AI POWERED DATA ANALYTICS

Data Analytics (In simple words):
Data Analytics is the process of collecting, cleaning, and analyzing data to find useful insights that help in making
better decisions.

In simple terms:
“Turning raw data into meaningful information for decision-making.”

Tools Used in This Course
In this AI-Powered Data Analytics course, you will use tools like:

• SQL Server (data extraction & querying)
• Power BI (dashboard & visualization)
• Python (analysis & automation)
• Advanced Excel (data handling & reporting)
• Tableau (basic visualization)
• AI Tools (to automate and enhance analytics workflows)

Target Audience
This course is suitable for:

• Freshers starting a career in IT
• Students interested in Data Analytics, BI, or Software Development
• Working professionals moving into Data Analytics or BI roles
• Anyone who wants to learn Data Analytics from scratch

MICROSOFT SQL SERVER (SQL + T-SQL)
Overview
Microsoft SQL Server is one of the most widely used Relational Database Management Systems (RDBMS) in the
Microsoft ecosystem.
This module covers the fundamentals of SQL Server, focusing on querying, managing, and analyzing data using
SQL and T-SQL. Learners will understand how to work with relational databases and perform efficient data
operations. It prepares learners for real-world data extraction and transformation tasks.

Introduction to Basic Database Concepts
Introduction to SQL Server
SQL Commands and Constraints
Working with Clauses
SQL Server Functions

• Aggregate Functions
• String Functions
• Date Functions
• Null Functions
• Working with Window Functions
• Joins
• Set Operators
• Iterative Statements
• CTE’s and Sub Queries
• Views
• T-SQL
• Stored Procedures
• UDF(User Defined Functions)
• Triggers
• Indexes
• Query optimization techniques
• Normalizations
• Data warehouse concepts

POWER BI DESKTOP & MICROSOFT FABRIC
Introduction
This module provides hands-on experience in Power BI for data transformation, modeling, and visualization.
Learners will build interactive dashboards, write DAX, and understand modern BI practices using Microsoft Fabric.
It focuses on delivering insights through effective reporting and analytics.
Power BI Desktop:

• o Introduction and overview
• o Power BI Desktop Components
• o Building Blocks of Power BI
• o Data Connectivity Modes
• o Different Views in Power BI Desktop

Power Query Editor (ETL)

• Introduction and overview
• Table Row and Column Transformations / Properties
• Combine Queries
• Joins in Power Query
• Parameters
• Adding New Columns

Data Model Introduction (Semantic Model)

• Modelling Concepts
• Cardinality and Relationships
• Cross Filter Direction
• Semantic Model Management

DAX (Data Analysis Expressions)

• Core Concepts
• Row Context, Filter Context, Context Transition
• Operators
• Aggregation Functions
• Text Functions
• Filter Functions
• Calendar Functions
• Date & Time Functions
• Logical Functions
• Time Intelligence
• Information Functions
• Table Functions
• Relationship Functions
• Window Functions
• User-Defined Functions

DAX Query View
TMDL View
Power BI Report Development (Visualization & Presentation)
Introduction to Desktop Tabs and Panes
Introduction to Power BI Native Visuals:

• Bar & Column Charts, Line & Area Charts, Combo Charts
• Pie & Donut Charts
• Maps
• Tables & Matrices
• KPI Visuals
• Distribution & Rank
• Slicers
• AI Visuals
• Others

Report Development
Performance Optimization techniques
Row-Level Security (RLS)
Incremental Refresh
Deployment Process
Microsoft Fabric for Power BI

• Introduction
• Different Licences Available
• Collaboration
• Data Gateway
• Sharing Content
• Fabric Activities
• Lakehouse
• Warehouse
• Semantic Models
• Schedule Refresh
• Handling RLS

Data Storytelling & Business Communication

• Data Storytelling Basics
• Business Understanding
• Structuring Insights
• Data Visualization
• Business Communication
• Dashboard & Presentation
• AI in Storytelling

PYTHON FOR DATA ANALYTICS & PROGRAMMING
 Introduction
This module introduces Python programming for data analysis, covering core concepts, data handling, and visualization. Learners will use libraries like Pandas, NumPy, and Matplotlib to analyze and process data. It enables practical, real-world data analysis using Python.
Python Fundamentals:

• Data Types
• Operators in Python
• Conditional Statements
• Loops

Data Structures
Functions in Python
Object-Oriented Programming (OOP)
Exception Handling
File Handling
Python Libraries for Data Analysis & Visualization

• Matplotlib
• NumPy
• Pandas
• Plotly
• Tkinter

Exploratory Data Analysis (EDA)

• EDA Fundamentals
• Univariate Analysis
• Bivariate Analysis
• Multivariate Analysis
• Correlation Analysis
• Data Quality Checks
• EDA Tools

Statistics for Data Analytics

• Descriptive Statistics
• Probability Fundamentals
• Distributions
• Sampling Techniques
• Inferential Statistics
• Hypothesis Testing
• Correlation vs Causation
• Regression Basics
• Business Applications

DATA ANALYSIS IN MICROSOFT EXCEL
Introduction
This module focuses on using Excel for data cleaning, analysis, and reporting. Learners will work with formulas,
pivot tables, and charts to generate insights. It builds essential skills for business reporting and data analysis.
Introduction to Microsoft Excel
Data Cleaning and Shaping
Excel Formula Fundamentals

• R1C1 referencing in Excel
• Relative and absolute references
• Manual and automatic formula calculations
• Basic formula writing best practices

Excel Functions
Aggregation Functions

• Text Functions
• Logical Functions
• Date & Time Functions
• Lookup Functions
• Math & Trigonometric Functions
• Statistical Functions

Conditional Formatting
Pivot Tables, Pivot Charts, and Slicers
Data Visualization in Excel
Power Query
Dashboard Design in Excel
Power Pivot & Data Model
TABLEAU IN DATA ANALYTICS
Introduction:
This module introduces Tableau for data visualization and dashboard creation. Learners will connect, analyze, and
present data using interactive visuals. It helps in building clear and impactful data stories.

• Introduction to Tableau
• Tableau Architecture
• Power BI vs Tableau
• Tableau Products
• Tableau Interface
• Main Components of Tableau
• Data Connection
• Organizing and Simplifying Data
• Calculated Fields
• Data Visualization in Tableau
• Filters & Advanced Options
• Data Combining Techniques
• Dashboards
• Actions in Tableau

AI IN DATA ANALYTICS
Introduction
This module explores how AI tools can enhance data analytics workflows. Learners will use tools like ChatGPT,
Claude and Copilot to automate tasks, generate insights, and improve productivity. It focuses on applying AI
across the analytics lifecycle.

Introduction to Generative AI
Generative AI for Data Analysts
Prompt Engineering for Analysts
Validation of AI Outputs
Data Privacy & Governance
A typical analyst workflow:

1. Requirement → ChatGPT / Claude
2. SQL → SQL Copilot
3. Python → GithubCopilot + ChatGPT
4. Insights → ChatGPT
5. Dashboard → Power BI Copilot

Traditional vs AI Analytics
Analytics Lifecycle with AI

• AI in Data Collection
• AI in Data Cleaning
• AI in Transformation
• AI in Analysis
• AI in Visualization
• AI in Reporting

Real-Life Use Cases

• � Shopping apps → personalized product recommendations
• �️ E-commerce platforms → smart product suggestions based on user behavior
• � Hospitals → disease prediction using patient data
• � Healthcare → early diagnosis through AI models
• � Digital platforms → personalized user experience optimization
• � Apps → user behavior tracking and personalization

CAPSTONE PROJECTS

• Retail Sales & Customer Insights
• Banking Loan Risk Analysis
• Telecom Customer Churn Analysis
• Social Media Sentiment Analysis
• E-Commerce Funnel Analysis
• Sales Data Analysis Dashboard
• Healthcare system
• Weather Forecasting

End-to-End Project Workflow
Real-World Data Analytics Lifecycle

• Problem understanding
• Data extraction (SQL)
• Data cleaning (Python/Power Query)
• Modeling
• Visualization
• Insight generation
• Stakeholder communication