Engineering foundations first
SQL, Python, data modeling, ETL design, cloud fundamentals, and ML concepts are taught from the ground up so GenAI tools become accelerators, not crutches.
8 month program with
data engineering, ML, and GenAI specialization
Modern Data Engineering and ML with GenAI
From Python and SQL to cloud pipelines, warehouses, streaming systems, machine learning workflows, and GenAI applications, this program is built for learners who want production-ready data engineering and AI skills.
Model accuracy
92.8%
GenAI-integrated learning
The program is built around how modern data platforms are engineered.
Learn foundations deeply, then use GenAI assistants to design, validate, debug, improve, and explain data pipelines, ML workflows, and platform decisions with professional judgment.
GenAI in every module
Use GenAI for query generation, pipeline design, code review, model debugging, documentation, and interview practice across the complete program.
Career-ready systems
Build projects that move from raw data to reliable pipelines, warehouses, streaming systems, ML features, APIs, and clear technical narratives you can explain in interviews.
Who it is for
Data engineering roles are changing. Build the skill stack that matches them.
Choose your current stage and see how this program supports growth into data engineering, ML, and GenAI platform roles.
I want to build reliable data systems that power AI products.
- End-to-end workflows with SQL, Python, ETL, cloud data platforms, and ML.
- GenAI-assisted learning that helps you move faster while validating output.
- Projects that make your engineering portfolio concrete and interview-friendly.
GenAI operating system
Design, automate, validate, and own production data workflows.
GenAI can accelerate execution. Your advantage is knowing what to ask, what to inspect, and how to turn outputs into reliable data platforms, ML systems, and engineering decisions.
01
GenAI-assisted labs
Practice with guided workflows where GenAI helps review SQL, pipeline code, model choices, data contracts, and architecture tradeoffs.
02
Engineering-first projects
Every build requires a clear explanation of architecture, data quality, orchestration, validation, tradeoffs, and business impact.
03
Interview simulation
Mock interviews focus on pipeline design, debugging, cloud architecture, ML workflows, GenAI integration, and system design.
Tools you will learn with us
Curriculum
Find the AI path for your role.
A structured path from analytics foundations to ML engineering and generative AI applications.
01
Phase 1 - Foundations
Build the base for Python, analytics, SQL, statistics, dashboards, and your first
applied data project.
Phase 1 - Foundations
Build the base for Python, analytics, SQL, statistics, dashboards, and your first applied data project.
Week 1
Python Fundamentals
Python basics, functions, OOP, NumPy, Pandas, file handling, and AI coding tools.
Week 2
EDA and SQL Basics
Advanced Pandas, data cleaning, visualization, statistics foundations, and SQL fundamentals.
Week 3
SQL Mastery and Dashboards
Window functions, SQL optimization, product analytics metrics, Tableau, and Power BI dashboards.
Week 4
Statistics, A/B Testing and Mini Project
Hypothesis testing, A/B testing, Swiggy/Zomato-style analytics project, presentation, and review.
02
Phase 2 - Machine Learning and Generative AI
Move from core ML to deep learning, NLP, LLMs, RAG, agents, recommendations,
forecasting, computer vision, and AI projects.
Phase 2 - Machine Learning and Generative AI
Move from core ML to deep learning, NLP, LLMs, RAG, agents, recommendations, forecasting, computer vision, and AI projects.
Week 5
ML Fundamentals
Linear and logistic regression, classification algorithms, evaluation metrics, and feature engineering.
Week 6
Advanced ML - Ensembles
Random Forest, bagging, XGBoost, LightGBM, SHAP, and hyperparameter tuning.
Week 7
Unsupervised and Deep Learning Intro
Clustering, dimensionality reduction, neural network fundamentals, PyTorch, and Keras.
Week 8
ML Project - Credit Risk Model
Credit risk EDA, feature engineering, model tuning, SHAP explanations, presentation, and review.
Week 9
LLMs and Prompt Engineering
Transformers, attention, prompt engineering, LLM APIs for data tasks, and RAG introduction.
Week 10
NLP Fundamentals
Text preprocessing, TF-IDF, sentiment analysis, NER, text classification, and Hugging Face.
Week 11
Advanced NLP and BERT
BERT architecture, fine-tuning, embeddings, semantic search, and multimodal AI introduction.
Week 12
GenAI Project - AI Business Insights Engine
RAG architecture, ingestion, embeddings, vector stores, LLM integration, evaluation, and API deployment.
Week 13
Advanced RAG and Agents
Hybrid search, reranking, LangChain, LlamaIndex, agentic AI, tool use, and responsible AI.
Week 14
Recommendation Systems
Collaborative filtering, matrix factorization, content-based systems, hybrid systems, and neural RecSys.
Week 15
Time Series Forecasting
Decomposition, ARIMA, SARIMA, Holt-Winters, Prophet, XGBoost, and LSTM forecasting.
Week 16
NLP Project - Customer Support AI
Chatbot design, BERT intent classification, NER, RAG responses, integration, and quality testing.
Week 17
Computer Vision Fundamentals
CNNs, ResNet, YOLO, transfer learning, and vision plus LLM applications.
Week 18
Phase 2 Capstone and Review
End-to-end ML plus GenAI capstone, optimization, LLM integration, presentation, and portfolio review.
03
Phase 3 - Data Engineering and Pipelines
Build data platforms with warehouses, dbt, Airflow, PySpark, Delta Lake, Kafka,
streaming, AWS, and infrastructure as code.
Phase 3 - Data Engineering and Pipelines
Build data platforms with warehouses, dbt, Airflow, PySpark, Delta Lake, Kafka, streaming, AWS, and infrastructure as code.
Week 19
Data Modeling and Warehousing
Star schema, snowflake schema, dbt basics, data quality, governance, and warehouse mini project.
Week 20
Apache Airflow and ETL Design
Airflow architecture, DAGs, ETL design patterns, integrations, monitoring, and alerting.
Week 21
ETL Project - Nike Sales Pipeline
Sales pipeline architecture, ingestion, dbt transformations, Airflow orchestration, dashboard, and presentation.
Week 22
Advanced dbt and Data Contracts
dbt tests, documentation, snapshots, incremental models, data contracts, lineage, and observability.
Week 23
PySpark Fundamentals
Spark architecture, RDDs, PySpark DataFrames, Spark SQL, optimization, and MLlib.
Week 24
Big Data Project - E-commerce Pipeline
Large-scale ingestion, feature engineering, personalization pipeline, performance tuning, and review.
Week 25
Delta Lake and Lakehouse Architecture
Lakehouse architecture, Delta Lake, ACID transactions, time travel, catalogs, and Databricks lab.
Week 26
Real-Time Streaming with Kafka
Kafka architecture, producers, consumers, consumer groups, Kafka Connect, Schema Registry, and Kafka Streams.
Week 27
Spark Streaming Project
Structured Streaming, Kafka integration, real-time analytics, exactly-once semantics, and dashboard showcase.
Week 28
Cloud Architecture - AWS
AWS foundations, IAM, VPC, EC2, S3, Glue, Athena, Redshift, Lambda, SQS, and serverless patterns.
Week 29
Cloud Project and Infrastructure as Code
AWS cloud data pipeline, Terraform, cost optimization, cloud security, IAM, and encryption.
Week 30
Phase 3 Capstone - Full Data Platform
Platform design, pipeline build sprint, infrastructure, testing, monitoring, documentation, and presentation.
04
Phase 4 - MLOps, Advanced AI and Career
Finish with production ML, model serving, monitoring, CI/CD, system design, capstone
polish, and interview readiness.
Phase 4 - MLOps, Advanced AI and Career
Finish with production ML, model serving, monitoring, CI/CD, system design, capstone polish, and interview readiness.
Week 31
Advanced ML and AI
Recommendation systems at scale, advanced forecasting, vector databases, advanced RAG, and agentic AI workflows.
Week 32
MLOps - Production ML
Docker, FastAPI model serving, monitoring, drift detection, retraining triggers, CI/CD, and MLflow.
Week 33
Capstone and Career Launch
Data platform system design, real-time analytics capstone build, integration, demo polish, portfolio, and mock interviews.
Phase 1 - Foundations
Build the practical base for Python, analytics, SQL, statistics, dashboards, and your first applied data project.
Weeks 1-4Classes 1-16Month 1
Week 1
Python Fundamentals
Python basics, functions, OOP, NumPy, Pandas, file handling, and AI coding tools.
Week 2
EDA and SQL Basics
Advanced Pandas, data cleaning, visualization, statistics foundations, and SQL fundamentals.
Week 3
SQL Mastery and Dashboards
Window functions, SQL optimization, product analytics metrics, Tableau, and Power BI dashboards.
Week 4
Statistics, A/B Testing and Mini Project
Hypothesis testing, A/B testing, Swiggy/Zomato-style analytics project, presentation, and review.
Phase 2 - Machine Learning and Generative AI
Move from core ML to deep learning, NLP, LLMs, RAG, agents, recommendations, forecasting, computer vision, and AI portfolio projects.
Weeks 5-18Classes 17-72Months 2-4
Week 5
ML Fundamentals
Linear and logistic regression, classification algorithms, evaluation metrics, and feature engineering.
Week 6
Advanced ML - Ensembles
Random Forest, bagging, XGBoost, LightGBM, SHAP, and hyperparameter tuning.
Week 7
Unsupervised and Deep Learning Intro
Clustering, dimensionality reduction, neural network fundamentals, PyTorch, and Keras.
Week 8
ML Project - Credit Risk Model
Credit risk EDA, feature engineering, model tuning, SHAP explanations, presentation, and review.
Week 9
LLMs and Prompt Engineering
Transformers, attention, prompt engineering, LLM APIs for data tasks, and RAG introduction.
Week 10
NLP Fundamentals
Text preprocessing, TF-IDF, sentiment analysis, NER, text classification, and Hugging Face.
Week 11
Advanced NLP and BERT
BERT architecture, fine-tuning, embeddings, semantic search, and multimodal AI introduction.
Week 12
GenAI Project - AI Business Insights Engine
RAG architecture, ingestion, embeddings, vector stores, LLM integration, evaluation, and API deployment.
Week 13
Advanced RAG and Agents
Hybrid search, reranking, LangChain, LlamaIndex, agentic AI, tool use, and responsible AI.
Week 14
Recommendation Systems
Collaborative filtering, matrix factorization, content-based systems, hybrid systems, and neural RecSys.
Week 15
Time Series Forecasting
Decomposition, ARIMA, SARIMA, Holt-Winters, Prophet, XGBoost, and LSTM forecasting.
Week 16
NLP Project - Customer Support AI
Chatbot design, BERT intent classification, NER, RAG responses, integration, and quality testing.
Week 17
Computer Vision Fundamentals
CNNs, ResNet, YOLO, transfer learning, and vision plus LLM applications.
Week 18
Phase 2 Capstone and Review
End-to-end ML plus GenAI capstone, optimization, LLM integration, presentation, and portfolio review.
Phase 3 - Data Engineering and Pipelines
Build modern data platforms with warehouses, dbt, Airflow, PySpark, Delta Lake, Kafka, streaming, AWS, and infrastructure as code.
Weeks 19-30Classes 73-120Months 5-7
Week 19
Data Modeling and Warehousing
Star schema, snowflake schema, dbt basics, data quality, governance, and warehouse mini project.
Week 20
Apache Airflow and ETL Design
Airflow architecture, DAGs, ETL design patterns, integrations, monitoring, and alerting.
Week 21
ETL Project - Nike Sales Pipeline
Sales pipeline architecture, ingestion, dbt transformations, Airflow orchestration, dashboard, and presentation.
Week 22
Advanced dbt and Data Contracts
dbt tests, documentation, snapshots, incremental models, data contracts, lineage, and observability.
Week 23
PySpark Fundamentals
Spark architecture, RDDs, PySpark DataFrames, Spark SQL, optimization, and MLlib.
Week 24
Big Data Project - E-commerce Pipeline
Large-scale ingestion, feature engineering, personalization pipeline, performance tuning, and review.
Week 25
Delta Lake and Lakehouse Architecture
Lakehouse architecture, Delta Lake, ACID transactions, time travel, catalogs, and Databricks lab.
Week 26
Real-Time Streaming with Kafka
Kafka architecture, producers, consumers, consumer groups, Kafka Connect, Schema Registry, and Kafka Streams.
Week 27
Spark Streaming Project
Structured Streaming, Kafka integration, real-time analytics, exactly-once semantics, and dashboard showcase.
Week 28
Cloud Architecture - AWS
AWS foundations, IAM, VPC, EC2, S3, Glue, Athena, Redshift, Lambda, SQS, and serverless patterns.
Week 29
Cloud Project and Infrastructure as Code
AWS cloud data pipeline, Terraform, cost optimization, cloud security, IAM, and encryption.
Week 30
Phase 3 Capstone - Full Data Platform
Platform design, pipeline build sprint, infrastructure, testing, monitoring, documentation, and presentation.
Phase 4 - MLOps, Advanced AI and Career
Finish with advanced AI workflows, production ML, model serving, monitoring, CI/CD, system design, capstone polish, and interview readiness.
Weeks 31-33Classes 121-132Month 8
Week 31
Advanced ML and AI
Recommendation systems at scale, advanced forecasting, vector databases, advanced RAG, and agentic AI workflows.
Week 32
MLOps - Production ML
Docker, FastAPI model serving, monitoring, drift detection, retraining triggers, CI/CD, and MLflow.
Week 33
Capstone and Career Launch
Data platform system design, real-time analytics capstone build, integration, demo polish, portfolio, and mock interviews.
Projects
Build portfolio work that starts with raw data and ends with decisions.
Each project is designed to be opened, explained, defended, and improved in an interview.
Instructors and mentors
Learn from practitioners who bring real workflows into class.
Live sessions, reviews, and mentor support help you build the judgment behind the tools.
Nagendra
Data science mentorGuides learners through analytics, Python, and ML projects.
Prerna
ML and analytics mentorFocuses on applied projects, data storytelling, and interviews.
Surjya
AI workflow mentorHelps learners use AI tools with review and validation habits.
Certificate
Finish with a certificate built for your resume and LinkedIn.
Showcase structured learning, hands-on projects, and AI-integrated data science skills through Edspark certification.
Project evaluation
Mentor guidance
Career support
Skills you will build
Data Engineering, ML, and GenAI skills you will learn
Explore the core domains covered through the program, projects, and advisor-led learning path.
Python
SQL
Machine Learning
Data Engineering
Pipelines
Generative AI
AWS
Kafka
dbt
MLOps
Where GenAI-native data engineering takes you
GenAI didn't replace data engineering.It raised the bar for production-ready builders.
Data Engineer
ML Engineer
GenAI Engineer
AI Platform Engineer
LLMOps Engineer
Analytics Engineer
Cloud Data Engineer
Data and AI engineering openings
Projected demand across data engineering, analytics engineering, ML, and AI-enabled platform functions.
GenAI skill wage premium
GenAI-skilled professionals are increasingly valued across equivalent technical, data, and ML engineering roles.
ML and platform specialist salary lift
Specialized ML, data platform, and GenAI depth can create stronger career leverage than generic tool familiarity.
Next cohort
Ready to upskill in Data Engineering and ML with GenAI
Talk to an advisor to understand eligibility, schedule, projects, fees, and the best path for your current career stage.
FREQUENTLY ASKED QUESTIONS
Got Questions? We've Got Answers
Do I need coding experience before joining?
No. The program begins with Python and SQL foundations, then moves into analytics, ML, and AI workflows step by step.
Will I build real projects?
Yes. The curriculum includes portfolio projects with real-world problem statements, data cleaning, dashboards, model building, and presentation.
How is AI included in the course?
AI is used throughout the course for SQL assistance, coding support, EDA, debugging, report generation, mock interviews, and project improvement.
Is placement support included?
Yes. Learners receive career guidance, resume support, interview preparation, and placement-oriented assistance.