- Regular Batch: 3 Months (130 Hours total | 2 Hours/day | Monday to Friday)
- Weekend Batch: 3.5 Months
- Fast Track: 2 Months
🏆 Program Highlights
- End-to-End Data Analytics & BI
- Snowflake Cloud Data Warehouse
- PySpark for Large-Scale Data Wrangling
- dbt (Data Build Tool) Transformations
- Power BI Enterprise Dashboarding & DAX
- Advanced ETL & ELT Pipelines
- Real-Time Insights & Streaming Analytics
- Cloud & DevOps Basics
- Query & Dashboard Performance Optimization
- Capstone Project & Rigorous Interview Preparation
🧰 Tools & Technologies Covered
- Languages: Python, SQL
- Data Processing: PySpark
- Cloud Warehousing: Snowflake
- Data Transformation: dbt (Data Build Tool)
- Business Intelligence & Viz: Power BI Desktop & Service
- Real-time Ingestion: Kafka Basics
- Version Control & Environments: Git & GitHub, VS Code, Jupyter Notebook
- Cloud Infrastructure: AWS / Azure
🛠️ Modules Covered (Complete Syllabus)
Module 1: Introduction to Data Analytics
- What is Data Analytics?
- Role of a Data Analyst vs. Data Engineer
- The Data Analytics Lifecycle
- OLTP vs. OLAP systems
- Modern Data Warehouse Concepts
- Data Lake vs. Data Warehouse vs. Lakehouse
- ETL vs. ELT Architectural Patterns
- Batch Processing vs. Real-Time Streaming Insights
Module 2: Advanced SQL for Data Analytics
- Advanced SQL Queries & Subqueries
- Complex Multi-table Joins
- Common Table Expressions (CTEs)
- Window Functions (RANK, DENSE_RANK, LEAD, LAG)
- Analytical & Aggregation Functions
- Stored Procedures & Functions
- Views & Materialized Views
- Query Optimization, Execution Plans & Performance Tuning
Module 3: Python Basics for Data Analytics
- Python Fundamentals & Environment Setup
- Core Data Types, Variables & Data Structures
- Control Flow, Conditional Statements & Loops
- Custom Functions & Local/Global Scope
- File Handling (CSV, Excel) & Exception Handling
- Working with Web APIs & Requests Library
- Parsing and Manipulating JSON Data
Module 4: PySpark Fundamentals for Analysts
- Introduction to Big Data & Apache Spark Architecture
- RDD vs. DataFrame vs. Dataset
- Configuring the SparkSession
- Data Transformations & Actions
- Understanding Lazy Evaluation & Directed Acyclic Graphs (DAGs)
- Spark SQL for Running SQL Queries over Big Data
Module 5: Advanced PySpark & Data Wrangling
- Large-Scale Data Cleaning & Schema Enforcement
- Handling Missing Values and NULLs
- PySpark Window Functions & Analytical Functions
- Writing User Defined Functions (UDFs)
- Optimizing Big Data Joins & Partitioning Strategies
- Caching & Persistence Mechanisms
- Spark Streaming Basics for Analytics
Module 6: Snowflake Cloud Warehouse Fundamentals
- Snowflake Architecture (Storage, Compute, Services)
- Creating & Scaling Virtual Warehouses
- Databases, Schemas, and Specialized Table Types
- Micro-partitions, Data Clustering, and Data Pruning
- Time Travel & Fail-safe Mechanisms
- Zero-Copy Cloning for Development and Testing
- Secure Data Sharing Architecture
Module 7: Data Loading & Ingestion in Snowflake
- Internal & External Stages (S3 / Azure Blob)
- File Format Objects & Error Handling Configuration
- Bulk Loading using COPY INTO Command
- Automated Near Real-time Loading with Snowpipe
- Incremental Loading Best Practices
- Processing Semi-Structured Data (JSON & Parquet Parsing)
Module 8: dbt (Data Build Tool) Transformations
- Introduction to dbt & Analytics Engineering Patterns
- dbt Architecture and Workflow Setup
- Writing and Structuring dbt Models
- Materializations (Views, Tables, Incremental, Ephemeral)
- Implementing Seeds & Snapshots for Historic Tracking (SCD Type 2)
- Configuring dbt Schema Tests & Data Quality Audits
- Advanced dbt Macros & Jinja Templating
- Generating Auto-Documented Data Lineage
- ELT Hands-on Pipeline Project using dbt with Snowflake
Module 9: PySpark + Snowflake Analytics Integration
- Configuring the Snowflake Connector for Spark
- Reading Large-Scale Snowflake Tables into PySpark DataFrames
- Writing Processed DataFrames back to Snowflake Warehouses
- Building Scalable End-to-End Analytics Workflows
- Data Migration and Aggregation Pipelines
Module 10: Advanced Snowflake for Data Analysts
- Snowflake Streams & Tasks for Workflow Automation
- Dynamic Tables for Declarative Data Pipelines
- Materialized Views for Performance Boosts
- Building Change Data Capture (CDC) Analytics Pipelines
- Analyzing Query Profiles to Identify Bottlenecks
- Warehouse Scaling Strategies & Cloud Cost Optimization
- Role-Based Access Control (RBAC) & Data Security
Module 11: Business Intelligence with Power BI (Replaced Airflow)
- Power BI Architecture & Desktop Installation
- Connecting to Data Sources (Excel, SQL Servers, Snowflake DirectQuery)
- Data Transformation in Power Query (M Language, Merging, Pivoting)
- Data Modeling: Star Schema, Snowflake Schema, and Relationships
- Introduction to DAX (Calculated Columns, Measures, Calculated Tables)
- Advanced DAX (CALCULATE, Time Intelligence Functions: YTD, MTD, SamePeriodLastYear)
- Creating Interactive Visualizations (KPI Cards, Matrix, Advanced Charts)
- Implementing Filters, Slicers, Bookmarks, and Tooltips
- Power BI Service: Workspaces, Dashboards, Gateways, and Scheduled Refreshes
- Row-Level Security (RLS) for Personalized Visual Data Controls
Module 12: Real-Time Data Analytics
- Streaming Concepts & Business Use Cases
- Apache Kafka Basics (Topics, Producers, Consumers)
- Consuming Streams using Spark Streaming
- Building Near Real-Time Interactive Analytics Dashboards
- Snowpipe Streaming Integrations
Module 13: Cloud & DevOps Basics for Analysts
- Azure/AWS Management Console Fundamentals
- Cloud Storage Services (Amazon S3 / Azure Blob Storage)
- Continuous Integration/Continuous Deployment (CI/CD) Basics for Analytics
- Version Control with Git & GitHub (Branching, Pull Requests)
- Automating Dashboard Refreshes & Pipeline Monitoring
Module 14: Data Modeling & Analytical Design
- Star Schema & Snowflake Schema Design Principles
- Fact Tables vs. Dimension Tables (Role-playing, Conformed Dimensions)
- Handling Change via Slowly Changing Dimensions (SCD Type 1, 2, 3)
- Designing Tailored Data Marts for Separate Business Units
Module 15: Performance Optimization Techniques
- Spark Performance Tuning & Shuffle Optimization
- Snowflake Query Performance Analysis & Tuning
- Enterprise Power BI Dashboard Optimization (Reducing DAX Overhead, Import vs. DirectQuery)
- Data Partitioning, Compression, and File Size Optimization
- Leveraging Tiered Caching Mechanisms
💼 Career Opportunities & Roles
- Data Analyst
- Business Intelligence (BI) Developer
- Power BI Developer
- Analytics Engineer
- Snowflake Data Analyst
- Data Insights Specialist
- Reporting Systems Analyst