Module 1: Foundations of Data Analytics

• Introduction to Data Analytics
• Types of Analytics
• Data Lifecycle & Workflow
• Tools for Data Analytics

Module 2: Excel for Data Analytics

• Excel Basics & Functions
• Data Cleaning & Transformation
• Advanced Excel Functions
• Pivot Tables & Charts
• Excel Dashboards

Module 3: SQL for Data Analytics

• Database Basics
• SQL Queries (SELECT, WHERE, ORDER BY)
• Aggregations and Grouping
• Joins
• Subqueries and CTEs
• Window Functions

Module 4: Python for Data Analytics

• Python Basics
• NumPy for Data Operations
• Pandas for Data Analysis
• Data Cleaning with Python
• Data Visualization with Matplotlib & Seaborn

Module 5: Tableau for Data Visualization

• Tableau Interface
• Dimensions and Measures
• Charts and Graphs
• Filters and Parameters
• Calculated Fields
• Dashboards and Storytelling

Module 6: Power BI for Data Visualization

• Power BI Interface
• Data Import and Cleaning
• Data Modeling and Relationships
• DAX Functions
• Interactive Visualizations
• Dashboards and Reports

Module 7: AWS for Data Analytics

• Introduction to Cloud Analytics
• AWS S3
• AWS Athena
• AWS Redshift
• AWS QuickSight
• Data Pipelines on AWS

Module 8: Applied Business Analytics

• HR Analytics
• Finance Analytics
• Marketing Analytics
• E-commerce Analytics
• Operations Analytics

Module 9: Capstone Project

• End-to-End Data Analytics Project

Module 10: Career & Interview Preparation

• Resume and Portfolio Building
• SQL Interview Preparation
• BI Dashboard Interview Preparation
• Business Case Studies
• Mock Interviews

Module 1: Foundations of Data Science

• Introduction to Data Science
• Role of Data Scientist vs Data Analyst
• Data Science Workflow
• Tools and Technologies in Data Science

Module 2: Python for Data Science

• Python Basics
• Data Structures and Functions
• Object-Oriented Programming Basics
• File Handling and Exceptions
• Python Libraries Overview

Module 3: Data Analysis Libraries

• NumPy for Numerical Operations
• Pandas for Data Analysis
• Data Cleaning and Preprocessing
• Data Transformation and Aggregation
• Data Visualization with Matplotlib and Seaborn

Module 4: Statistics for Data Science

• Descriptive Statistics
• Probability and Distributions
• Hypothesis Testing
• Correlation and Covariance
• Inferential Statistics
• Statistical Significance

Module 5: SQL for Data Science

• Database Basics
• SQL Queries (SELECT, WHERE, ORDER BY)
• Aggregations and Grouping
• Joins
• Subqueries and CTEs
• Window Functions

Module 6: Data Science Concepts

• Exploratory Data Analysis (EDA)
• Feature Engineering
• Handling Missing Data
• Outlier Detection
• Data Scaling and Normalization

Module 7: ETL and Data Pipelines

• Introduction to ETL
• Data Extraction Techniques
• Data Transformation Methods
• Data Loading into Databases
• Automating Data Pipelines
• Tools for ETL

Module 8: Machine Learning Foundations

• Introduction to Machine Learning
• Supervised Learning
• Unsupervised Learning
• Model Training and Validation
• Evaluation Metrics
• Overfitting and Regularization

Module 9: Advanced Machine Learning

• Ensemble Methods
• Dimensionality Reduction
• Feature Selection
• Hyperparameter Tuning
• Introduction to Deep Learning

Module 10: Applied Data Science

• Business Analytics with Data Science
• Predictive Modeling
• Prescriptive Analytics
• End-to-End Project Workflow

Module 11: Capstone Project

• End-to-End Data Science Project

Module 12: Career & Interview Preparation

• Resume and Portfolio Building
• Python and SQL Interview Preparation
• Statistics and ML Interview Preparation
• Business Case Studies
• Mock Interviews

Module 1: Foundations of Generative AI

• Introduction to Artificial Intelligence and Generative AI
• Evolution from AI → ML → DL → Gen AI
• Industry Applications of Generative AI
• Tools and Ecosystem for Generative AI

Module 2: Python for Generative AI

• Python Basics
• Data Structures and Functions
• Object-Oriented Programming Basics
• File Handling and Exceptions
• Python Libraries for AI

Module 3: Data Analysis Libraries

• NumPy for Numerical Operations
• Pandas for Data Analysis
• Data Cleaning and Preprocessing
• Data Transformation and Aggregation
• Data Visualization with Matplotlib and Seaborn

Module 4: Statistics for AI

• Descriptive and Inferential Statistics
• Probability and Distributions
• Hypothesis Testing
• Correlation and Covariance
• Statistical Significance for AI Models

Module 5: SQL for Data Science and AI

• Database Basics
• SQL Queries (SELECT, WHERE, ORDER BY)
• Aggregations and Grouping
• Joins
• Subqueries and CTEs
• Window Functions

Module 6: Data Science Concepts

• Exploratory Data Analysis (EDA)
• Feature Engineering
• Handling Missing Data
• Outlier Detection
• Data Scaling and Normalization

Module 7: ETL and Data Pipelines

• Introduction to ETL
• Data Extraction Techniques
• Data Transformation Methods
• Data Loading into Databases
• Automating Data Pipelines
• Tools for ETL

Module 8: Machine Learning Foundations

• Introduction to Machine Learning
• Supervised and Unsupervised Learning
• Model Training and Validation
• Evaluation Metrics
• Overfitting and Regularization

Module 9: Advanced AI Concepts

• Neural Networks Basics
• Deep Learning Introduction
• Activation Functions
• Backpropagation and Optimization
• Model Deployment Basics

Module 10: Generative AI

• Transformer Architecture
• Large Language Models (LLMs)
• Prompt Engineering Techniques
• Fine-tuning Pretrained Models
• Text, Image, Audio, and Video Generation
• Ethical and Responsible AI

Module 11: MLOps for Generative AI

• Introduction to MLOps
• Model Versioning and Experiment Tracking
• Model Deployment Pipelines
• Monitoring and Retraining Models
• CI/CD for AI Projects

Module 12: AWS for AI and Gen AI

• AWS S3 for Data Storage
• AWS Athena for Querying
• AWS SageMaker for Model Training
• AWS Lambda for Deployment
• AWS Bedrock and AI Services
• QuickSight for AI Dashboards

Module 13: Applied Generative AI

• Domain-Specific Gen AI Use Cases
• Business Process Automation with AI
• Predictive and Generative Insights
• End-to-End Generative AI Workflow

Module 14: Capstone Project

• End-to-End Generative AI Project

Module 15: Career & Interview Preparation

• Resume and Portfolio Building
• Python, SQL, and Statistics Interview Preparation
• Machine Learning and AI Interview Preparation
• Generative AI Use Case Discussion
• Mock Interviews

Module 1: Foundations of Web Development

• Introduction to Web Development
• Client-Server Architecture
• Frontend vs Backend vs Full-Stack
• Tools and Technologies Overview

Module 2: Python for Web Development

• Python Basics
• Data Structures and Functions
• File Handling and Exceptions
• Object-Oriented Programming Basics
• Python Libraries for Web Development

Module 3: Frontend Development (HTML, CSS, JS)

• HTML Basics and Semantic Structure
• CSS Styling and Responsive Design
• JavaScript Fundamentals
• DOM Manipulation
• Event Handling and Validation
• Frontend Best Practices

Module 4: Introduction to Django

• Django Framework Overview
• MVT (Model-View-Template) Architecture
• Setting up Django Environment
• Django Project and App Structure
• URL Routing and Views

Module 5: Templates and Static Files

• Django Template Language (DTL)
• Template Inheritance
• Using Static Files (CSS, JS, Images)
• Dynamic Rendering with Templates

Module 6: Models and Databases (SQL)

• Introduction to Databases
• SQL Queries (SELECT, INSERT, UPDATE, DELETE)
• Django Models and ORM
• Relationships (One-to-One, One-to-Many, Many-to-Many)
• Database Migrations
• QuerySets and Filters

Module 7: Forms and User Management

• Django Forms and Validation
• ModelForms
• Authentication and Authorization
• User Registration and Login
• Sessions and Cookies
• Role-Based Access Control

Module 8: Advanced Django Features

• Class-Based Views
• Middleware
• Django Admin Customization
• REST Framework Basics (Django REST API)
• Security Best Practices in Django

Module 9: AWS for Django Deployment

• Introduction to Cloud Deployment
• Hosting Django on AWS EC2
• Storing Static and Media Files on AWS S3
• AWS RDS for Databases
• Using AWS Elastic Beanstalk for Django Apps
• CI/CD for Django on AWS

Module 10: Capstone Project

• End-to-End Web Application using Django, SQL, Frontend, and AWS

Module 11: Career & Interview Preparation

• Resume and Portfolio Building
• Python, SQL, and Django Interview Preparation
• Frontend and Full-Stack Interview Questions
• System Design Basics for Web Apps
• Mock Interviews

Module 1: Foundations of Big Data

• Introduction to Big Data Ecosystem
• Characteristics of Big Data
• Batch vs Streaming Data
• Big Data Use Cases in Industry
• Overview of Big Data Tools and Technologies

Module 2: Python for Big Data

• Python Basics
• Data Structures and Functions
• File Handling and Exceptions
• Python Libraries for Data Engineering
• Working with Large Datasets in Python

Module 3: SQL for Big Data

• SQL Basics and Queries
• Joins and Aggregations
• Window Functions
• Advanced Query Optimization
• Using SQL in Big Data Environments

Module 4: Hadoop Ecosystem Overview

• HDFS
• YARN and Resource Management
• MapReduce Basics
• Hive and Impala Basics
• Sqoop and Flume Concepts

Module 5: Introduction to Apache Spark

• Spark Architecture and Components
• Spark vs Hadoop MapReduce
• RDD Fundamentals
• Transformations and Actions
• Spark Execution Flow

Module 6: PySpark for Big Data Processing

• Introduction to PySpark
• SparkContext and SparkSession
• DataFrames and Datasets
• PySpark SQL
• Data Cleaning and Transformation
• Aggregations and Joins

Module 7: Advanced PySpark

• User Defined Functions (UDFs)
• Spark MLlib Basics
• Spark Streaming Concepts
• Structured Streaming with PySpark
• Performance Tuning and Optimization
• Partitioning and Caching

Module 8: Cloud Platforms for Big Data

• AWS for Big Data
• GCP for Big Data
• Azure for Big Data
• Cloud Data Warehousing Concepts

Module 9: Data Engineering & ETL Pipelines

• ETL vs ELT in Big Data
• Building Scalable Pipelines with PySpark
• Ingesting Data from Multiple Sources
• Data Lake vs Data Warehouse
• Orchestration Tools
• Best Practices for Data Pipelines

Module 10: Big Data Analytics & Visualization

• Using PySpark with BI Tools
• Connecting Big Data with SQL Interfaces
• Querying Large-Scale Datasets
• Building Analytical Dashboards

Module 11: Real-Time Data Processing

• Kafka Basics for Streaming
• Integrating Kafka with PySpark
• Real-Time Data Ingestion
• Monitoring Streaming Jobs
• Streaming Analytics Use Cases

Module 12: Security & Best Practices in Big Data

• Data Governance and Compliance
• Authentication and Authorization
• Data Security in Cloud
• Monitoring and Logging
• Cost Optimization

Module 13: Applied Big Data Projects

• Data Ingestion Pipeline with PySpark and Cloud
• Real-Time Streaming Project with Kafka and Spark
• Big Data ETL with Cloud Data Warehouse
• Machine Learning with Spark MLlib

Module 14: Capstone Project

• End-to-End Big Data Project integrating PySpark, Cloud, and BI Tools

Module 15: Career & Interview Preparation

• Resume and Portfolio Building
• PySpark Interview Preparation
• Big Data & Cloud Interview Questions
• System Design for Data Engineering Interviews
• Mock Interviews

Module 1: Foundations of DevOps

• DevOps Principles and Culture
• SDLC and Delivery Pipelines
• Agile and Scrum Basics
• Toolchain Overview

Module 2: Linux, Shell & Networking

• Linux Filesystem and Permissions
• Bash Shell and Scripting
• Systemd, Services and Logs
• Networking Basics (TCP/IP, DNS, HTTP)
• SSH and Secure Remote Access

Module 3: Version Control with Git & GitHub

• Git Basics and Branching
• Pull Requests and Code Reviews
• Git Workflows (GitFlow, Trunk-Based)
• GitHub Actions Overview
• Managing Secrets and Protected Branches

Module 4: Scripting for Automation (Bash & Python)

• Bash Automation Patterns
• Python for Ops Tasks
• Virtual Environments and Packaging
• Task Scheduling and Cron
• Error Handling and Logging

Module 5: CI/CD Pipelines

• Continuous Integration Concepts
• Build and Test Automation
• Continuous Delivery and Deployment
• Jenkins / GitHub Actions / GitLab CI
• Artifacts and Release Management

Module 6: Containers with Docker

• Container Fundamentals
• Dockerfiles and Image Builds
• Volumes, Networks and Compose
• Image Scanning and Hardening
• Private Registries

Module 7: Kubernetes & Orchestration

• Kubernetes Architecture
• Pods, Deployments, Services, Ingress
• ConfigMaps, Secrets and RBAC
• Helm Charts and Kustomize
• Autoscaling, Upgrades and Rollbacks

Module 8: Infrastructure as Code (IaC)

• IaC Concepts and Benefits
• Terraform Basics and State
• Modules, Variables and Workspaces
• CloudFormation Overview
• Policy as Code (Open Policy Agent)

Module 9: Cloud for DevOps (AWS Focus)

• AWS IAM and Security Basics
• VPC, Subnets, Gateways and Networking
• EC2, ALB/ELB and Auto Scaling
• S3, ECR and CloudFront
• ECS and EKS Fundamentals
• Elastic Beanstalk Overview

Module 10: Configuration Management

• Ansible Playbooks and Roles
• Idempotency and Inventory Management
• Templating and Variables
• Chef/Puppet Overview
• Desired State and Drift Control

Module 11: Monitoring, Logging & Observability

• Metrics, Logs and Traces
• Prometheus and Grafana
• ELK/EFK Stacks
• AWS CloudWatch and X-Ray
• Alerting and On-Call Basics

Module 12: Security & DevSecOps

• Secure SDLC and Threat Modeling
• Secrets Management (Vault/SM)
• Container and Image Security
• Dependency and SAST/DAST Scanning
• Compliance and Policy Enforcement

Module 13: SRE & Incident Management

• SLI, SLO and Error Budgets
• Reliability Patterns and Runbooks
• Incident Response and Postmortems
• Capacity Planning and Scalability
• Blue/Green and Canary Releases

Module 14: Applied DevOps Projects

• Containerized App with CI/CD
• Terraform Infrastructure on AWS
• Kubernetes Deployment with Helm
• Observability Dashboard and Alerts
• Security Gates in Pipeline

Module 15: Career & Interview Preparation

• Resume and Portfolio Building
• Linux, Git and Scripting Interview Prep
• CI/CD, Docker and Kubernetes Interview Prep
• AWS and IaC Interview Questions
• System Design and Troubleshooting Drills

Along with our core programs, we also offer
personalized training services tailored for students and working professionals abroad.
Our goal is to bridge the gap between academic learning and real-world industry requirements.

Who We Support

• Students pursuing degrees in technology-related fields such as:
• BS in Computer Science
• MS in Computer Science
• FinTech
• MIS (Management Information Systems)
• BioTech
• Management with Technology focus
• Employees working in technology roles across various industries who may be:
• Struggling with complex industry projects
• Seeking career advancement in the tech field

Our Commitment

• Alignment with university curriculum requirements for global students
• Hands-on guidance in STEM (Science, Technology, Engineering, and Mathematics) domains
• Practical mentoring for career growth and project success

Top Universities We Support Students From