Business Analyst Training & Certification

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

2. Types of Data

3. Data Storage

4. Data Analysis

5. Data Engineering

6. Data Science

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)

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

2. Human Resource Management Systems (HRMS)

3. Retail & E-Commerce

4. Healthcare

1. Introduction to Business Analysis

Definition and Role of Business Analysts

Business Analysis Frameworks and Certifications

2. Understanding Requirements

Types of Requirements

Elicitation Techniques and Documenting Requirements

Prioritizing and Managing Requirements Changes

3. Business Analysis Tools and Techniques

Overview of tools used in business analysis for various purposes, including elicitation, documentation, and validation.

1. Lifecycle Management

Overview of the Business Analysis Life Cycle

Comparison with Project Management Life Cycle

Planning, Monitoring, and Evaluating Solutions

2. Working with Stakeholders

Stakeholder Identification and Analysis

Communication Planning and Effective Meeting Facilitation

Building Relationships and Negotiating Conflicts

1. Designing Solutions

Principles of Solution Design and Design Thinking

User Interface Design and Prototyping Techniques

Usability Testing and Iterative Design for Solutions

2. Requirements Validation

Techniques for Validating Requirements

Tracing Requirements and Ensuring Alignment with Business Needs.

1. Software Testing

Fundamentals of Testing in Business Analysis

Planning, Design, Execution, and Automation of Tests

Performance Testing and Managing Defects

2. Release Management and Post-Implementation

Planning and Managing Releases

Deployment Execution and Post-Go-Live Support

Ensuring Business Continuity and Fostering Continuous Improvement.

1. Introduction to SQL for data analysis

2. Basic SQL queries for data retrieval

3. Advanced data filtering, sorting, and aggregation in SQL

4. Complex queries with joins and subqueries

5. DML and DDL operations in SQL for data analysts

1. Data cleaning and preparation strategies

2. Transforming and manipulating data with SQL

3. Introduction to Power BI for data analysis

4. Data transformation and cleansing with Power Query

5. Managing complex data structures for analysis

1. Fundamentals of data visualization

2. Creating and customizing visualizations in Power BI

3. Designing interactive dashboards

4. Dashboard design best practices

5. Publishing and sharing insights with Power BI

1. Advanced DAX for complex calculations

2. Time series analysis in Power BI

3. Implementing data security in Power BI projects

4. Automating data refresh and management

5. Integration of Power BI with other services

1. Effective data storytelling with Power BI

2. Collaboration features in Power BI

3. Managing access and report sharing

4. Deploying Power BI Apps for organizations

5. Best practices for BI solutions maintenance

1. Fundamentals and importance of software testing

2. Types of testing: unit, integration, system, acceptance

3. Role of testing in the SDLC

4. Crafting effective test cases and plans

5. Introduction to automated testing

1. Organizing testing phases and activities

2. Managing the defect lifecycle

3. Effective test case design strategies

4. Test prioritization and execution

5. Overview of test management tools

1. Basics of test automation and tool selection

2. Automating test cases for efficiency

3. Popular test automation frameworks

4. Maintaining automated test scripts

5. CI/CD integration with automated tests

1. Introduction to performance testing concepts

2. Load and stress testing techniques

3. Basics of security testing for applications

4. Common security vulnerabilities and tests

5. Tools for performance and security testing

1. Implementing TDD and BDD in projects

2. Advanced automation: data-driven and keyword-driven testing

3. Strategies for mobile and cross-browser testing

4. Exploring non-functional testing: usability, accessibility

5. Upcoming trends in software testing

Topics:

1. Cloud Computing Basics

Understanding cloud computing: Definitions, service models (IaaS, PaaS, SaaS), and deployment models (public, private, hybrid, multicloud).

2. Cloud Service Providers Overview

Introduction to major cloud platforms (e.g., AWS, Azure, Google Cloud), focusing on their core services relevant to developers.

3. Cloud-based Development Environments

Setting up and utilizing cloud-based IDEs and development tools to streamline development workflows.

4. Deploying Applications on the Cloud

Basic concepts of application deployment on the cloud, including containerization basics with Docker and initial orchestration concepts.

Topics:

1. Understanding DevOps

The philosophy, practices, and benefits of DevOps in modern software development, emphasizing collaboration, automation, and integration.

2. Version Control with Git

Deep dive into using Git for source code management, including best practices for branches, commits, merges, and pull requests.

3. Continuous Integration/Continuous Deployment (CI/CD)

Introduction to CI/CD pipelines, overview of tools ( GitHub Actions), and setting up basic pipelines for automated testing and deployment.

4. Monitoring and Feedback

Basics of application monitoring, log management, and utilizing feedback for continuous improvement.

Topics:

1. Containers and Docker

Introduction to containers, Docker fundamentals, creating Docker images, and container management basics.

2. Managing Application Infrastructure

Basic strategies for managing infrastructure as part of the application lifecycle, including introduction to infrastructure as code (IaC) principles.

Topics:

1. Scalable Application Design

Principles of designing scalable applications that can grow with user demand, focusing on microservices architecture and stateless application design.

2. Cloud-native Services for Developers

Leveraging cloud-native services (e.g., AWS Lambda, Azure Functions, Google Cloud Run) for building and deploying applications.

3. Databases in the Cloud

Overview of cloud database services (SQL and NoSQL) and integrating them into web applications.

4. Security Basics in Cloud and DevOps

Understanding security best practices in cloud environments and throughout the DevOps pipeline.

Topics:

1. Agile and Scrum Methodologies

Incorporating Agile and Scrum practices into team collaboration for efficient project management.

2. Code Review and Collaboration Tools

Utilizing code review processes and collaboration tools GitHub, to enhance code quality and team productivity.

3. Automation in Development

Exploring automation beyond CI/CD, including automated testing frameworks, database migrations, and environment setup.

4. DevOps Culture and Best Practices

Cultivating a DevOps culture within teams, embracing continuous learning, and adopting industry best practices for sustainable development.

Introduction to Generative AI

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

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

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

1. What are AI Agents?

2. vs. Traditional AI:

3. Applications:

AI Agent Frameworks

1. CrewAI (Multi-Agent Collaboration):

2. n8n (Workflow Automation):

Designing AI Agents

CrewAI + n8n: Automating Business Workflows

Multi-Agent Systems: Collaboration & Specialization

Real-World Applications

Case Studies:

AI Customer Support Agents