Filtering grouped results using HAVING.
    • Example: SELECT age, COUNT(*) FROM students GROUP BY age HAVING COUNT(*) > 2;
  • MySQL Subqueries
    • Using nested SELECT statements inside WHERE or FROM clauses.
    • Example: SELECT name FROM students WHERE age > (SELECT AVG(age) FROM students);
  • MySQL DCL Commands
    • GRANT – Provides access privileges to users.
    • REVOKE – Removes previously granted privileges.
    • Example: GRANT SELECT ON school.* TO 'user'@'localhost';
  • MySQL TCL Commands
    • COMMIT – Saves all changes made in a transaction permanently.
    • ROLLBACK – Undoes recent changes in a transaction.
    • SAVEPOINT – Sets a point within a transaction for partial rollback.
    • Example: START TRANSACTION; UPDATE students SET age=30 WHERE id=5; ROLLBACK;
  • MySQL Views
    • Creating a virtual table using CREATE VIEW.
    • Updating and Dropping views.
    • Example: CREATE VIEW student_names AS SELECT name FROM students;
  • MySQL Indexes
    • Creating indexes for faster data retrieval.
    • Example: CREATE INDEX idx_name ON students(name);
  • MySQL Normalization
    • Understanding database redundancy and normalization.
    • 1NF, 2NF, 3NF, BCNF – step-by-step normalization examples.
  • MySQL Backup and Restore
    • Backing up databases using mysqldump.
    • Restoring data from backups.
    • Example: mysqldump -u root -p studentdb > backup.sql
  • MySQL Advanced Topics
    • Stored Procedures and Functions.
    • Triggers and Events.
    • Database Transactions and Isolation Levels.
    • Performance Tuning and Query Optimization.
7. NoSQL (MongoDB)
  • MongoDB Overview
    • Introduction to NoSQL and MongoDB.
    • Difference between SQL (Relational) and NoSQL (Document-based) databases.
    • MongoDB architecture and components.
    • Installing MongoDB and MongoDB Compass.
    • Connecting using Mongo Shell and Compass GUI.
    • Understanding documents, collections, and databases.
  • MongoDB Get Started
    • Basic MongoDB syntax overview.
    • CRUD operations introduction.
    • Creating users and managing connections.
  • MongoDB Create Database
    • Switching or creating a new database using use databasename.
    • Listing databases: show dbs.
    • Example: use studentDB
  • MongoDB Collection
    • Collections are similar to SQL tables.
    • Automatic creation when inserting a document.
    • Manual creation using db.createCollection("students").
    • Listing collections: show collections.
  • MongoDB Insert
    • Inserting documents using insertOne() and insertMany().
    • Example: db.students.insertOne({ name: "Supriya", age: 22 }) db.students.insertMany([{ name: "Kiran", age: 23 }, { name: "Ravi", age: 21 }])
  • MongoDB Find
    • Retrieving all or specific documents using find() and findOne().
    • Example: db.students.find(), db.students.findOne({ name: "Supriya" })
  • MongoDB Query
    • Filtering documents using conditions.
    • Comparison operators: $eq, $ne, $gt, $lt, $gte, $lte
    • Logical operators: $and, $or, $not
    • Example: db.students.find({ age: { $gt: 20 } })
  • MongoDB Sort
    • Sorting documents ascending (1) or descending (-1).
    • Example: db.students.find().sort({ name: 1 })
  • MongoDB Delete
    • Deleting documents using deleteOne() or deleteMany().
    • Example: db.students.deleteOne({ name: "Ravi" })
  • MongoDB Drop Collection
    • Removing an entire collection: db.students.drop()
  • MongoDB Update
    • Updating documents using updateOne() or updateMany().
    • Operators: $set, $inc, $unset
    • Example: db.students.updateOne({ name: "Supriya" }, { $set: { age: 23 } })
  • MongoDB Limit
    • Limiting results using limit() and skipping using skip().
    • Example: db.students.find().limit(5)
  • MongoDB Aggregation
    • Performing data analysis with aggregate().
    • Stages: $match, $group, $project, $sort, $limit
    • Example: db.students.aggregate([ { $match: { age: { $gt: 20 } } }, { $group: { _id: "$course", total: { $sum: 1 } } } ])
  • MongoDB Indexes
    • Improve query performance using indexes.
    • Types: Single, Compound, Text, Unique, TTL.
    • Example: db.students.createIndex({ name: 1 })
  • MongoDB Relationships
    • Using embedded documents and references.
    • One-to-One, One-to-Many, Many-to-Many relationships.
    • Example (embedded): { name: "Supriya", courses: [{ courseName: "MERN" }, { courseName: "Python" }] }
  • MongoDB Schema Design
    • Embedding vs Referencing.
    • Normalization vs denormalization.
    • Best practices for large-scale applications.
  • MongoDB Data Validation
    • Ensuring data integrity using JSON schema validator.
    • Example: db.createCollection("students", { validator: { $jsonSchema: { bsonType: "object", required: ["name", "age"], properties: { name: { bsonType: "string" }, age: { bsonType: "int", minimum: 18 } } } } })
  • MongoDB Views
    • Virtual collections with db.createView().
    • Example: db.createView("adult_students", "students", [ { $match: { age: { $gte: 18 } } } ])
  • MongoDB Transactions
    • ACID transactions across multiple documents.
    • Start, commit, rollback using sessions.
    • Example: const session = client.startSession(); session.startTransaction(); db.students.updateOne(...); session.commitTransaction();
  • MongoDB Backup and Restore
    • Using mongodump and mongorestore.
    • Example: mongodump --db studentDB --out /backup/studentDB mongorestore /backup/studentDB
  • MongoDB Performance Optimization
    • Indexing strategies, query optimization with explain().
    • Sharding for horizontal scaling.
    • Best practices for large datasets.
  • MongoDB Security
    • Authentication and Role-Based Access Control (RBAC).
    • Encryption at rest and in transit.
    • Auditing user activity.
  • MongoDB Advanced CRUD with Mongoose
    • Node.js integration using Mongoose models.
    • Validation, middleware, and advanced queries.
    • Example: const Student = mongoose.model("Student", studentSchema); Student.find({ age: { $gte: 18 } })
8. Artifical Intelligence
9. Machine Learning
10. πŸ€– Learn Smarter with AI Tools like ChatGPT, Gemini & Copilot
11. πŸš€Sharpen Your Skills with Real-World Coding Practice on HackerRank, LeetCode & CodeChef