🎓
Rahul placed successfully!

Secured a role in AI & GenAI at Tech Mahindra

📊 Enterprise Data & Cloud Analytics Track

Data Science & Big Data Engineering

Master the complete enterprise data lifecycle. Build end-to-end predictive machine learning pipelines, execute distributed computing with Apache Spark, design Snowflake cloud data lakes, and stream real-time telemetry with Apache Kafka.

4 Months Comprehensive Track
4+ Big Data Labs Terabyte-Scale Pipelines
100% Placement Support
Global Cert Data Scientist Credential

Who Is This Program Ideal For?

Structured for professionals and freshers looking to build elite decision-making and quantitative analytics capabilities.

📊

Aspiring Data Analysts

Freshers or career switchers aiming for high-growth Business Analyst, Product Analyst, or SQL Developer roles.

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MIS & Excel Pros

Professionals hitting limits with spreadsheets who want to transition into database querying and scalable BI pipelines.

🚀

Product & Growth Leads

Managers and marketers who want to query production SQL databases directly to evaluate retention, CAC, and LTV.

What You Will Master

Designed around practical enterprise problem-solving using SQL, advanced data modeling, and business KPI architecture.

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Advanced Analytical SQL

Master Window functions (RANK, DENSE_RANK, LEAD/LAG), CTEs, Recursive queries, and subquery optimization.

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Business KPI Architecture

Calculate Monthly Recurring Revenue (MRR), Churn Rates, User Cohort Retention, and Marketing Attribution.

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Relational Data Modeling

Design Kimball dimensional models, Star/Snowflake schemas, Fact/Dimension tables, and primary/foreign key constraints.

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Executive BI Storytelling

Connect SQL databases directly to Power BI and Tableau for interactive parameter controls and drill-down reporting.

Curriculum & Weekly Syllabus

Comprehensive modular breakdown tailored for real-world enterprise analytics.

Module 1: Relational Databases & Core SQL Architecture ▾
  • RDBMS Fundamentals: PostgreSQL & MySQL architecture and table constraints
  • DDL, DML, DQL: Writing robust SELECT, WHERE, GROUP BY, and HAVING statements
  • Mastering JOINS: INNER, LEFT, RIGHT, FULL OUTER, CROSS, and SELF JOINS
  • Aggregate functions, conditional logic with CASE WHEN, and NULL handling (COALESCE/NULLIF)
Module 2: Advanced SQL: Window Functions & CTEs ▾
  • Window Functions: ROW_NUMBER(), RANK(), DENSE_RANK(), and NTILE() partitioning
  • Value Windowing: LEAD(), LAG(), FIRST_VALUE(), and running rolling moving averages
  • Common Table Expressions (CTEs) vs. Subqueries vs. Temporary Tables
  • String parsing, Regex pattern matching, and complex Date/Time interval arithmetic
Module 3: Business Analytics & Product Metrics ▾
  • Cohort Analysis: Calculating retention rates and user lifecycle churn tables
  • RFM Analysis (Recency, Frequency, Monetary) for customer lifetime segmentation
  • Funnel Analysis: Tracking multi-step user drop-offs from signup to checkout conversion
  • A/B Testing Analytics: SQL calculations for p-values, hypothesis tests, and statistical power
Module 4: Performance Optimization & Query Tuning ▾
  • Understanding Query Execution Plans (EXPLAIN & EXPLAIN ANALYZE)
  • Indexing strategies: B-Tree, Hash, Composite Indexes, and covering indexes
  • Partitioning strategies: Range, List, and Hash table partitioning for million-row datasets
  • Writing stored procedures, user-defined functions (UDFs), and database triggers
Module 5: Executive BI Dashboards (Power BI / Tableau) ▾
  • Connecting live database servers to Power BI & Tableau via DirectQuery
  • Writing DAX measures, calculated columns, and dimensional star modeling
  • Building C-level KPI summary boards with real-time refresh automations
  • Publishing and sharing secure enterprise dashboard workspaces

Real-World Business Capstones

Solve actual production business problems and present clean SQL scripts and executive dashboards.

Capstone 01

SaaS Churn & MRR Waterfall Analysis

Write advanced CTEs tracking expansion, contraction, and net churn revenue across 100k+ customer subscription records.

Capstone 02

E-Commerce Multi-Touch Attribution

Construct SQL models attributing marketing spend across first-click, last-click, and linear touchpoints for digital ad campaigns.

Capstone 03

Supply Chain Inventory Optimization

Build automated stock replenishment queries calculating reorder points, safety stock, and vendor delivery lead-time variances.

Capstone 04

Executive Financial Health Dashboard

Deploy a live Power BI report integrated with PostgreSQL presenting margin analysis, EBITDA tracking, and departmental burn rates.

Learner Success & Reviews

See how our alumni transitioned into high-growth analytics roles after completing this business track.

"The hands-on SQL query assignments and business case studies gave me practical confidence during technical assessment rounds."

NK
Neha Kapoor
Placed at PwC (Business Analyst) ↑ 130% Hike (8.2 LPA)

"From basic spreadsheet reports to writing recursive SQL queries and building automated Power BI models. Exceptional mentorship!"

KM
Karan Mehra
Placed at KPMG (SQL Data Analyst) ↑ 115% Hike (7.5 LPA)

Frequently Asked Questions

Quick answers to common questions regarding batches, databases, and placement guidance.

Is prior programming knowledge required for this course? ▾

No prior coding background is needed. We start from foundational database queries and progress step-by-step into advanced analytical SQL and BI visualization.

Which SQL dialect is taught in this program? ▾

We primarily use standard ANSI SQL on PostgreSQL and MySQL, which easily translates to Microsoft SQL Server, Snowflake, and BigQuery.

Will I receive support for SQL case study interview rounds? ▾

Yes, we conduct 50+ live SQL whiteboard drills, take-home case study walkthroughs, and 1-on-1 mock technical interview evaluations.

Tools & Database Ecosystem Covered

Master the standard analytics stack deployed across modern enterprises.

◆ PostgreSQL ◆ MySQL ◆ Microsoft Power BI ◆ Tableau Desktop ◆ Advanced Excel ◆ Snowflake SQL ◆ Google BigQuery ◆ DBeaver ◆ DAX & Power Query ◆ Git & GitHub
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