The Analytics Engineer Accelerator (DE & DA)

The shift you need to make.

Many data professionals find themselves stuck in the "reporting loop"—constantly generating SQL queries and Excel sheets without ever touching the modern engineering stack. You want to build production-grade systems, automate workflows, and deploy robust pipelines, but your current role limits you to ad-hoc analysis.

The Analytics Engineer Accelerator is designed to bridge that gap. Over 12 weeks, we move purely from querying data to engineering data. You will leave with a stronger engineering foundation, a GitHub portfolio of production-ready code, and the narrative needed to ace Data Engineering & Analytics interviews.

Cohort Structure

27 Sessions across 14 Weeks
3 Hours per Session (Theory + Hands-on Lab + Project Work)
Intimate Cohort: 25-30 people max
Note: Recordings Available for Each Session & You will get it Processed After Every Week Live Class.

What You Get

Core Training

81 Hours Live Instruction: Deep dive sessions, not just overviews.
3 Capstone Projects: End-to-end systems you can actually show hiring managers.
Processed Recordings: High-quality archives of every single session.

Career Readiness

Professional Resume & LinkedIn Profile Optimization
Mock Interviews tailored to DE/DA roles
Strategic Job Search Plan

Is this you?

?

Do you feel limited by only knowing SQL and Excel, while jobs ask for Spark and Airflow?

?

Are you struggling to pass technical screens because you lack "production" experience?

?

Do you want a portfolio that isn't just another Titanic dataset analysis?

If this resonates, this cohort is built for your next step.

Curriculum Structure

5 Phases • 27 Sessions • Zero Fluff

Session 1: SQL Foundations – Query Execution

SELECT statements, aliasing, DISTINCT
WHERE, AND/OR, IN, BETWEEN, LIKE, NULLs
ORDER BY, LIMIT, pagination
Query execution & logical order

Session 2: Advanced Joins

INNER, LEFT, RIGHT, FULL joins
Primary & foreign key relationships
Multi-table joins
Join optimization & deduplication

Session 3: Window Functions

ROW_NUMBER, RANK, DENSE_RANK
PARTITION BY concept
Running totals & cumulative metrics
Top N analysis & latest records

Session 4: Data Modeling

Fact & dimension tables
Star schema design
Snowflake schema & normalization
SCD Type 1 & 2 for history

Session 5: Python for Analytics

Pandas DataFrames & file I/O
Cleaning nulls & duplicates
Merge, groupby transformations
KPI & business metric calculation

Session 6: Power BI Dashboard

SQL to Power BI data import
Relationship & model creation
DAX measures & calculated columns
Executive dashboard design

Session 7: Distributed Computing

Distributed systems basics
Scaling concepts
Partitioning strategies
Fault tolerance

Session 8: Hadoop Ecosystem

HDFS storage
YARN resource management
MapReduce fundamentals
Hadoop architecture deep dive

Session 9: Spark Fundamentals

Spark architecture
RDD vs DataFrame
Lazy evaluation
Spark performance basics

Session 10: PySpark Analytics

Reading data sources
Transformations
Aggregations
Writing data outputs

Session 11: Advanced PySpark Optimization

Broadcast joins
Handling data skew
Partition optimization
Spark UI performance tuning

Session 12: Databricks & Delta Lake

Databricks workspace setup
Delta tables
Time travel & versioning
Optimization techniques

Session 13: Medallion Architecture

Bronze layer (raw ingestion)
Silver layer (cleaned data)
Gold layer (business-ready)
Enterprise implementation

Session 14: ETL / ELT Pipelines

ETL vs ELT patterns
Incremental loading
Change Data Capture (CDC)
Pipeline design patterns

Session 15: Orchestration

DAG design
Workflow automation
Scheduling strategies
Monitoring & alerting

Session 16: Data Quality

Validation rules
Data testing frameworks
Quality monitoring
Quality frameworks (20+ checks)

Session 17: Kafka & Streaming

Kafka fundamentals
Streaming architecture
Spark Streaming integration
Fault tolerance patterns

Session 18: Real-Time Dashboards

Streaming dashboards
KPI monitoring
Automated alerts
Anomaly detection

Session 19: Prompt Engineering for Data

SQL generation with AI
PySpark code generation
Prompt optimization techniques
Workflow automation with AI

Session 20: AI-Powered Automation

Report automation
Dashboard automation
Workflow automation
Anomaly detection automation

Session 21: Capstone 1 — E-commerce Analytics Platform

End-to-end pipeline creation
Data modeling
PySpark processing
Executive dashboard

Session 22: Capstone 2 — Real-Time Streaming Analytics

Streaming pipeline
Kafka integration
Spark Streaming
Live dashboard

Session 23: Capstone 3 — Enterprise Data Architecture

Enterprise pipeline design
Data governance
Lineage tracking
Scalability patterns

Session 24: Career Readiness

Resume optimization
Portfolio presentation
Interview preparation
Salary negotiation

Learn how AI Agents work and can do tasks in real time — from foundations to building your own agent system.

Session 25: Foundations of Agentic AI

Introduction to Artificial Intelligence
Evolution from Traditional AI to Generative AI
What is Agentic AI & Agentic AI vs Generative AI
Understanding AI Agents & their components
Role of Large Language Models (LLMs)
Memory, Tools & Integrations in Agents
Real-World Applications of Agentic AI

Session 26: Key Concepts of Agentic AI

Reasoning in AI Agents & Chain of Thought
Task Planning & Goal-Driven Decision Making
Short-Term vs Long-Term Memory
Vector Databases for AI
Tool Usage: API & Web Search Integration
Agent Frameworks: LangChain, AutoGPT & CrewAI

Session 27: Creating an Agentic AI Project (Hands-on)

Understanding Agent Architecture
Defining Goals & Designing Instructions
Adding Reasoning & Integrating Tools/APIs
Implementing Memory in Agents
Building & Testing the Agent Workflow
Deploying the Agent System

Lead Instructors

Durgesh Yadav & Pragya Rathi

(10,000+ students trained)

7-Eleven Data Engineer
Product Analyst @ Target; Data Specialist
GeeksforGeeks Data Analytics Instructor; Data Engineering Instructor at Bosscoder; DA Instructor @ Newton School
Multiple students placed in Top Product Based Companies

Who This Is For

Aspiring analytics engineers and data analysts ready to move into engineering roles.
Professionals seeking production-grade portfolio projects to showcase actual skills, not just theory.
Learners who thrive in structured environments with live instruction and hands-on labs.

Everything Included

27 live sessions (81 hours)

12-week structure, 2 sessions/week

3 capstone portfolio projects

Complete Career Support Package

Note: Recordings Available for Each Session & Processed Weekly

What students say

"Honestly, I don’t know what Durgesh Yadav was thinking while pricing this course. Power BI, SQL, Python, EDA, projects, interview prep — all stuffed into one super-cheap package. Bhai, this is not a course, this is a full data analytics universe. Other institutes charge a kidney for half this content. If you want maximum learning with minimum financial trauma, this is the course"

— Darshani Teltumbade (Nov 2025)

"Well articulated presentation.You simplified the differences between data science, data engineering and analytics. Looking forward to enrol with you in the near future"

— Simbarashe Mpofu (Jan 2026)

"Really Insightful and has end to end core knowledge of DE and has changed my perspective of how to approach an interview ."

— Anonymous (Oct 2025)

Frequently Asked Questions

No. While we provide job-ready support including resume optimization, portfolio building, and mock interviews to help you land a role, we do not offer a placement guarantee. Our focus is on skill acquisition and readiness.

We cover SQL, Python, Power BI, Hadoop, Spark, Databricks, Delta Lake, Kafka, and ChatGPT.

Yes. The course is structured to take you from foundational analytics (SQL/Excel) into engineering. It is designed specifically for analysts making that transition.

Pricing may appear in different currencies depending on your location at checkout, but the seat corresponds to the same program.

Yes, recordings are available for each session and you will get them processed after every week's live class.