Master Data Visualization with Our Power BI Training

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

Overview of Analytics and Power BI Tools Suite

Career Opportunities and Job Roles in Power BI

Power BI Data Analyst (PL 300) Certification Overview

Introduction to AI Visuals and Features in Power BI

Understanding the Power BI Ecosystem and Architecture

Data Sources and Types for Power BI Reporting

Power BI Design Tools and Desktop Tool Installation

Exploring Power BI Desktop Interface: Data View, Report View, and Canvas

Visual Interaction Techniques in Reports

Using Slicers for Dynamic Report Filtering

Managing Report Pages and Visual Sync Limitations

Implementing Grouping and Binning in Reports

Creating and Utilizing Hierarchies for Drill-Down Reports

Introduction to Power Query M Language

Basic Data Transformations in Power Query

Understanding Query Duplication and Grouping

Overview of Power BI Cloud Components and App Workspaces

Creating and Managing Reports and Dashboards in Power BI Cloud

Sharing, Subscribing, and Exporting Reports in Power BI Cloud

Understanding the Importance of DAX in Power BI

Learning Basic DAX Syntax, Data Types, and Contexts

Simple Measures and Calculations with DAX

Accessing Big Data Sources and Azure Databases

Advanced Filtering Techniques and Utilizing Bookmarks

Implementing Various Chart Types and Map Visuals

Deep Dive into Advanced Data Cleaning and Preparation Techniques

Implementing Parameter Queries for Dynamic Data Loads

Creating and Managing Parameters in Power Query

Configuring and Managing Gateways for Data Refresh

Utilizing Workbooks and Excel Online with Power BI Cloud

Creating and Managing Power BI Apps

Implementing Quick Measures and Advanced Calculations

Data Modeling and Relationship Management in DAX

Mastering Variables and Dynamic Expressions in DAX

Advanced DAX Functions for Time Intelligence

Implementing Row Level Security (RLS) with DAX

Utilizing DAX for Custom Analytics and Reporting

Configuring Power BI Report Server

Understanding Power BI Administration and AI Features

Managing Security and Administration in Power BI

Topics:

Introduction to Excel: Interface, Basic Operations, and Managing Worksheets

Fundamental Data Operations: Sorting, Filtering, and Conditional Formatting

Basic Formulas and Functions: Sum, Average, Logical Functions (IF, AND, OR), and Text Functions (LEFT, RIGHT, CONCATENATE)

Topics:

Advanced Data Management: Data Validation, Advanced Filtering, and Named Ranges

Creating and Managing Tables for Efficient Data Analysis

Introduction to Data Visualization: Creating and Customizing Charts (Bar, Line, Pie), and Using Sparklines

Topics:

Comprehensive Guide to PivotTables: Creating, Customizing, Slicers, and Timelines

Basic to Advanced PivotTable Techniques: Grouping Data, Calculated Fields

Data Cleanup Techniques: Removing Duplicates, Text to Columns, Flash Fill

Topics:

Mastering Lookup Functions: VLOOKUP, HLOOKUP, XLOOKUP

Introduction to Power Query for Data Transformation and Cleaning

Power Pivot and DAX Basics: Creating Data Models, Introduction to DAX Formulas for Data Analysis

Topics:

Automating Tasks with Macros and an Introduction to VBA for Custom Functions

Advanced Chart Techniques and Creating Interactive Dashboards

Workbook Protection, Sharing Workbooks for Collaboration, Documenting and Auditing Workbooks

Topics:

Understanding Table Relationships: Primary keys, foreign keys, and the importance of relationships in databases.

Join Operations: `INNER JOIN`, `LEFT JOIN`, `RIGHT JOIN`, and `FULL JOIN`.

Subqueries and Nested Queries: Using subqueries in the `SELECT`, `FROM`, and `WHERE` clauses.

Aggregating Data: Using `GROUP BY` and aggregate functions (`COUNT`, `SUM`, `AVG`, `MIN`, `MAX`).

Topics:

Data Manipulation Commands: `INSERT`, `UPDATE`, `DELETE`.

Managing Tables: Creating and altering tables (`CREATE TABLE`, `ALTER TABLE`, `DROP TABLE`).

Advanced Filtering Techniques: Using `LIKE`, `IN`, `BETWEEN`, and wildcard characters.

Working with Dates and Times: Understanding and manipulating date and time data.

Topics:

Advanced SQL Functions: String functions, mathematical functions, and date functions.

Window Functions: Overviews of `ROW_NUMBER`, `RANK`, `DENSE_RANK`, `LEAD`, `LAG`, and their applications.

Query Performance Optimization: Indexes, query planning, and execution paths.

Common Table Expressions (CTEs): Writing cleaner and more readable queries with `WITH` clause.

Topics:

Analytical SQL for Reporting: Building complex queries to answer analytical questions.

Pivoting Data: Transforming rows to columns (`PIVOT`) and columns to rows (`UNPIVOT`).

Data Warehousing Concepts: Introduction to data warehousing practices and how they apply to SQL querying.

Integrating SQL with Data Analysis Tools: Connecting SQL databases with tools like Excel, Power BI, and Python for deeper data analysis.

### Topics:

1. Introduction to Python

Overview of Python’s history, key features, and comparison with other languages.

Setting up the Python environment, writing your first program. 2. Core Programming Concepts

Variables, data types, conditional statements, loops, control flow.

Introduction to strings, string manipulation, and basic functions.

Topics:

1. Deep Dive into Collections

Understanding lists, tuples, dictionaries, sets, and frozen sets.

Functions, methods, and comprehensions for collections.

2. Functional Programming in Python

Exploring function arguments, anonymous functions, and special functions (map, reduce, filter).

3. Object-Oriented Programming (OOP)

Classes, objects, constructors, destructors, inheritance, polymorphism.

Encapsulation, data hiding, magic methods, and operator overloading.

Topics:

1. Mastering Exception Handling

Exception handling mechanisms, try & finally clauses, user-defined exceptions.

2. File Handling Essentials

Basics of file operations, handling Excel and CSV files.

3. Database Programming

Introduction to database connections and operations with MySQL.

Topics:

1. Getting Started with Flask

Setting up Flask, creating simple applications, routing, and middleware.

2. Exploring Django

Introduction to Django, MVC model, views, URL mapping.

Topics:

1. Automation and Scripting

Enhancing file handling, database automation, and web scraping with BeautifulSoup.

2. GUI Development with TKinter

Basics of TKinter for developing desktop applications.

3. Version Control with Git

Managing projects with Git, understanding repository management, commits, merging, and basic Git commands.

Topics:

Cloud Computing Fundamentals: Overview of cloud service models (IaaS, PaaS, SaaS) and deployment models (public, private, hybrid).

Basics of DevOps: Understanding the DevOps culture, practices, and its significance in cloud environments.

Data on the Cloud: Exploring cloud storage solutions, databases, and big data services provided by major cloud providers (AWS, Azure, Google Cloud).

Introduction to Infrastructure as Code (IaC): Concepts and tools for managing infrastructure through code.

Topics:

Cloud Storage Solutions: Differences between object storage, file storage, and block storage. Use cases for each.

Cloud Databases: Overview of relational and NoSQL database services in the cloud (e.g., AWS RDS, Azure SQL Database, Google Cloud Firestore).

Data Warehousing and Big Data Solutions: Introduction to cloud-based data warehousing services (e.g., Amazon Redshift, Google BigQuery, Azure Synapse Analytics).

Data Migration to Cloud: Strategies and tools for migrating data to cloud environments.

Topics:

Automated Data Pipelines: Designing and implementing automated data pipelines using cloud services.

Continuous Integration and Continuous Delivery (CI/CD) for Data: Applying CI/CD practices to data pipeline development, including version control, testing, and deployment strategies.

Monitoring and Logging: Tools and practices for monitoring cloud resources and data pipelines, understanding logs and metrics for troubleshooting.

Infrastructure as Code (IaC) for Data Systems: Using IaC tools (e.g., Terraform, CloudFormation) to provision and manage cloud data infrastructure.

Topics:

Serverless Data Processing: Leveraging serverless architectures for data processing tasks (e.g., AWS Lambda, Azure Functions).

Containerization and Data Services: Using containers (e.g., Docker, Kubernetes) for deploying and scaling data applications and services in the cloud.

Machine Learning and AI in the Cloud: Introduction to cloud-based machine learning services and integrating AI capabilities into data pipelines.

Data Analytics and Visualization: Tools and services for analyzing and visualizing data directly in the cloud (e.g., Amazon QuickSight, Google Data Studio, Power BI on Azure).

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