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Full Stack AI & Data Science Course Curriculum
💻 Python
💻 Stats
🔵 Data Science and ML with AI
💻 SQL
🔵 Power BI
💻 DSA Python
💻 AWS
🔵 Projects
1. Introduction to Python
- Overview of Python
- Why Choose Python for Programming?
- What is Python?
- Real-World Applications of Python
- Platform Dependent vs Independent Languages
- Key Features of Python
- Limitations of Python
- The Evolution and History of Python
2. Writing Your First Python Program
- Installing Python
- Installing and Setting Up PyCharm
- Introduction to IDLE
- How Python Code is Executed
- Writing and Running the “Hello World” Program
3. Data Types in Python
- Python Statements, Indentation, and Comments
- How to Print in Python
- Getting User Input in Python
- Understanding Methods, Identifiers, and Variables
- Python Data Types and Variables
- Rules for Declaring Identifiers
4. Input and Output in Python
- Input and Output Functions in Python
- Reading Data in a Single Line
- Using the print() Function
- Formatted Print Statements
- The Replacement Operator
- Printing with the format() Method
5. Operators in Python
- Different Types of Operators in Python
- Bitwise Operators in Python
- Arithmetic and Assignment Operators
- Number System Conversion in Python
- Identity Operators
6. Control Statements and Loops in Python
- Writing if-else Statements in Python
- Using if-elif Conditions
- Creating Pyramid Patterns with Control Statements
- Transfer Statements: Break and Continue
- Using the Pass Statement
- Introduction to Loops
- Types of Loops: while and for
- Using Nested Loops
7. Strings in Python
- What Are Strings?
- Built-in String Functions
- String Class and Methods
- Common String Operations
- Type Casting in Python
8. Collections in Python
- Understanding Collections in Python
- Working with Lists, Tuples, Sets, and Frozen Sets
- Using Dictionaries in Python
- Bytes and Bytearray Collections
9. Functions in Python
- Understanding Functions in Python
- Types of Functions and Methods
- Creating Functions in Python
- Parameters and Arguments
- Function Arguments and Call by Value
- Types of Function Arguments
- Returning Values from Functions
- Passing Functions as Arguments
- Global Variables and the global Keyword
- Using the eval() Function
10. Recursion in Python
- Writing Recursive Functions
- Implementing Factorial with Recursion
- Reversing Numbers with Recursion
- Fibonacci Series with Recursion
11. Lambda Functions in Python
- Anonymous Functions with lambda
- Using the filter() Function with lambda
- lambda with the map() Function
12. Arrays in Python
- Introduction to Arrays
- Arrays Operations
- Types and Concepts of Arrays
- Using Arrays in Python
- Array Methods in Python
- Creating Arrays with NumPy
- Comparing Arrays in Python
- Understanding Object References
- Difference Between View and Copy
- Exploring NumPy Array Dimensions and Attributes
- Working with Multidimensional Arrays
- Using Matrices in Python with NumPy
13. Searching and Sorting in Python
- Implementing Binary Search
- Writing a Bubble Sort Algorithm
14. Object-Oriented Programming in Python
- Procedural vs Object-Oriented Programming
- Key Concepts of OOP in Python
- Creating Classes and Objects
- Understanding the self Variable
- Using the __init__() Method
- Constructor Overloading in Python
- Exploring Polymorphism and Operator Overloading
- Relational Operator Overloading
- Overloading vs Overriding in Python
- Using Getters and Setters
15. Object-Oriented Programming part-2
- Static Variables and Methods
- Working with Inner Classes
- Understanding Is-A vs Has-A Relationships
- Implementing Inheritance and Using the super() Method
- Types of Inheritance in Python
- Role of Constructors in Multiple Inheritance
- Abstract Classes and Methods
- Creating Interfaces in Python
16. Exception Handling in Python
- Basics of Exception Handling
- Common Exception Types
- Using try, except, and finally Blocks
- Exception Handling with Practical Programs
- Using the finally Statement
- Working with Assertions
- Raising Exceptions in Python
- Writing Custom Exceptions
17. File Handling in Python
- Introduction to Files in Python
- File Modes (r+, w+, a+)
- Reading, Writing, and Appending Files
- Using with for File Handling
- Handling File Exceptions
- Writing Data to a File with Practical Programs
- Working with readlines() and writelines() Methods
- Counting Lines, Words, and Characters in a File
- Reading Files Character by Character
- Binary File Operations in Python
- Working with the Pickle Module
- Reading and Writing CSV Files
- Using tell() and seek() Methods
18. Python Libraries
- Core Concepts of NumPy Arrays
- Creating Arrays in NumPy
- Comparing Arrays in NumPy
- Performing Arithmetic Operations on Arrays
- Data Analysis with Pandas
- Working with DataFrames in Pandas
- Inserting, Deleting, and Updating Data in Pandas DataFrames
- Creating Beautiful Graphs with Matplotlib
- Creating Pie Chart, Bar Graphs, Scatter Plots, Histogram, etc.
- Draw Plot using Seaborn
- Work on Bar, Histogram, Scatter and Heatmap Plots.
1. Introduction to Statistics
- What is Statistics
- Importance in Data Science
- Understanding Data
- Types of Statistics: Descriptive vs. Inferential
- Types of Data: Qualitative vs. Quantitative
- Populations vs. Samples
2. Descriptive Statistics
- Central Tendency: Mean, Median, Mode
- Dispersion
- Range, Variance, Standard Deviation
- Coefficient of Variation
3. Shape of Distribution
- Skewness and Kurtosis
- Understanding Normal Distribution
- Visualization Techniques
4. Graphs and Plots in Statistics
- Histograms
- Box Plots
- Bar Charts
- Pie Charts
- Scatter Plots
- Correlation
5. Probability Theory
- Basics of Probability
- Axioms of Probability
- Conditional Probability
- Bayes’ Theorem
- Applications of Bayes’ Theorem
6. Probability Distribution
- Random Variables
- Discrete Variables
- Probability Distributions: PMF, PDF and CDF
- Binomial, Poisson, Exponential, Uniform, Log-Normal
7. Statistical Inference
- Sampling and Sampling Distributions
- Hypothesis Testing
- Defining Null and Alternative Hypotheses
- Type I and Type II Errors
- Using p-Value and Significance Levels
- Applying Z-test, T-test, Chi-Square Test, and ANOVA
- Confidence Intervals
- Margin of Error
8. Regression
- What is Regression and Its Types
- Root Mean Square Error
- K Nearest Neighbor Algorithm
- Support Vector Machine
- Random Forest Algorithm
- Clustering
1. Data Science Essentials
- Introduction to Data Science
- The Evolution of Data Science
- Big Data vs. Data Science
- Key Terminologies in Data Science
- Working with NumPy, Pandas & Matplotlib
- Data Science vs. AI and Machine Learning
- Data Science vs. Analytics
- Data Science Applications in the Real World
2. Career in Data Science
- Why Data Science is in Demand
- Career Opportunities in Data Science
- How to build a career in Data Science
- Required Skill Set to become a Data Scientist
- AI & Data Science Roadmap
- Salary Trends & Career Growth
- Future of Data Science
3. Deep Dive into Data Science
- A Real-World Use Case
- Preparing Data for Analysis
- Building a Machine Learning Model
- Making Predictions with the Model
- Delivering Business Value with Data Science
- The Data Science Project Workflow
- Stages of a Data Science Project
- Roles: Data Engineer, Data Scientist, ML Engineer
4. Analytics Classification
- Types of Analytics
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
5. Data Science Industry Applications
- Data Science in Finance and Banking
- Data Science in Retail
- Data Science in Healthcare
- Data Science in Logistics and Supply Chain
- Data Science in the Technology Industry
- Data Science in Manufacturing
- Data Science in Agriculture
6. Exploratory Data Analysis Unlocking Insights
- What is Exploratory Data Analysis
- Numerical vs Categorical Data
- Continuous vs. Discrete Data
- Feature Engineering
- Handling Missing Values
- Handling Outliers
- Univariate, Bivariate, Multivariate Analysis
- Correlation
- Data Cleaning Essentials
- Business Insights from EDA
7. Machine Learning Introduction
- What is Machine Learning?
- How is it Different from AI?
- Why Machine Learning?
- Types of Machine Learning
- Key ML Terminologies
- Features and Labels in ML
- Training and Testing in ML
- Overfitting vs. Underfitting
- The Machine Learning Workflow
- Popular Machine Learning Algorithms
- AI vs ML vs Data Science
- AI & ML Case Studies in Industry
- AI & ML Applications in Real World
- Reinforcement Learning
- Data Science Project Architecture
- Data Science & Engineering Data Flow
- Data Platform Strategy
- Data Ecosystem Project Architecture
8. Supervised Learning – Regression
- What is Regression in Machine Learning?
- Types of Regression Problems
- Linear Regression
- Evaluation Metrics
- Common Challenges with Regression
- Linear vs Polynomial vs Ridge vs Lasso Regression
- Regression Industry Applications
- Regression Real-World Use Cases
9. Classification – Logistic Regression
- What is Classification?
- Regression vs. Classification
- Classification Real-World Applications
- Types of Classification Problems
- Data Preparation for Classification
- Label Encoding vs One-Hot Encoding
- Feature Scaling in ML
- Train-Test Split in ML
- Handling Imbalanced Data in Classification
- Model Evaluation Metrics in Classification
- Accuracy in Classification
- Precision, Recall, F1-Score in Classification
- Confusion Matrix in Classification
- ROC Curve and AUC Score in Classification
- Overfitting & Underfitting in ML
- Regularization Techniques in Classification
10. Logistic Regression – KNN
- What is k-NN?
- How k-NN Works
- KNN Distance Metrics
- Choosing the Right ‘k’ in KNN
- Data Preparation for KNN
- Categorical Features Handling in KNN
- Strengths and Limitations of KNN
- Evaluation Metrics in KNN
- KNN Model Tuning
- Cross-Validation in KNN for Optimal ‘k’
11. Decision Tree
- Introduction to Decision Tree
- Types of Decision Trees
- Components of a Decision Tree
- Decision Tree Splitting Criteria
- Gini Impurity in Decision Tree
- Entropy & Information Gain in Decision Tree N
- Advantages and Limitations of Decision Tree
- Pre-Pruning and Post-Pruning in Decision Tree
- Decision Tree Evaluation Metrics
- Decision Tree Hyperparameter Tuning
- Decision Tree Cross-Validation
12. Random Forest Algorithm
- Introduction to Random Forest
- Why use Random Forest over a Single Tree?
- Concepts Behind Random Forest
- How Random Forest Works
- Random Forest Hyperparameters
- Random Forest Advantages and Limitations
- Random Forest Use Cases
13. Unsupervised Learning
- What is unsupervised learning?
- Supervised vs unsupervised learning
- Where to use Unsupervised learning
- Application of Unsupervised learning
- Popular Algorithms in Unsupervised Learning
- Real world use cases of Unsupervised learning
- Evaluation Metrics
14. Unsupervised Learning – Clustering
- Introduction to Clustering Algorithms
- What is Clustering?
- Differences Between Supervised and Unsupervised Learning
- Classification vs Clustering
- K-means Clustering
- K-means Clustering Real-world industry use cases
- Elbow method in K-means Clustering
- Hierarchical Clustering
- Agglomerative vs Divisive Approach in Hierarchical Clustering
- Hierarchical Clustering Real-world industry use cases
- Dimensionality Reduction
15. Machine Learning in Business
- Enterprise Applications Overview
- Predictive Modeling & Segmentation
- Personalization Engines
- Time Series & Anomaly Detection
16. Scikit-Learn
- What is Scikit-Learn and Why is it Important?
- Features of Scikit-Learn for Machine Learning
- Installing Scikit-Learn and Setting Up the Environment
- Understanding the Machine Learning Workflow with Scikit-Learn
17. Natural Language Processing
- What is Natural Language Processing?
- Applications of NLP
- Text Preprocessing
- Text Classification
- Sentiment Analysis
18. Deep Dive in AI
- Introduction to Artificial Intelligence
- Introduction to Gen AI
19. Deep Learning
- What is Deep Learning?
- Definition and its importance in AI.
- Comparison with machine learning.
- Applications of Deep Learning
- Computer vision: Image recognition, object detection.
- Natural language processing: Speech recognition, chatbots.
- Applications in healthcare, finance, and autonomous vehicles.
- Deep Learning vs Machine Learning
- Deep Learning case studies in industry
- Why Deep Learning?
- Need for Deep Learning in Industry
- Why Deep Learning is in Demand
- Key Deep Learning Terminologies
- Basics of neurons, layers, and their roles.
- Activation functions: Sigmoid, ReLU, Tanh.
- Forward propagation and backpropagation.
- CNN
- Activation Functions in Deep Learning
- Optimizers in Deep Learning
- ResNet50
- Vanishing gradients
- Transfer Learning
- DenseNet121
- RNN
- ANN vs CNN vs RNN
- LSTM
- RNN vs LSTM
- LSTM in deep learning
20. Deep Learning Frameworks
- Real-time implementation with TensorFlow
- Real-time implementation with Keras
- Real-time implementation with PyTorch
- PyTorch vs TensorFlow
- PyTorch Features
- PyTorch Use Cases
21. Types of Neural Networks
- Fully connected networks.
- Convolutional Neural Networks (CNNs) for images.
- Recurrent Neural Networks (RNNs) for sequential data.
22. Prompt Engineering
Module 1: Introduction to Prompt Engineering
- What is Prompt Engineering?
- Importance in working with AI models like GPT and DALL·E.
- Applications across industries (e.g., content creation, customer support, design).
Module 2: Fundamentals of Prompt Design
- Types of prompts:
- Instruction-based.
- Conversational.
- Contextual.
- Components of effective prompts:
- Clarity, specificity, and relevance.
Module 3: Challenges in Prompt Engineering
- Understanding limitations of AI models.
- Ethical considerations in designing and using pro
23. Generative AI
Module 1: Introduction to Generative AI
- What is Generative AI?
- Differences between traditional AI and Generative AI.
- Real-world applications:
- Chatbots and conversational AI.
- Image generation (e.g., DALL·E).
- Video synthesis and editing.
Module 2: Fundamentals of Generative AI
- Understanding Generative Models:
- How they create new data.
- Types of Generative AI:
- GANs (Generative Adversarial Networks).
- VAEs (Variational Autoencoders).
- Transformers (foundation of GPT, DALL·E, and other tools).
Module 3: Popular Tools and Frameworks
- Overview of deep learning frameworks
- APIs and tools
- Working with pre-trained models to simplify tasks
24. Large Language Models (LLMs)
- What are Large Language Models?
- Definition and key features.
- Examples: ChatGPT, Bard, Claude, GPT-4.
- Importance of LLMs in AI and Data Science
- Role in modern AI applications.
- Real-world use cases in various industries.
- Fundamentals of LLMs
- How Do LLMs Work?
- Overview of transformer architecture.
- Basics of the attention mechanism.
- Tokenization process.
- Training Large Language Models
- Data preprocessing for LLMs.
- Overview of training datasets (Common Crawl, Wikipedia).
- Applications of LLMs
- Natural Language Processing Tasks
- Emerging Use Cases
- Healthcare, education, and finance.
- Research and scientific discoveries.
25. Agentic AI & AI Agents
- Agentic AI Essentials
- Historical evolution: From reactive systems to proactive agents
- Working with Agentic AI
- Applications of Agentic AI
- Agent vs. Model vs. Tool: Understanding the distinction
- Agentic AI vs AI Agents
- Multi Agent Systems
- Core characteristics of AI agents: autonomy, proactivity, adaptability
- Real-world use cases of AI agents
- Agent architectures
- Open-source agent framework
26. Data Engineering
- Introduction to Data Engineering
- What is Big Data?
- Difference between Big Data and Data Science
- Characteristics of Big Data
- Five Vs
- Importance of Big Data in Today’s World
- Evolution of Big Data and Its Role in Business
- Use Cases of Big Data Across Industries
27. Working on Hadoop
- What is Hadoop?
- Installation of Hadoop
- Architecture of Hadoop
- Hadoop Ecosystem: Hive, Pig, HBase, and Sqoop
- Working with HDFS and MapReduce
- Setting Up and Navigating HDFS
- Basic HDFS Commands for File Management
- How MapReduce Works
28. PySpark
- Introduction to Spark
- RDDs
- Data Processing with PySpark
- Spark SQL
1. Understanding SQL
- Introduction to SQL
- SQL Prerequisites
- What is SQL
- Purpose of SQL
- MySQL Server Installation
- SQLYog Installation
- Difference Between DBMS and RDBMS
- SQL Database and Database Server
- Comments in SQL
2. Commands in SQL Part – 1
- What are SQL Commands
- SQL DDL Statements
- SQL DDL Statements – Alter and Drop Command
- Practical Implementation of SQL DDL Command
- SQL DML Statements
- SQL DML Statements – Delete and Update Command
- Practical Implementation of SQL DML Command
3. Commands in SQL Part – 2
- SQL TCL Command
- Practical Implementation of TCL Command
- SQL DCL Command
- Practical Implementation of DCL Command
4. Data Types in SQL
- What are Data Type in SQL
- String Data Type in SQL
- Numerical Data Type in SQL
- Date Data Type in SQL
- Practical Implementation of Date Data Type in SQL
5. Operators in SQL
- What are Operators in SQL
- Arithmetic Operator in SQL
- Relational Operator in SQL
- Logical Operator in SQL
- Practical Implementation of Arithmetic, Relational and Logical Operators
- SQL String Operators
- SQL Like and Not Like Command
6. Clauses in SQL Part – 1
- Constraints in SQL
- Practical Implementation of Constraints in SQL
- Order by Clause in SQL
- Practical Implementation of Order by Clause
- GROUP BY and HAVING in SQL
- Practical Implementation of GROUP BY and HAVING in SQL
7. Clauses in SQL Part – 2
- Like Command in SQL
- Practical Implementation of Like Command in SQL
- Between and In Command in SQL
- Practical Implementation of Between and In Command
- ANY and ALL Operators
- Alias in SQL
- Practical Implementation of Alias in SQL
8. SQL Joins
- What are Joins in SQL
- Practical Implementation of SQL Joins
- Practical Implementatin of Self Join
9. SQL Keys
- Primary Key and Foreign Key Constraint in SQL
- Practical Implementation of Primary Key and Foreign Key Constraint
10. Advanced SQL
- Injection in SQL
- Practical Implementation of Injection
- Null Functions
- Check Constraint in SQL
- Practical Implementation of Check Constraint
11. Deep Dive in SQL
- Default Constraint in SQL
- Practical Implementation of Default Constraint
- Null Value in SQL
- Practical Implementations of Null Value in SQL
- Auto Increment in SQL
- Practical Implementation of Auto Increment in SQL
12. Functions in SQL Part – 1
- Aggregate Functions in SQL
- Practical Implementation of Aggregate Functions
- String Functions
- Nested Query in SQL
- Practical Implementation of Nested Query
13. Functions in SQL Part – 2
- SQL Window Function
- Practical Implementation of Window Function
- Bulk Insert in SQL
- Backup and Restore in SQL
14. Projects in SQL
- SQL Library Management System Project
- SQL Sales Data Analysis Project
- SQL Restaurant Billing System Project
- SQL Patient Record System Project
- SQL Vehicle Service Booking System Project
- SQL Simple Inventory ans Sales System Project
- SQL Movie Rating System Project
- SQL E-commerce Product Refund and Return Analytics System Project
1. Getting Started with Microsoft Power BI
- What is Business Intelligence
- Stages of Business Intelligence
- Use Cases of BI
- What is Power BI?
- Benefits of using Power BI
- Real-world applications of Power BI
- Various BI Tools
2. Power BI Installation and Setup
- How to download Power BI Desktop
- Step-by-step installation guide
- Initial setup and configuration
3. Power BI Architecture and Components
Understanding Power BI components
- Desktop, Service, Mobile
- How these components work together
Power BI Desktop UI
- Introduction to Power BI Desktop
- Installation of Power BI Desktop
- Exploring the Power BI Desktop interface
- Overview of key features and tools
- Navigating through different panes and menus
4. Get Data from Different Data Sources
Identify and Connect to a Data Source
- Types of data sources supported by Power BI
- Choosing the right data source for your needs
Get Data from Flat Files and Relational Data Sources
- Importing data from Excel, CSV, and other flat files
- Connecting to databases like SQL Server, MySQL, and more
5. Power Query
Introducing the Power Query Editor and Power Query UI
- What is Power Query?
- Introduction to Power Query Editor
- Navigating the Power Query Editor interface
- Basic features and functionalities
Power Query Ribbon and Different Tabs Introduction
- Overview of the Power Query ribbon
- Understanding different tabs and their functions
6. Power Query – Clean, Transform, and Load the Data
Enhance the Structure of the Data and Table Structure
- Organizing and structuring your data tables
- Creating relationships between tables
Profile the Data
- Analyzing data quality
- Identifying and handling data issues
Evaluate and Transform Column Data Types
- Changing data types for accuracy
- Ensuring consistency across your data
Change Data Source Settings
- Updating and managing data source connections
- Handling data source changes efficiently
7. Design a Data Model
Identify Fact and Dimension Tables
- Understanding the difference between fact and dimension tables
- Organizing your data for better analysis
Define Relationships and Cardinality
- Creating relationships between tables
- Understanding one-to-many and many-to-many relationships
Design a Data Model Using a Star Schema
- Building a star schema for efficient data analysis
- Benefits of using a star schema in Power BI
8. Data Model and DAX
Data Modelling and DAX
- Introduction to Relationships
- Creating Relationships
- Cardinality
- Cross-filter direction
- Use of inactive relationships
Introduction of DAX
- Why is DAX used?
- DAX syntax
- DAX functions
- Context in DAX
- Measures using DAX
9. Data Model and Calculations (DAX)
Create Calculated Tables
- Adding new tables using DAX
- Practical examples of calculated tables
- Learning about table, information, logical, text, iterator,
- Time intelligence functions (YTD, QTD, MTD)
Create Calculated Columns
- Creating columns with custom calculations
- Using DAX for dynamic data manipulation
10. Basic Measures using DAX
Create Basic Measures Using DAX
- Understanding measures in Power BI
- Writing simple DAX formulas for calculations
- Date and time functions
Difference Between Calculated Columns and Measures
- When to use calculated columns vs. measures
- Best practices for using DAX in your data model
- DAX advanced features
11. Create Reports
Create Report from Raw Data
- Starting your first Power BI report
- Importing and organizing your data in reports
12. Visualization in Reports
Add Visualization Items to Reports
- Inserting charts, graphs, and other visuals
- Customizing visual elements for clarity
Choose an Appropriate Visualization Type
- Selecting the right visual for your data
- Understanding different visualization options
13. Power BI Charts and Custom Visuals in Reports
Formatting and Configuring Power BI Charts
- Enhancing the look and feel of your charts
- Using formatting tools effectively
Use a Custom Visual and Import Custom Visuals
- Exploring the Power BI marketplace for custom visuals
- How to import and use custom visuals in your reports
14. Customize Themes and Conditional Formatting in Reports
Apply and Customize Themes
- Changing the overall theme of your report
- Creating a consistent look and feel
Configure Conditional Formatting
- Highlighting important data points
- Using color rules to emphasize trends
15. Sorting and Slicing in Reports
Apply Sorting
- Sorting data to improve readability
- Techniques for effective data sorting
Apply Slicing and Filtering
- Adding slicers for interactive filtering
- Using filters to focus on specific data
16. Create Hierarchies and AI Visuals in Reports
Create Hierarchies
- Building data hierarchies for better navigation
- Using hierarchies to drill down into data
Drilldown/Drillup into Data Using Interactive Visuals
- Exploring data at different levels of detail
- Making your visuals interactive and dynamic
AI Visuals – Q & A, Key Influencers, Decomposition Tree, Smart Narratives
- Utilizing AI-powered visuals for deeper insights
- How to implement and use AI visuals in your reports
17. How to Publish Reports
Publish Reports
- Sharing your reports with others
- Publishing reports to the Power BI service
18. Manage Workspaces
Create and Configure a Workspace
- Setting up workspaces for collaboration
- Organizing your projects within workspaces
Assign Workspace Roles
- Managing user roles and permissions
- Ensuring secure access to your data
Configure and Update a Workspace App
- Creating apps for easy access to reports and dashboards
- Updating and maintaining workspace apps
19. Create Dashboards
Create Dashboard
- Building your first Power BI dashboard
- Adding and arranging visuals on the dashboard
Manage Tiles on a Dashboard
- Customizing tiles for better presentation
- Organizing tiles for optimal layout
Pin a Live Report Page to a Dashboard
- Linking live report data to your dashboard
- Ensuring real-time data updates on your dashboa
20. Generative AI in Power BI
- Introduction to GenAI and its integration with Power BI
- Using Power BI Copilot for report generation and data analysis
- Enhancing dashboards with AI-generated summaries and insights
- Automating data storytelling with natural language narratives
- Real-world use cases of GenAI in business intelligence
- Best practices for using GenAI features responsibly
1. Introduction to DSA using Python
- What is DSA using Python?
- Why Python is a great choice for DSA
- Why DSA with Python
- Types of Data Structures
2. Stack in DSA Python
- Introduction to Stack in DSA Python
- Stack using List
- Stack using Collection Module in DSA Python
- Stack using Queue Module in DSA Python
- Convert Infix to Postfix Expression using Stack
- Convert Infix to Prefix in DSA
3. Queue in DSA Python
- What is a Queue in Data Structures?
- Queue using List in DSA Python
- Queue using Collection Module in DSA Python
- Linear Queue using Collection Module
- Circular Queue in DSA Python
- Insert, Display and Delete in Circular Queue
- D Queue in Data Structures
- Deque using Collection Module
- Priority Queue in DSA Python
- Priority Queue using List
- Priority Queue using Module
4. Linked List in DSA Python
- What is Linked List in DSA Python?
- Create, Display, Insert and Delete in Linked List in DSA Python
- Searching in Linked List
- Sorting in Linked List
- Stack Linked List in DSA Python
- Queue Linked List in DSA Python
5. Circular Linked List in DSA Python
- What is a Circular Linked List?
- Create, Display, Insert and Delete in DSA Python
6. Doubly Linked List in DSA Python
- What is Doubly Linked List in DSA Python?
- Create, Display and Reverse Display in Doubly Linked List in DSA Python
- Insertion and Display in Doubly Linked List in DSA Python
7. Circular Doubly Linked List in DSA Python
- What is Circular Doubly Linked List in DSA Python
- Creation and Display in Circular Doubly Linked List in DSA Python
- Insertion and Reverse Display in Circular Doubly Linked List in DSA Python
- Deletion in Circular Doubly Linked List in DSA Python
8. Recursion in DSA Python
- What is Recursion in DSA Python?
- Types of Recursion
- Tail Recursion in DSA Python
9. Searching in DSA Python
- Linear Search in DSA Python
- Binary Search in DSA Python
10. Sorting in DSA Python
- Linear Sort in DSA Python
- Bubble Sort in DSA Python
- Selection Sort
- Insertion Sort
- Radix Sort
- Shell Sort
- Quick Sort
- Two way Merge Sort
- Merge Sort
11. Tree in DSA Python Part – 1
- What is Tree in Data Structures using Python?
- Binary Tree and Binary Search Tree in DSA Python
- Tree Traversals in DSA Python
- Tree Traversal Methods – Inorder, Preorder, Postorder
- Creation of Tree from Inorder to Preorder in DSA Python
- Creation of Binary Search Tree
- Inorder, Preorder and Postorder in BST in DSA Python
- Searching and Sorting in BST in DSA Python
- Delete Node From Tree in DSA Python
12. Tree in DSA Python Part – 2
- Strictly Binary Tree
- AVL Tree
- Red Black Tree
- Threaded Binary Tree in DSA
- B Tree in DSA Python
- B+ Tree in DSA Python
- Heap Tree in DSA Python
13. Time Complexity in DSA using Python
- Address Calculation of Array in DSA Python
- Address Calculation of 3D Array in DSA Python
- Address Calculation of Lower & Upper Bound of Array in DSA
- Tower of Hanoi in DSA Python
14. Graph in DSA using Python
- What is a Graph in DSA?
- Graph Representation
- Graph Algorithms
- Kruskal’s Algorithm
- Prim’s Algorithm for Minimum Spanning Tree in DSA
- Dijkstra’s Algorithm
- Dijkstra Algorithm for Directed Graph
- Floyd Warshall Algorithm
- Breadth First Search Algorithm
- Depth First Search Algorithm
- What is Hashing
1. Introduction to AWS
- Traditional IT Systems and Classic Data Centers
- Cloud Computing
- Service Models and Deployments Models
2. AWS Cloud
- Cloud Overview
- Global Infrastructure and components
- Free Tier in AWS
- Practical Implementation of Free Tier
- Service Categories in AWS
3. AWS IAM
- What is IAM, Users, Groups and Roles
- Practical Implementation of IAM
- IAM Policies in AWS
- Practical Implementation of IAM Policies
- IAM Security Tools in AWS
- Practical Implementation of IAM Security Tools
- How to Access AWS Resources
- AWS CLI
- IAM Best Practices in AWS
4. EC2 AWS
- What is EC2
- Creating First Instance
- EC2 Instances Types
- Security Groups in AWS
- Practical Implementation of Security Group
- SSH and Instance Connect
- Stopping and Terminating Instance
- Instance Purchasing Option in AWS
5. Amazon EBS and EFS
- What is Amazon Elastic Block Storage
- Creating EBS Volumes
- What is Amazon EBS Snapshots
- Practical Implementation of EBS Snapshots
- What is Amazon Elastic File System
- Creating Shares File System using EFS
6. Know More About AWS
- High Availability
- Elastic Load Balancers in AWS
- Application Load Balancer
- Network Load Balancer
- Auto Scaling Group in AWS
- Practical Implementation of Auto Scaling Group
7. About S3
- Simple Storage Service in AWS
- Creating S3 Buckets
- Bucket Policy and Hosting Static Website
- Storage Classes and Versioning in AWS
- Practical Implementation of Storage Classes and Versioning
- S3 Encryption
- Storage Gateway
8. AWS RDS and DynamoDB
- Database Offerings in AWS
- Amazon RDS and Aurora
- Practical Implementation of Amazon RDS
- Amazon Deployment Options
- Amazon DynamoDB
- Amazon DynamoDB Tables
- Amazon Redshift
9. IP Address and VPC
- IP Addressing
- VPC and Components in AWS
- VPC Implementation
- NACL and Security Groups in AWS
- Implementation of NACL
- VPC Endpoints
- Transit Gateway
10. AWS CloudFormation
- Introduction to CloudFormation
- Practical Implementation of CloudFormation
- Elastic Beanstalk in AWS
- Elastic Beanstalk Implementation
- CodeDeploy in AWS
- CodeCommit in AWS
- CodeBuild in AWS
- CodePipeline in AWS
- CodeArtifact in AWS
11. AWS Security Part – 1
- Shared Responsibility Model in AWS
- Security and Compliance Services in AWS
- WAF and Shield in AWS
- Encryption uisng KMS in AWS
- Encryption using KMS Implementation
12. AWS Security Part – 2
- Firewall Manager in AWS
- Secrete Manager Service in AWS
- Secrete Manager Service Implementation
- Security and Compliance Services in AWS
13. AWS Monitoring & Observability
- What is CloudWatch in AWS
- CloudWatch Implementation
- EventBridge in AWS
- CloudTrail in AWS
- CloudTrail Implementation
- TrsutedAdvisor in AWS
14. AWS SQS and SNS
- Integration and Decoupling Services in AWS
- What is Amazon SQS
- Amazon SQS Implementation
- What is Amazon SNS
- Amazon SNS Implementation
15. AWS Global Application Services
- Need for Global Application Services
- Route 53 in AWS
- CloudFront in AWS
- Global Accelertor in AWS
16. AWS Data Analytics Services
- What is Amazon Data Analytics Services
- Athena in AWS
- Implementation of Athena
- Amazon Kinesis in AWS
- Amazon Kinesis Implementation
- Amazon Glue
- Amazon QuickSight
17. AWS Lambda
- Server Less Offerings in AWS
- Lambda in AWS
- Implementation of Lambda
- API Gateway in AWS
18. Optimizing AWS Resources
- Organizations Service in AWS
- Control Tower in AWS
- CostExplorer in AWS
- Budgets in AWS
- Cost Optimization in AWS
19. AWS Framework and Migration
- Well Architected Framework in AWS
- Cloud Adoption Framework in AWS
- Cloud Migration Strategies in AWS
- Database Migration Service in AWS
- AI and ML Services Overview in AWS
- Amazon Elastic Kubernetes Service
- Support Plans in AWS
20. AWS Exam Tricks
- Exam CLF-C02
- How to Prepare for the AWS Exam?
- Sample Questions of AWS
- Practical Tips
1. Sentiment Analysis
Analyze and classify movie reviews as positive or negative using natural language processing techniques. This project explores text preprocessing and sentiment classification.
2. Image Classification
Build and train a convolutional neural network to classify images into 10 different categories. This computer vision project introduces deep learning frameworks and image preprocessing.
3. Credit Card Fraud Detection
Detect fraudulent credit card transactions using anomaly detection algorithms. This project focuses on handling imbalanced datasets and implementing effective classification models.
4. Movie Recommendation System
Develop a recommendation engine to suggest movies to users based on their viewing history. This collaborative filtering project delves into matrix factorization and similarity measures.
5. Natural Language Processing for News Categorization
Classify news articles into predefined categories using NLP techniques. This text classification project covers tokenization, vectorization, and model training for multi-class classification.
6. Sales Forecasting
Predict weekly sales for Walmart stores across different departments using historical sales data. This project involves time series forecasting and multivariate regression.
7. Customer Churn Analysis
Build a model to predict customer churn based on usage patterns and demographic information. This project focuses on handling imbalanced datasets and implementing classification algorithms.
8. Energy Consumption Forecasting
Predict future energy consumption levels using historical usage data and external factors. Focuses on time series forecasting and model deployment.
9. Facial Emotion Recognition
Develop a deep learning model to detect emotions from facial images. Focuses on convolutional neural networks (CNNs) and image preprocessing.
10. Real-Time Stock Price Prediction
Develop a system that predicts stock price movements using live market data and technical indicators. Implement streaming data processing to update predictions continuously.
11. Retail Store Sales Forecasting
Predict future sales for a retail store using historical sales data and promotional information. This project focuses on time series analysis and regression techniques to help optimize inventory and marketing strategies.
12. E-commerce Product Classification
Automatically categorize e-commerce products into predefined categories using product descriptions and metadata. This project leverages text classification and machine learning algorithms.
13. Traffic Sign Recognition
Build a computer vision model to recognize and classify traffic signs from images. This project involves deep learning with convolutional neural networks (CNNs) and image preprocessing techniques.
14. Employee Attrition Prediction
Predict which employees are likely to leave the company based on their demographic and job-related features. This classification project focuses on handling imbalanced datasets and feature selection.
15. Music Genre Classification
Build a model to classify songs into their respective genres using audio features. This project involves audio data processing, feature extraction using libraries like Librosa, and applying machine learning algorithms for multi-class classification.
16. Spam Detection in Emails
Develop a classification model to identify spam emails based on their content and metadata. This project emphasizes text preprocessing, feature extraction, and implementing machine learning algorithms for binary classification.
17. Blockchain Transaction Analysis
Analyze and classify blockchain transactions to identify patterns, anomalies, and potential fraudulent activities. This project delves into network analysis, feature engineering from transaction data, and applying machine learning algorithms for anomaly detection.
18. Healthcare Patient Diagnosis
Develop a classification model to diagnose diseases from patient medical records. This project emphasizes data preprocessing, handling missing values, and building accurate diagnostic models.
19. Real-Time Language Translation
Build a real-time language translation model using neural machine translation techniques. This natural language processing project involves sequence-to-sequence models and attention mechanisms to facilitate multilingual communication.
20. Predicting Loan Approval
Build a classification model to predict loan approval outcomes based on applicant demographics, financial history, and loan details. This project emphasizes handling imbalanced datasets and feature selection to improve prediction accuracy.
21. Speech Emotion Recognition
Create a model to recognize emotions from speech recordings using audio signal processing and machine learning algorithms. This project explores feature extraction from audio data and classification techniques to interpret human emotions.
22. Detecting Fake News
Develop a natural language processing (NLP) model to classify news articles as real or fake based on their content and metadata. This project emphasizes text preprocessing, feature extraction, and implementing classification algorithms to combat misinformation.
23. Emotion Detection from Text
Build a natural language processing (NLP) model to detect emotions in text data, such as tweets or customer reviews. This project involves text preprocessing, sentiment analysis, and classification algorithms to interpret human emotions accurately.
24. Handwritten Signature Verification
Create a machine learning model to verify the authenticity of handwritten signatures. This computer vision project involves image preprocessing, feature extraction, and implementing classification algorithms to detect forgeries.
💻 Self-paced 🔵 Instructor-led Live
Data Science & AI Projects
Build Real-time Projects & Get Job-Ready!
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Detecting Fake News
You’ll create a program that analyzes news articles and determines if they are true or fake by studying patterns in text data.Credit Card Fraud Detection
Build a system that examines transaction data and identifies unusual activities to detect fraud in credit card usage.Sentiment Analysis
Analyze customer reviews or social media comments to identify whether the emotions expressed are positive, negative, or neutral. -
Brain Tumor Detection
Learn to use data science techniques to analyze medical images and identify brain tumors.Breast Cancer Classification
Develop a program that classifies breast cancer as malignant or benign by studying patient data and history.Language Translation
Design a model that translates text from one language to another, making communication across languages seamless. -
Movie Recommendation System
Create a program that suggests movies to people based on their previous choices and preferences, just like Netflix or Amazon Prime.Generate Human Faces
Build a model that can create realistic human faces from scratch using data, similar to how gaming or animation industries work.Customer Segmentation
Group customers into categories based on their shopping habits, helping businesses understand and target them better. -
Speech Emotion Recognition
Create a system that listens to spoken words and identifies emotions like happiness, sadness, or anger from the tone of voice.Sales Forecasting
Predict future sales for a store using past data, helping businesses plan their inventory and strategies.Retail Market Basket Analysis
Discover which products are frequently bought together to help stores design better offers and improve sales.
Tools and Technologies You’ll Learn in Full Stack AI & Data Science Bootcamp
In our AI & Data Science Bootcamp, you will learn
This Bootcamp is designed to make you job-ready and confident in the exciting world of AI and Data Science!
Learn Python from Scratch
Master Data Visualization
Understand Data Handling
Solve Real-World Problems
Grasp Machine Learning Concepts
Build Strong Analytical Skills
Learn Statistics for Data Science
Get Job Ready
From Learning to Placement – Enroll in our Live Data Science & AI Bootcamp
Join 41,750+ learners enrolled in TechVidvan’s AI & Data Science Course – Try before you Buy
UPCOMING
BATCHES
Weekend (Sat-Sun)
📅 20-Jun-2026
🕗 08.30 PM IST | 11.00 AM EDT
Weekend (Sat-Sun)
📅 08-Aug-2026
🕗 5.00 PM IST | 07.30 AM EDT
Benefits:
- ✓ Live Classes with Expert Mentors
- ✓ Practical-based learning
- ✓ Hands-on real-time Projects
- ✓ Lifetime Course Access
- ✓ Live Industry Case Studies
- ✓ Job-Ready Upskill Training
- ✓ Regular doubt-clearing sessions
- ✓ Weekly Assignments + Feedback
- ✓ Job Assistance & Resume Building
- ✓ Latest Tools, Tech & AI Integration
Benefits:
- ✓ Everything in Live Online Course, plus:
- ✓ Earn Global IBM certificates
- ✓ Professional-level training from IBM
- ✓ IBM-aligned curriculum
- ✓ Industry-grade projects
- ✓ Company-wise Interview Ques
- ✓ Masterclasses from IBM experts
- ✓ Additional Real-time Projects
- ✓ IBM-aligned labs for job-ready output
- ✓ Enhanced Career Opportunities
Job Guarantee
Job Guaranteed + Money Back
₹1,25,000 | $1,500
Benefits:
- ✓ Everything in IBM-backed Course, plus:
- ✓ Job Guarantee with Money Back
- ✓ LinkedIn & Job-Profile Optimization
- ✓ 1:1 Mentorship & Career Coaching
- ✓ Placement support until you get a Job
- ✓ Lifetime support & upgrade
- ✓ Company-Specific Interview Prep
- ✓ Real-Time Assessments & Capstone
- ✓ After Job Support
- ✓ Role-based mock interviews
Learn From Industry’s Best Instructors
AI & Data Science Case Studies
Transforming Business Operations in Top Companies
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Data Science at Netflix
Netflix uses data science to understand what people watch. It suggests shows and movies that match their interests, making it easier to find something they’ll enjoy.Data Science at Facebook
Facebook uses data science to learn how people connect and share. It helps show posts and ads that are most useful or interesting to each person.Data Science at Flipkart
Flipkart uses data science to predict what customers might want to buy. It helps suggest products and keeps stock ready for a smooth shopping experience. -
Data Science in Retail
Retail stores use data science to see what customers like to buy and when. It helps plan product placement and create better shopping deals for customers.Data Science in Digital Marketing
Digital marketers use data science to track customer behavior and improve ads. It ensures people see the right ads and companies get better results.Data Science in Healthcare
Data science helps doctors understand patient data, predict illnesses, and improve treatment. It makes healthcare more accurate and effective. -
Data Science in Education
Schools use data science to track how students are doing. It helps find learning gaps and improve teaching methods and students learning.Data Science in Movies & Entertainment
The movie industry uses data science to understand what audiences like. It helps make better movies and predict which ones will do well.
Full Stack AI & Data Science Job Roles
Learning Path of Full Stack AI & Data Science
TechVidvan’s Career Services
Career Support
Complete support from our dedicated placement team.
Dedicated Instructor
Expert data science career instructor to guide you.
Resume Building
Create professional, ATS-friendly resumes to impress recruiters.
Lifetime Learning Access
Get lifetime access to all courses for continuous learning and growth.
Job Application
Streamline your job applications with expert guidance and tools.
Job Guarantee Programs
Secure your future with programs guaranteeing jobs or money back.
TechVidvan Internship
Gain real-world experience with career-boosting internships.
Job Portal Access
Offer exclusive access to job listings through a dedicated job portal.
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“Transitioning into analytics felt challenging at first, but steady learning and hands-on work made the shift smooth and achievable.”
Veeresh Kottargi
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“I stopped doubting my potential the day I started building projects — that’s when everything began to change for me.”
Avina Garg
-
“Every phase of my journey taught me one thing — when you keep upgrading yourself, new leadership opportunities naturally open up.”
Aditya Agrawal
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“Learning became easier the moment I started treating challenges as opportunities — that mindset changed everything for me.”
Aditi Garg
Need Guidance? Ask Our Experts Now!
Full Stack AI & Data Science Course FAQs
Who should take this Data Science course?
This TechVidvan Data Science Bootcamp is ideal for engineering students, IT professionals, and anyone interested in pursuing a career in data science or analytics.
Do I need programming skills to start?
While some basic programming knowledge is helpful, it’s not necessary. The course starts with introductory lessons in Python to get you comfortable with coding.
Which programming languages are used in this course?
The course primarily focuses on Python, the most widely used language in data science, along with relevant libraries like Pandas and NumPy and many more.
What tools will I learn to use?
You will get hands-on experience with tools like Jupyter Notebook, SQL, and Power BI to analyze and visualize data.
Why are statistics important in Data Science?
Statistics form the backbone of data science. You’ll learn how to use statistical methods to analyze data and make data-driven decisions.
Will I receive a certificate after completing the course?
Yes, upon successfully finishing the course, you’ll receive a certificate that you can share with employers or on your LinkedIn profile.
What kind of career opportunities can I expect after completing this course?
This course prepares you for a variety of roles, including Data Analyst, Data Scientist, and Machine Learning Engineer, across multiple industries.
Are there any practical projects in the course?
Yes, there are multiple projects where you will apply your learning to real-world problems using actual datasets.
What industries hire Data Science professionals?
Industries like tech, healthcare, finance, retail, and government are actively hiring data scientists to solve complex problems.
What is the typical salary range for a data scientist?
Starting salaries for data scientists are usually between $70,000 and $100,000 per year, depending on location and experience level.
