Topics:

• 1. What is an Application?

• 2. Types of Applications

• 3. Web Application Fundamentals

• 4. Web Technologies: (List key technologies and their roles)

  Frontend: HTML, CSS, JavaScript, React

  Backend: Python, Java, Node.js

  Databases: SQL (MySQL, PostgreSQL), NoSQL (MongoDB).

• 5. Software Development Life Cycle (SDLC)

  Phases: Planning, Analysis, Design, Implementation (Coding), Testing, Deployment, Maintenance.

• 6. Application Development Methodologies

  Agile: Core principles, Scrum, Kanban

  Waterfall

Topics:

• 1. What is Data?

• 2. Types of Data

• 3. Data Storage

• 4. Data Analysis

• 5. Data Engineering

• 6. Data Science

Topics:

• 1. The Importance of Computing Power

• 2. Key Computing Technologies:

  CPU (Central Processing Unit)

  GPU (Graphics Processing Unit)

• 3. Cloud Computing:

  What is the Cloud?

  Cloud Service Models:IaaS (Infrastructure as a Service),PaaS (Platform as a Service),SaaS (Software as a Service)

Topics:

• 1. What is Artificial Intelligence (AI)?

• 2. How AI Works?

• 3. Key Concepts:

  Machine Learning (ML)

  Deep Learning (DL)

• 4. Generative AI:

  What is Generative AI?

  Examples: Large Language Models (LLMs), image generation models.

• 5. AI in Everyday Learning

Topics:

• 1. Customer Relationship Management (CRM)

• 2. Human Resource Management Systems (HRMS)

• 3. Retail & E-Commerce

• 4. Healthcare

Topics:

• What is Data Engineering

• Data Engineer Roles & Responsibilities

• Difference Between ETL Developer & Data Engineer

• Types of Data

• Steaming Vs Batch Data

Topics:

• Cloud Introduction and Azure Basics

• Azure Implementation Models: IaaS, PaaS, SaaS

• Overview of Azure Data Engineer Role

• Understanding Azure Storage Components

• Introduction to Azure ETL & Streaming Components

Topics:

• Azure SQL Server and Database Deployment

• DTU vs. DWU: Understanding Performance Levels

• Managing Firewall Rules and Secure SSMS Connections

• Azure Account and Subscription Management

Topics:

• Azure Resources and Resource Types

• Introduction to Azure Data Factory (ADF) and Azure Synapse Analytics

• Basic Concepts of Data Movement and Processing

Topics:

• Synapse SQL Pools (Data Warehousing) and Massively Parallel Processing (MPP)

• Data Movement with DMS and SQL Pool Management

• Table Creations, Distributions, and Indexing for Performance

Topics:

• Azure Data Factory Pipeline Architecture and Integration Runtime

• Constructing ETL Pipelines with DIU Considerations

• Data Flow Activities and Monitoring

Topics:

• Incremental Data Loading and Handling On-Premise Data Sources

• Advanced ADF Features: Data Flows, ETL Logging, and Performance Tuning

• Implementing CDC with ADF for Real-Time Data Capture

Topics:

• Integrating Spark with Synapse Analytics for Big Data Processing

• Utilizing Python Notebooks and Spark Pools for Data Analysis

• Performance Optimization and Data Transformation Techniques

Topics:

• Security Measures with Azure Active Directory and Role-Based Access Control

• Managing Parameters and Security in Synapse and ADF Pipelines

• Utilizing Azure OpenDatasets and Parquet Files for Advanced Analytics

Topics:

• Azure Storage Essentials: Files, Tables, and Queues

• Introduction to Azure Data Lake Storage Gen2 (ADLS Gen2)

• Configuring and Managing Storage Accounts

• Hierarchical Namespace (HNS) and its Advantages

Topics:

• Managing BLOB Storage: Binary Large Objects Explained

• Utilizing Azure Storage Explorer for Efficient Storage Management

• Directory and File Operations in Azure Data Lake

• Best Practices for Organizing Data in ADLS Gen2

Topics:

• Implementing Security Measures in Azure Data Lake Storage

• Access Control with Shared Access Signatures (SAS) and Access Control Lists (ACLs)

• Role-Based Access Control (RBAC) in Azure Storage

• Encryption, Authentication, and Compliance Features

Topics:

• Strategies for SQL Database Migrations to Azure

• Integrating Azure SQL with Data Lake Storage

• Utilizing Azure Data Factory for Data Movement and Transformation

• Data Migration Tools and Techniques

Topics:

• Advanced Concepts in Azure Table Storage

• Data Replication and Geo-Redundancy Options

• Optimizing Storage Costs and Performance

• Leveraging Data Lake for Big Data Analytics

Topics:

• Fundamentals of Azure Stream Analytics

• Developing Stream Analytics Jobs for Real-Time Insights

• Integrating IoT Devices with Azure for Data Streaming

• Processing and Analyzing Streaming Data

Topics:

• Understanding Azure Event Hubs for Large-Scale Event Processing

• Configuring Event Hubs and Event Hub Namespaces

• Connecting Event Hubs with Azure Stream Analytics

• Patterns for Real-Time and Event-Driven Data Processing

Topics:

• Monitoring Azure Storage and Stream Analytics Resources

• Performance Tuning for Azure Data Services

• Implementing Disaster Recovery Strategies

• Using Azure Monitor and Key Vaults for Operational Excellence

Topics:

• Azure Cloud Overview: Understanding SaaS, PaaS, IaaS

• Introduction to Azure Databricks: Configuration, Compute Resources, and Workspace Usage

• Spark Clusters in Azure Databricks: Configurations, Types, and Resource Management

• Databricks File System (DBFS): Utilizing Files and Tables with Spark

Topics:

• Integrating Python with Spark: PySpark Basics

• Data Loading Techniques: Using PySpark for Data Ingestion and Processing

• Utilizing SQL in Databricks: Creating and Managing Spark Databases and Tables

• Advanced Data Transformation: Working with DataFrames and Spark SQL for Data Analytics

Topics:

• Configuring Azure Data Lake Storage (ADLS) for use with Databricks

• Data Management: Reading and Writing Data to ADLS using PySpark and Scala

• Secure Data Access: Managing Access and Security between Databricks and ADLS

Topics:

• Understanding Databricks Architecture: Driver and Worker Nodes, RDDs, and DAGs

• Building and Monitoring Databricks Jobs: Scheduling, Task Management, and Optimization

• Implementing Delta Lake for Reliable Data Lakes: ACID Transactions and Performance Tuning

Topics:

• Machine Learning Fundamentals in Databricks: Using MLlib for Predictive Modeling

• Data Exploration and Visualization: Leveraging Notebooks for Insights

• Advanced Analytic Techniques: Utilizing Scala and Python for Complex Data Analysis

Topics:

• Databricks Security: Integrating with Azure Active Directory (AD)

• Managing Permissions: Workspace, Notebooks, and Data Security

• Compliance and Data Governance: Best Practices in Databricks Environments

Topics:

• Streaming Data with Databricks: Concepts and Practical Applications

• Integrating Azure Event Hubs with Databricks for Real-Time Analytics

• Processing Live Data Streams: Building and Deploying Stream Analytics Solutions

Topics:

• Automating Workflows with Azure Logic Apps and Databricks

• CI/CD for Databricks: Automation and Version Control Integration

• Deployment Strategies: Best Practices for Production Deployments in Azure

Introduction to Generative AI

Topics:

• 1. What is Generative AI?

• 2. Key Applications:

  Text (ChatGPT, Claude, LLaMA)

  Images (DALL·E, MidJourney, Stable Diffusion)

  Audio (Music Generation, Voice Cloning)

  Code (GitHub Copilot, Cursor)

• 3. Evolution of GenAI:

  Rule-Based → Deep Learning → Transformers

  GANs vs. VAEs vs. LLMs

Topics:

• 1. Effective Prompt Design

  Instruction-Based

  Few-Shot

  Zero-Shot

• 2. Advanced Techniques:

  Chain-of-Thought (CoT) Prompting

  Self-Consistency & Iterative Refinement

• Hands-on

  Optimizing prompts for GPT-4, Claude, LLaMA

Transformer Architecture

Topics:

• 1. Why Transformers?

  Limitations of RNNs/LSTMs

• 2. Key Components

  Self-Attention & Multi-Head Attention

  Encoder-Decoder (BERT vs. GPT)

• 3. Evolution

  BERT → GPT → T5 → Mixture of Experts

• 4. Large Language Models (LLMs)

• 5. Pre-training vs. Fine-tuning

• 6. Popular Architectures

  GPT-4, Claude, Gemini, LLaMA 3

  BERT (Encoder-based) vs. T5 (Text-to-Text)

Introduction to AI Agents

Topics:

• 1. What are AI Agents?

• 2. vs. Traditional AI

• 3. Applications

• AI Agent Frameworks

  CrewAI (Multi-Agent Collaboration)

  n8n (Workflow Automation)

Topics:

• Designing AI Agents

• CrewAI + n8n

  Automating Business Workflows

• Multi-Agent Systems

  Collaboration & Specialization

Real-World Applications

Topics:

• Case Studies:

  AI Customer Support Agents