Home / Programs / Business Analyst
Cohort 014 · Business Analyst & AI Agents · Enrolling Now

Business Analyst
+ AI Agents

Master end-to-end Business Analyst and AI Agents with real-world, job-ready implementation skills. Build foundations in Python and SQL, ship pipelines with PySpark and Databricks, scale on Microsoft Fabric, and integrate Generative + Agentic AI into production data workflows.

3mo
duration
30+
modules
4.7/5
cohort rating
100k+
enrolled
Where our Business Analyst alumni work
MicrosoftAmazonSalesforceAI EngineerDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL MicrosoftAmazonSalesforceAI EngineerDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
What you leave with

Four things every Business Analyst grad walks away with.

Most programs stop at tools. Ours makes you ship pipelines, platforms, and AI-powered data products that hiring teams can verify.

01
Agent-Ready skills
Build, deploy, and monitor AI agents that run production workflows — not chatbot toys.
02
A shipped capstone
A live React + FastAPI + LangGraph app on Kubernetes, monitored, observable, public URL.
03
Verifiable credential
2026 Agent-Ready rubric, graded 1–5 with a public verification URL recruiters can check.
04
Direct placement pipeline
GitHub + LinkedIn rewrite, resume rebuild, and warm intros to our 1,000+ hiring partners.
3 months, four phases

From "loads CSVs" to ships AI-native data pipelines.

Weeks 1–3 build Python and SQL depth. Weeks 4–7 cover Power BI and data storytelling. Weeks 8–10 move into PySpark, Databricks, and Fabric. Weeks 11–12 ship Generative + Agentic AI data agents.

WEEKS 1–3 · FOUNDATIONS

Python + SQL for Business Analyst

  • Python data structures, iterators, OOP
  • PostgreSQL querying, joins, windows, CTEs
  • Database design, indexing, optimization
  • Data file formats and transformation patterns
YOU SHIPA Python + SQL ingestion and transformation workflow over production-like datasets.
WEEKS 4–7 · ANALYTICS

Power BI and business intelligence

  • Power Query and source integrations
  • Star schema modeling and DAX measures
  • Advanced visuals, storytelling, KPI dashboards
  • Publishing, sharing, governance, refresh
YOU SHIPA complete Power BI reporting suite consumed by business teams and leaders.
WEEKS 8–10 · DATA PLATFORM

PySpark, Databricks, and Microsoft Fabric

  • Spark DataFrames, joins, windows, optimization
  • Databricks workflows, Delta Lake, Unity Catalog
  • Fabric OneLake, Lakehouse, Warehouse, RTI
  • Streaming, orchestration, and governance
YOU SHIPAn enterprise-grade ELT platform with scheduled jobs, observability, and governed data products.
WEEKS 11–12 · GENERATIVE + AGENTIC AI

Deploy AI agents that automate analytics, retrieval, and reporting across your data platform.

Use LLM APIs, LangChain, RAG, and LangGraph workflows with persistence and HITL. Add MCP tool access and enterprise guardrails. Your capstone connects pipelines, dashboards, and AI agents into a single production-ready data intelligence system.

Partner orgs (2026)48
Capstones deployed280+
→ Placement offers82%
Course curriculum

Seven sections. 65+ modules. The AI-native Business Analyst stack.

Jump to any section on the left. Click a module to see topics, hands-on lab, and key technologies.

01

Application Life Cycle Management

How modern apps work, how teams ship them with Agile, where compute & cloud fit, and how AI plugs into the 2026 stack.
10 MODULES
WEEK 1
Key Topics: What is an Application Types of Applications Web Application Fundamentals Web Technologies: Frontend (HTML, CSS, JavaScript, React) Web Technologies: Backend (Python, Java, Node.js) Web Technologies: Databases (SQL: MySQL, PostgreSQL; NoSQL: MongoDB) Software Development Life Cycle (SDLC) SDLC Phases: Planning, Analysis, Design, Implementation, Testing, Deployment, Maintenance
Key Topics : What is an Application Types of Applications Web Application Fundamentals Web Technologies: Frontend (HTML, CSS, JavaScript, React) Web Technologies: Backend (Python, Java, Node.js) Web Technologies: Databases (SQL: MySQL, PostgreSQL; NoSQL: MongoDB) Software Development Life Cycle (SDLC) SDLC Phases: Planning, Analysis, Design, Implementation, Testing, Deployment, Maintenance
Key Topics : Waterfall vs. Agile Methodologies The Agile Mindset Popular Agile Frameworks Scrum Overview & Pillars Scrum Roles (Product Owner, Scrum Master, Dev Team) Scrum Events (Sprint, Planning, Daily Scrum, Review, Retrospective) Scrum Artifacts (Product Backlog, Sprint Backlog, Increment) Writing User Stories Epics and Themes Acceptance Criteria Estimating User Stories Managing Backlogs Tools: Google Sheets, Azure Boards
Key Topics : The Importance of Computing Power Key Computing Technologies: CPU (Central Processing Unit) Key Computing Technologies: GPU (Graphics Processing Unit) Introduction to Cloud Computing Cloud Service Models: IaaS (Infrastructure as a Service) Cloud Service Models: PaaS (Platform as a Service) Cloud Service Models: SaaS (Software as a Service)
Key Topics : What is Artificial Intelligence (AI)? How AI Works? Machine Learning (ML) Fundamentals Deep Learning (DL) Fundamentals What is Generative AI? Large Language Models (LLMs) Image Generation Models AI in Everyday Learning
02

Business Analyst with GenAi

Modern React with hooks, Redux Toolkit and routing, paired with PostgreSQL fundamentals through query optimization.
10 MODULES
WEEKS 2–4
Key Topics : Role of a Modern Business Analyst Agile Mindset & Scrum vs. Waterfall Scrum Framework Deep Dive (Roles, Events, Artifacts) User Stories, Epics, Themes & Acceptance Criteria Requirement Backlogs in Sheets & Azure Boards Introduction to GenAI in BA Work BA Tools & Skills for the Future Application Lifecycle Management (ALM)
Key Topics : Discovery Phase Overview Identifying Stakeholders & Stakeholder Analysis Power/Interest Grid & Influence/Impact Matrix Creating User Personas Empathy Mapping The RACI Matrix Agile Interviews (Discovery, Contextual, Stand-up) Surveys in Agile Context Workshops and Facilitation GenAI for Stakeholder Management
Key Topics : Introduction to Requirement Prioritization MoSCoW Method WSJF (Weighted Shortest Job First) RICE Scoring Model Comparing Prioritization Techniques GenAI for Requirements Management Requirement Traceability Requirement Versioning Requirement Validation Aligning Requirements with Business Needs & OKRs
Key Topics : Business Process Modeling Notation (BPMN) As-Is and To-Be Process Mapping SIPOC Analysis Value Stream Mapping Customer Journey Mapping Identifying Bottlenecks and Waste Automation Opportunities GenAI for Diagram Generation & Workflow Simulation Version Control for Process Diagrams **Hands-On:** Create As-Is BPMN diagram for existing process Design To-Be process with automation Conduct Value Stream Mapping workshop Identify 5 automation opportunities
Key Topics : From Process to Requirements Business Requirements Document (BRD) Functional Design Document (FDD) User Story Mapping to Functional Requirements Integration Scenarios & API Requirements Non-Functional Requirements (NFRs) Data Modeling & Schema Design Requirements Traceability Matrix GenAI for Requirements Documentation Linking Requirements to Objectives & OKRs Requirements Review & Sign-Off Process
Key Topics : Introduction to Prototyping Wireframing vs. Mockups vs. Prototypes Design Thinking for BAs User-Centered Design Principles Creating Wireframes (Balsamiq, Figma) Interactive Prototypes Usability Testing Design Validation with Stakeholders GenAI for UI/UX Design
Key Topics : BA Role in Testing Writing Test Scenarios from Requirements User Acceptance Testing (UAT) Planning Creating UAT Scripts Facilitating UAT Sessions Defect Management & Triage Acceptance Criteria Validation GenAI for Test Case Generation Testing Documentation
Key Topics : Change Management Fundamentals Stakeholder Communication Planning Creating Training Materials Conducting Training Sessions User Adoption Strategies Resistance Management Communication Cadence & Channels GenAI for Training Content Creation
Key Topics : Go-Live Readiness Assessment Deployment Planning Cutover Strategy Super Care Period Planning Production Support Handoff Monitoring & Issue Resolution Post-Implementation Review Lessons Learned
Key Topics : Measuring Business Value KPI Definition & Tracking Data Analysis for BAs SQL Basics for Requirements Power BI / Tableau for Reporting Process Optimization Retrospectives & Feedback Loops GenAI for Data Analysis BA Career Growth & Specializations
03

Testing for Business Analyst

Python from fundamentals through OOP, then FastAPI — async APIs with Pydantic validation, SQLAlchemy, and JWT auth.
15 MODULES
WEEKS 5–8
Key Topics : What is Software & Types of Software (System, Programming, Application) Software Applications (Desktop, Web, Mobile) What is Software Testing & Why Testing is Important Real-world Testing Failures (Therac-25, Mars Climate Orbiter, Boeing 737 MAX) Software Development Life Cycle (SDLC) - Overview Quality, QA vs QC Errors, Bugs, Defects & Failures Software Testing Life Cycle (STLC) - Introduction
Key Topics : SDLC Models Overview Waterfall Model (Phases, Advantages, Disadvantages, When to Use) V-Model (Verification & Validation Model) BRS & SRS Documents HLD & LLD Documents Static Testing (Review, Walkthrough, Inspection) Comparison of SDLC Models White Box, Black Box & Grey Box Testing When to use each testing approach
Key Topics : Unit Testing Concept & Responsibility Unit Testing Techniques (Basis Path, Conditional Coverage, Loop Coverage) Unit Testing Frameworks (JUnit, NUnit, pytest) Integration Testing Purpose & Importance Integration Testing Types: Incremental (Top-Down, Bottom-Up, Sandwich/Hybrid) Non-Incremental (Big Bang) Stubs & Drivers Developer's Role in Testing
Key Topics : System Testing Overview Functional vs Non-Functional Testing GUI Testing Visual Design Verification Functional Elements Testing Content & Interaction Verification Usability Testing User-friendliness Assessment User Satisfaction Metrics User Acceptance Testing (UAT) Alpha Testing Beta Testing
Key Topics : Object Properties Testing Database Testing (SQL Basics for Testers) CRUD Operations Validation Data Integrity Testing Error Handling Testing Calculations/Computations Testing Links Testing (Internal, External, Anchor, Email) Cookie Testing Session Testing Practical Examples & Scenarios
Key Topics : Performance Testing Load Testing Stress Testing Spike Testing Endurance/Soak Testing Volume Testing Scalability Testing Security Testing (Authentication, Authorization, Encryption) Recovery Testing Compatibility Testing (Hardware, OS, Browser) Installation Testing (Fresh, Upgrade, Uninstall, Reinstall) Garbage Testing
Key Topics : Regression Testing (Unit, Regional, Full) Retesting vs Regression Testing Smoke Testing (Build Verification Testing) Sanity Testing Exploratory Testing Ad-Hoc Testing Monkey Testing Positive Testing vs Negative Testing Globalization Testing Localization Testing Severity vs Priority
Key Topics : Equivalence Class Partitioning (ECP) Valid & Invalid Equivalence Classes Practical Examples Boundary Value Analysis (BVA) Boundary Values & Conditions Test Case Creation Decision Table Testing Conditions, Actions & Rules Complex Business Logic Testing State Transition Testing States, Events, Transitions State Transition Diagrams & Tables Error Guessing (Experience-based Testing)
Key Topics : Software Testing Life Cycle (STLC) Phases Requirement Analysis Test Planning Test Case Development Test Environment Setup Test Execution Test Closure Test Plan (Contents, Creation, Sample) Use Case, Test Scenario & Test Case Understanding the Differences Use Case Components (Actor, Action, Goal) Test Case Design & Documentation Test Case Components & Format Test Data Management Writing Effective Test Cases Requirement Traceability Matrix (RTM) Defect/Bug Life Cycle & Reporting
Key Topics : Agile Methodology Agile Principles & Values Advantages & Disadvantages Agile Frameworks Overview Scrum Framework Scrum Roles (Product Owner, Scrum Master, Development Team) Scrum Artifacts (Product Backlog, Sprint Backlog, Increment) Scrum Events (Sprint, Sprint Planning, Daily Scrum, Sprint Review, Sprint Retrospective) Burndown/Burnup Charts Testing in Agile Environment JIRA for Test Management Creating Test Cases in JIRA Bug Tracking & Reporting Sprint Management Version Control System (GitHub Basics) Real Project Execution Interview Preparation & Resume Building
04

Power BI for Data Analysis

Production Business Analyst : Power BI for analytics, then Microsoft Fabric — OneLake, Lakehouse medallion architecture, Spark, real-time intelligence, and Copilot.
25 MODULES
WEEKS 9–14
Key Topics : Business Intelligence fundamentals and modern analytics Power BI components and architecture Interface navigation and first report creation Understanding Desktop vs. Service capabilities
Key Topics : File, database, cloud, and web source connectivity Import vs. DirectQuery vs. Live Connection Data source settings and credential management Performance considerations for connection modes
Key Topics : Power Query interface and applied steps Data profiling and quality assessment Essential transformations: filtering, splitting, merging Reshaping: pivot, unpivot, grouping Combining queries: append and merge operations
Key Topics : Star schema vs. snowflake schema design Creating and managing table relationships Primary and foreign keys Hierarchies and date dimension tables Data model optimization strategies
Key Topics : Data visualization principles and chart selection Core visualizations: charts, tables, maps, KPIs Interactive elements: slicers, filters, bookmarks, drill-through Dashboard layout and mobile optimization Storytelling with data
Key Topics : DAX syntax and structure Calculated columns vs. measures Essential functions: aggregation, logical, text, date/time CALCULATE and FILTER functions Creating KPIs and business metrics
Key Topics : Time intelligence functions: YTD, MTD, QTD Prior period comparisons and growth rates Filter context vs. row context Variables and iterator functions DAX performance optimization
Key Topics : Custom visuals from AppSource Advanced chart types: waterfall, funnel, decomposition tree R and Python integration AI visuals: Key Influencers, Q&A, Smart Narratives Dynamic visuals with parameters
Key Topics : Publishing and workspace management Dashboards vs. reports Data refresh and gateway configuration Sharing strategies and Power BI apps Integration with Teams, SharePoint, Excel, PowerPoint
Key Topics : Power BI admin portal and tenant settings Row-Level Security (RLS) and Object-Level Security (OLS) Incremental refresh and aggregations Dataflows and deployment pipelines Performance optimization and capacity management Enterprise licensing models APIs and embedded analytics
05

SQL for Business Analyst

The mathematical backbone behind every ML and DL model: linear algebra, probability, distributions, hypothesis testing, and applied statistics for ML.
5 MODULES
WEEK 15
Key Topics : Introduction to Databases & DBMS Relational Database Management Systems (RDBMS) ACID Properties (Atomicity, Consistency, Isolation, Durability) Introduction to PostgreSQL PostgreSQL Installation & Setup (Windows, Mac, Linux) PostgreSQL Tools: psql, pgAdmin 4 Database Objects (Databases, Schemas, Tables) Data Types: Numeric, Character, Date/Time, Boolean, Special types Constraints: PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, CHECK, DEFAULT Creating Databases and Tables INSERT Operations and Data Population Referential Integrity
Key Topics : SELECT Statement Basics Column Aliases and Expressions WHERE Clause and Filtering Comparison Operators (=, !=, >, ,>=, =) - Logical Operators (AND, OR, NOT) - BETWEEN, IN, LIKE operators - NULL handling (IS NULL, IS NOT NULL) - ORDER BY (Sorting data) - DISTINCT (Removing duplicates) - LIMIT and OFFSET (Pagination) - String Functions (UPPER, LOWER, CONCAT, SUBSTRING, etc.) - Numeric Functions (ROUND, CEIL, FLOOR, ABS, etc.) - Date and Time Functions (CURRENT_DATE, EXTRACT, DATE_TRUNC, etc.) - Aggregate Functions (COUNT, SUM, AVG, MIN, MAX) - GROUP BY and HAVING Window Functions (ROW_NUMBER, RANK, LAG, LEAD, etc.) - JOIN Operations: - INNER JOIN - LEFT JOIN / RIGHT JOIN - FULL OUTER JOIN - CROSS JOIN - SELF JOIN - Multi-table Joins - Join Optimization
Key Topics : Subqueries in WHERE, SELECT, FROM clauses Correlated Subqueries EXISTS and NOT EXISTS IN and NOT IN with subqueries Common Table Expressions (CTEs) Recursive CTEs for hierarchical data Multiple CTEs Set Operators: UNION and UNION ALL INTERSECT EXCEPT UPDATE Statements UPDATE with expressions UPDATE with JOIN DELETE Statements DELETE with subqueries TRUNCATE vs DELETE Transaction Management BEGIN, COMMIT, ROLLBACK Savepoints Transaction Isolation Levels Concurrency Control
Key Topics : ALTER TABLE Operations Adding, Modifying, Dropping Columns Managing Constraints Indexes and Performance Index Types (B-tree, Hash, GIN, GiST) Creating and Managing Indexes When to use indexes Views and Abstraction Creating Views Updatable Views Materialized Views Refreshing Materialized Views Stored Functions PL/pgSQL Programming Language Function Parameters and Return Types Control Structures (IF, CASE, LOOP) Functions Returning Tables Stored Procedures Procedure vs Function differences Exception Handling in PL/pgSQL Triggers BEFORE, AFTER, INSTEAD OF triggers Trigger Functions Audit Logging with Triggers Data Validation with Triggers Advisory Locks
Key Topics : Entity-Relationship (ER) Modeling Entities, Attributes, Relationships Relationship Types (1:1, 1:M, M:N) ER Diagrams Normalization Principles First Normal Form (1NF) Second Normal Form (2NF) Third Normal Form (3NF) Normalization Benefits and Trade-offs When to Denormalize Database Design Best Practices Naming Conventions Data Type Selection Primary Key Strategies Foreign Key Design Query Optimization EXPLAIN and EXPLAIN ANALYZE Reading Execution Plans Index Strategies Query Rewriting Techniques Performance Tuning Database Statistics (ANALYZE) VACUUM and Maintenance Connection Pooling Table Partitioning
06

Generative AI & Agentic AI

The mathematical backbone behind every ML and DL model: linear algebra, probability, distributions, hypothesis testing, and applied statistics for ML.
5 MODULES
WEEK 15
Large Language Models fundamentals Transformer architecture Comparing major LLMs (GPT, Claude, Gemini, DeepSeek) Evolution of LLMs from GPT-1 to 2026 frontier models LLM architecture and tokenization Model selection for different use cases Cost optimization strategies
Advanced prompt engineering techniques Context engineering and design Reasoning mode optimization Reducing hallucinations Zero-shot, few-shot, and chain-of-thought prompting Multimodal prompting (text, image, audio) Domain-specific prompt design
OpenAI, Anthropic, Google, and DeepSeek APIs LangChain 1.0 fundamentals Create_agent abstraction Middleware systems for customization Multi-provider integration Streaming and batching Function calling and structured outputs Cost-optimized pipelines
Vector databases (ChromaDB, Pinecone, Qdrant) Building production RAG pipelines Agentic RAG and self-improving retrieval MCP-Enhanced RAG Embedding strategies Hybrid search (semantic and keyword) Document processing at scale Hallucination reduction techniques
Streamlit and Gradio interfaces LangGraph Platform deployment Cost optimization strategies AI governance and EU AI Act compliance API security and rate limiting Monitoring and observability Scaling strategies Integration with enterprise tools
Agentic AI fundamentals (plan, reason, act) LangChain 1.0 Agents with middleware Model Context Protocol (MCP) Tool integration patterns Enterprise adoption and use cases Agent architectures and design patterns
LangGraph 1.0 architecture State management and graph-based logic Node caching for development Pre/Post hooks for guardrails Building AI workflows Production use cases
Parallel execution with deferred nodes Conditional routing and decision trees Iterative refinement loops Type-safe streaming Essay evaluation systems Customer feedback routing Multi-stage approval workflows Quality-gated content generation
Durable state management Built-in persistence (PostgreSQL, Redis) Human-in-the-loop (HITL) implementations Multi-day workflow support Enterprise compliance and audit trails Restart and failure recovery
LangGraph Platform deployment Multi-agent system design Google A2A Protocol for agent-to-agent communication LangSmith observability and monitoring MCP security model Prompt injection prevention Compliance and audit trails Agent guardrails and safety
Tools you'll master

40+ tools, one production capstone.

Not a shallow tour. You'll use every one of these in at least one graded exercise.

R
React 18
RT
Redux Toolkit
TS
TypeScript
V
Vite
Nd
Node.js
Py
Python
FA
FastAPI
SA
SQLAlchemy
Pg
PostgreSQL
M
MongoDB
PB
Power BI
MF
MS Fabric
Np
NumPy
Pd
Pandas
Sk
scikit-learn
TF
TensorFlow
PT
PyTorch
HF
Hugging Face
SM
spaCy
OAI
OpenAI
LC
LangChain
LG
LangGraph
LS
LangSmith
MC
MCP
VD
Vector DBs
D
Docker
K
Kubernetes
G
Git
GH
GitHub
aws
AWS
Az
Azure
C
Cursor AI
Real-time projects

You don't watch videos. You ship software.

Three full-production projects, each threaded through the entire curriculum. By the capstone, you've built the whole stack around them.

Hero project · weeks 3–12

LMS analytics platform

Ingest learner events, build transformation layers, and publish executive and academic dashboards with AI-generated insight summaries.

PySparkDatabricksPower BILangGraphPostgreSQL
View project →
Enterprise · weeks 6–11

HRMS data pipeline

Build secure ETL workflows for employee, payroll, and performance datasets with governed semantic models and decision-ready KPIs.

MS FabricDelta LakePower BIUnity Catalog
Real-time · weeks 8–12

CRM intelligence stream

Create near real-time customer analytics with streaming events, automated anomaly flags, and AI-assisted executive reporting.

Structured StreamingKQLPower BILangChain
Capstone · weeks 11–12

Your AI Business Analyst agent in a real partner org.

Pick a real partner data problem. Deploy a production data pipeline and an AI agent that explains metrics, detects risks, and accelerates business decisions.

2026: 220+ deployed76% → placement offers
See capstone gallery →
Your instructor

Taught by engineers who shipped agentic AI to production.

Not a career trainer. A practitioner who still ships code.

AS
Aarav Sharma
Lead Instructor · Business Analyst & AI
React · FastAPI · PyTorch · LangChain
"A 2026 full-stack engineer doesn't stop at React + an API. They train the model, deploy it behind FastAPI, wrap it in an agent, and ship the whole thing to a real org. That's what we build, every cohort."
10 yrs
FULL STACK
2,400+
LEARNERS
4.9 /5
RATING

Aarav started as a React engineer at an Indian unicorn before leading platform teams across three continents. He's shipped React + FastAPI products for a healthcare network with 80M users, trained NLP classifiers in production for a top-3 bank, and — most recently — deployed the first LangGraph agent into a Fortune-500 insurer's claims pipeline.

His cohorts get two things other programs don't give you: a real engineer who still ships code, and a curriculum rewritten every quarter to match what hiring managers actually ask about.

FAQ

Questions we actually get — answered honestly.

If the answer you need isn't here, book a 20-minute advisor call. No-slides, no-pitch — just your questions.

No. About 40% of our Business Analyst cohort comes from non-CS backgrounds — mechanical, electrical, and commerce. The first phase is foundations by design. What you need: consistency and around 12–15 hours/week.
Plan for 12–15 hours: 2 live classes × 2 hours, 1 lab × 3 hours, and roughly 5 hours of asynchronous project work. Weekends are optional office hours with the TA team.
Yes. Every student gets a dedicated placement advisor from week 8 onwards — not a helpdesk. They review your resume, redo your LinkedIn, mock-interview you, and make direct warm introductions to our 1,000+ hiring partners. We track individual outcomes, not cohort averages.
Full refund within 7 days of cohort start, no questions. Pro-rata refund through week 4 if the program isn't working for you. We'd rather refund than have an unhappy alum.
You actually build. Sections 6 (ML), 7 (DL/NLP), and 8 (Generative + Agentic AI) are hands-on — you'll train classifiers, build a RAG pipeline, ship a LangGraph workflow, and deploy your capstone agent into a real partner org. Nothing in the AI track is theory-only.
You get the Agent-Ready 2026 credential, graded on a 1–5 band with a public verification URL. It's co-branded with our partner ecosystem (Salesforce Partner + Business Analyst ), and it names the specific capstone artifact you deployed. Recruiters can verify in 10 seconds.
All three. On-campus at our Hyderabad flagship; online cohorts on IST and PST; weekend cohorts for working professionals. Every format ships the same three projects and the same capstone.
We'd rather pause your cohort than push you through. You can freeze your seat for up to 90 days and rejoin the next cohort without paying again. TAs run catch-up sessions every Saturday for anyone more than one week behind.

Cohort 014 starts 14 May 2026.
40 seats. 12 already claimed.

Book a 20-minute advisor call. We'll walk through the curriculum, match it to your current role, and show you two real capstones from cohort 022.

₹89,000
₹1,20,000
25% off · EARLY BIRD
3 MONTHS · STARTS 14 MAY · 40 SEATS · 12 CLAIMED

Get Skilled

Call UsCall Us