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Course Details

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This Data Analytics Certification Program is designed to help learners develop strong analytical thinking, hands-on technical skills, and real-world project experience.

The course focuses on practical learning, industry-relevant tools, and real business use cases rather than just theory. By the end of the program, learners will be confident in analyzing data, building dashboards, and presenting insights that support business decisions.

This online program is suitable for beginners, working professionals, and career switchers who want to build a future-ready skill set in analytics.

Course Curriculum

    • Introduction to SQL Server (RDBMS)
    • Database concepts, Tables, Rows & Columns
    • SQL Server overview
    • Data Types: Numeric, Character, Date & Time
    • SQL (Structured Query Language)
    • DDL, DML, DCL, TCL concepts
  • DDL Commands
    • CREATE (Database, Table, View)
    • ALTER
    • DROP
    • TRUNCATE
    DML Commands
    • INSERT
    • UPDATE
    • DELETE
    • SELECT
    DELETE vs TRUNCATE vs DROP
    • WHERE clause
    • LIKE & Wildcards
    • IN, BETWEEN
    • GROUP BY
    • HAVING clause
    • CASE expression
    • SELECT TOP (Number / Percent)
    • Views
    • Difference between Table & View
    • Temporary Tables
    • Table Variables
    SQL Set Operators
    • UNION vs UNION ALL
    • INTERSECT
    • EXCEPT
    • INNER JOIN
    • LEFT JOIN
    • RIGHT JOIN
    • FULL JOIN
    • CROSS JOIN
    • Self Join
    • NOT NULL
    • UNIQUE
    • PRIMARY KEY
    • FOREIGN KEY
    • CHECK
    • DEFAULT
    Indexes & Performance
    • Index concepts
    • Clustered Index
    • Non-Clustered Index
    • Unique Index
    • Columnstore Index
    • Index Seek vs Index Scan
    • Common Table Expressions (CTE)
      • Recursive CTE
      • Non-recursive CTE
    • Subqueries
      • Correlated Subqueries
      • Non-Correlated Subqueries
      • Single-row & Multi-row subqueries
    • Stored Procedures
      • Create & Execute
      • Parameters
      • SET NOCOUNT ON
    • Functions
      • User Defined Functions (Scalar & Table Valued)
      • System Functions (String, Date, Ranking, Conversion)
    • Triggers
      • DML Triggers
      • DDL Triggers
      • Real-time use cases
    • Introduction to Microsoft Azure
    • What is Azure Data Factory?
    • ADF Architecture & Components
    • ADF vs SSIS
    • Use cases of ADF
    • Azure Resource Group
    • Azure Subscription & Pricing overview
    • Azure Portal navigation
    • Creating Azure Data Factory
    ADF Core Components
    • Pipelines
    • Activities
    • Datasets
    • Linked Services
    • Integration Runtime (Azure, Self-hosted)
  • Data Movement
    • Copy Activity
    • Source & Sink concepts
    • Azure Blob Storage
    • Azure Data Lake Gen2
    • SQL Server
    • Azure SQL Database
    • REST / HTTP
    • File-based sources
    Data Transformation
    • Mapping Data Flows
    • Select, Filter
    • Derived Column
    • Aggregate
    • Join, Lookup
    • Conditional Split
    • Debug & performance tuning
  • Control Flow Activities
    • Execute Pipeline
    • If Condition
    • Switch
    • ForEach
    • Until
    • Wait & Web activities
    Parameters & Variables
    • Pipeline parameters
    • Dataset parameters
    • Global parameters
    • Variables (Set & Append)
    • Dynamic expressions
    Triggers & Scheduling
    • Schedule Trigger
    • Tumbling Window Trigger
    • Event-based Trigger
    • Trigger dependencies
    • Watermark concept
    • Incremental data loading
    • Delta load design patterns
    • File-based incremental loads
    Error Handling & Logging
    • Try-Catch pattern in ADF
    • On Failure & On Success paths
    • Logging using pipelines
    • Retry & timeout policies
    Monitoring & Debugging
    • ADF Monitoring dashboard
    • Pipeline run history
    • Activity run analysis
    • Alerts & diagnostics
    Security & Access Management
    • Azure RBAC
    • Managed Identity
    • Key Vault integration
    • Secure credentials & secrets
    • Git integration in ADF
    • Collaboration using Git
    • Publish vs Debug mode
    • ARM templates overview
    Performance Optimization
    • Integration Runtime performance
    • Parallelism & batch size
    • Data Flow optimization
    • Cost optimization best practices
    Real-Time Scenarios
    • End-to-end ADF project
    • SQL → ADLS → Synapse use case
    • API to Data Lake ingestion
    • Folder-based ingestion
    • Business Intelligence overview
    • Power BI Desktop & Service
    • Installation and setup
    • Power BI architecture and components
    Data Connections
    • Excel, CSV, Text, Folder sources
    • SQL Server, SSAS, SharePoint, Web
    • Import vs DirectQuery vs Live Connection
  • Power Query
    • Data cleaning and transformation
    • Pivot, Unpivot, Transpose
    • Append vs Merge
    • Types of joins
    • Manage Parameters
    • Query Folding
    Data Modeling
    • Relationships and cardinality
    • Fact & Dimension tables
    • Star and Snowflake schema
    • Many-to-Many & Bridge tables
    Data Visualization
    • Charts, Tables, Matrix, KPIs
    • Filters and Slicers
    • Drill Down & Drill Through
    • Tooltips
    • Field Parameters
    • Bookmarks & Navigation
  • DAX
    • Calculated Columns, Measures, Tables
    • Row Context vs Filter Context
    • Variables
    • Aggregation, Logical, Filter functions
    • Text functions
    • Lookup and Relationship functions
    • Calendar tables
    • YTD, QTD, MTD
    • TOPN, RANKX
    • SUMMARIZE, GROUPBY
    • Performance optimization
    Power BI Service
    • Workspaces
    • Reports vs Dashboards
    • Apps
    • Pro vs Premium licensing
    Security & Enterprise
    • Row Level Security (Static & Dynamic)
    • Sharing and access control
    • Power BI Gateway
    • Scheduled refresh

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    Program Structure

    Training Phase

    • Duration: 12 Weeks
    • Mode: Instructor-led with hands-on practice
    • Focus: Skill-building, projects, and real-world application

     

     

    Internship Phase

    • Duration: 12 Weeks (for selected candidates)
    • Stipend: ₹5,000 per month
    • Work on live or simulated industry projects
    • Mentorship and performance evaluation included

    Internship selection is based on performance during the training phase.

    Who This Program Is For

    • Students and fresh graduates
    • Working professionals seeking upskilling
    • Career switchers entering analytics roles
    • Anyone looking to build strong data analytics foundations

    No prior advanced technical background is required. A willingness to learn is enough.

    Skills You Will Build

    • Query and analyze data using SQL
    • Work with cloud-scale data using Azure Databricks
    • Transform and process data using Apache Spark concepts
    • Create insightful dashboards using Power BI
    • Understand how analytics is applied in real business scenarios

    What This Program Includes

    • Instructor-led, structured training
    • Hands-on practical sessions
    • Real-world datasets and use cases
    • End-to-end analytics workflow exposure
    • Guided learning with mentor support

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