SB
Spandana Bala
Power BI Developer
Hyderabad · India
Now at · Infosys
Class 014 · Power BI & AI Agents · Enrolling Now
Power BI
+ AI
Agents
Master Microsoft Power BI from raw data to enterprise analytics — Power Query, star schema modelling, DAX, RLS, and Copilot — paired with Fabric and a Generative + Agentic AI project.
4mo
duration
30+
modules
4.7/5
class rating
100k+
enrolled
Where our Power BI alumni work
Microsoft
Amazon
Salesforce
ServiceNow
Deloitte
Infosys
Accenture
TCS
Wipro
Capgemini
Cognizant
HCL
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
What you leave with
Four things every Power BI grad walks away with.
01
Agent-Ready BI skills
Build Copilot-powered dashboards, scalable semantic models, and RAG analytics agents with LLM APIs and vector DBs — AI-augmented BI, not drag-and-drop.
02
A shipped project
A production RAG app on ChromaDB/Pinecone with hybrid search, Streamlit UI on FastAPI, wired into a live Power BI semantic model — public URL.
03
Verifiable credential
2026 Agent-Ready rubric mapped to PL-300, DA-100, PL-900, and AI Specialist — graded 1–5 with a public verification URL.
04
Direct placement pipeline
GitHub + LinkedIn rewrite, BI-tuned resume rebuild, and warm intros into our 1,000+ hiring partners staffing Power BI and Analytics Engineer roles.
4 months, four phases
From “pivots Excel” to ships RAG-powered BI agents..
WEEKS 1–2 · Foundations
IT & AI Foundations + Excel Mastery
- Application lifecycle, Agile methodologies, cloud computing, AI foundations
- Excel formulas, PivotTables with slicers and timelines, PivotCharts
- Power Query in Excel — Get & Transform, automated data preparation
- Excel Data Model, Power Pivot, DAX basics, What-If Analysis, Solver
YOU SHIPAn automated Excel dashboard built on Power Pivot with a multi-table Data
Model, ready to graduate into Power BI.
WEEKS 3–8 · POWER BI
Power BI for Data Analysis (the centerpiece)
- BI fundamentals, Power BI architecture, Desktop vs Service, data source connectivity
- Power Query transformations, star schema modelling, relationships, hierarchies, date dimensions
- DAX — calculated columns, measures, CALCULATE/FILTER, time intelligence, advanced patterns
- Custom visuals, AI features, Copilot, publishing, sharing, RLS, OLS, incremental refresh, governance
YOU SHIPA production-grade Power BI semantic model with a published workspace,
Copilot-augmented reports, RLS-secured access, and a deployment pipeline running across Dev → Test → Prod.
WEEKS 9–12 · DATA STACK
Python for Data + PostgreSQL for AI
- Python fundamentals, data structures, automation, AI-driven applications
- PostgreSQL — SELECT, JOINs, aggregates, window functions, CTEs, subqueries
- Advanced PostgreSQL — stored procedures, triggers, PL/pgSQL, performance tuning
- ER modelling, normalisation (1NF/2NF/3NF), indexes, design best practices
YOU SHIPAn end-to-end pipeline — Python ingestion → PostgreSQL transformation → Power
BI dashboard, with automated refresh and clean traceability.
WEEKS 13–16 · GENERATIVE + AGENTIC AI
Deploy production RAG-powered analytics agents that automate insight generation, retrieval, and reporting across your data stack.
LangGraph 1.0 with state, persistence and HITL approvals. MCP-powered tool integration. Multi-agent routing via Google A2A. Observed via LangSmith, governed for the EU AI Act. Walk out with a production agent embedded in a real Power BI workspace.
Partner orgs
(2026)48
Projects
deployed280+
→ Placement
offers82%
Course curriculum
Nine sections. 75+ modules. The full AI-native stack.
01
Fundamentals of IT & AI
Foundational track building the conceptual bedrock for every data professional —
application lifecycle, Agile/Scrum, computing infrastructure, AI/ML/Generative/Agentic AI
fundamentals, and real-world digital systems.
Application fundamentals — what applications are, their types, web architecture
Web Technologies — Frontend (HTML, CSS, JavaScript, React) and Backend (Python,
Java, Node.js)
Database Systems — SQL (MySQL, PostgreSQL) and NoSQL (MongoDB) for data
management
The seven SDLC phases — Planning, Analysis, Design, Implementation, Testing,
Deployment, Maintenance
How each phase builds on the previous for systematic development of robust
applications
Understanding the SDLC is fundamental to managing complex software projects
Methodology Evolution — Waterfall vs Agile, the Agile mindset
Popular frameworks — Scrum, Kanban, Extreme Programming (XP)
Scrum Roles — Product Owner, Scrum Master, Development Team
Scrum Events — Sprint Planning, Daily Scrum, Sprint Review, Sprint Retrospective
Scrum Artifacts — Product Backlog, Sprint Backlog, Increment deliverables
User Stories — Epics, Themes, Acceptance Criteria
Estimating user stories and managing backlogs with Google Sheets and Azure Boards
Transparent communication, progress tracking, collaborative planning across
distributed teams
CPU Technology — general-purpose computing, sequential operations, multi-core
parallel processing
GPU Technology — parallel processing for AI training, data processing, scientific
simulations
IaaS — Infrastructure as a Service: virtualised servers, storage, networking
PaaS — Platform as a Service: development and deployment environments without
infrastructure management
SaaS — Software as a Service: ready-to-use applications via web browser
Understanding fundamental computing technologies and cloud service models is
essential for navigating today's digital landscape
Machine Learning — algorithms that improve through experience, learning from data
patterns without explicit programming
Deep Learning — neural networks with multiple layers that process complex
patterns in large datasets
Generative AI — systems that create new content, from text to images, based on
learned patterns
Large Language Models — LLMs that process and generate human-like text, powering
chatbots, content creation, and translation
Image Generation — AI models creating original images from text descriptions
Customer Relationship Management — CRM systems centralising customer
interactions, sales pipelines, marketing campaigns
Human Resource Management Systems — HRMS solutions for recruitment, payroll,
performance management, employee records
Retail & E-Commerce — digital commerce platforms integrating inventory, payment
processing, customer analytics
Healthcare Applications — medical software managing patient records, appointment
scheduling, diagnostic support
Understanding real-world digital systems demonstrates how the technology stack
delivers measurable business value
02
Power BI for Data Analysis
Microsoft Power BI is the industry-leading business intelligence platform that
transforms raw data into compelling visual insights — connecting to diverse data sources, modelling
relationships, creating interactive dashboards, and publishing insights that drive business
decisions. Aligns with the PL-300 Power BI Data Analyst Associate certification.
Business intelligence fundamentals and modern analytics
Power BI components and architecture
Interface navigation and first report creation
Understanding Desktop vs Service capabilities
Power BI ecosystem overview — Desktop, Service, Mobile, Report Server
The role of Power BI in the modern data stack
First report — load a CSV, build a simple visual, publish to the service
File, database, cloud, and web source connectivity
Connection Modes — Import vs DirectQuery vs Live Connection
Data source settings and credential management
Performance considerations across connection modes
On-premises Data Gateway configuration
Common connectors — SQL Server, Azure SQL, SharePoint, Excel, web APIs
Choosing the right connection mode is the most important early architectural
decision
Power Query interface and the Applied Steps methodology
Data profiling and quality assessment
Essential transformations — filtering, splitting, merging columns
Reshaping — pivot, unpivot, grouping
Combining queries — append and merge operations
M language fundamentals for custom transformations
Parameters, query folding, and performance optimization
Hands-on lab — clean a messy CSV using only Power Query, no manual editing
Star schema vs Snowflake schema design patterns
Creating and managing table relationships
Primary and foreign keys, cardinality, cross-filter direction
Hierarchies and date dimension tables
Data model optimisation strategies
The cost of bidirectional relationships
Calculated columns vs measures — when to use each
A clean star schema is the single biggest determinant of dashboard performance
Core Visualisations — charts (column, bar, line, area, scatter), tables,
matrices, maps, KPIs
Interactive elements — slicers, filters, bookmarks, drill-through, drill-down
Data visualisation principles and chart selection
Dashboard layout and visual hierarchy
Mobile optimisation and responsive design
Storytelling with data — narrative flow and progressive disclosure
Accessibility considerations — colour-blind safe palettes, alt text
DAX syntax and structure
Calculated columns vs Measures — context, performance, use cases
Essential functions — aggregation, logical, text, date/time
The CALCULATE and FILTER functions — the heart of DAX
Creating KPIs and business metrics
Common DAX patterns for business questions
Row context vs filter context — the mental model that unlocks DAX
If you only learn one DAX function deeply, make it CALCULATE
Time intelligence functions — YTD, MTD, QTD calculations
Prior period comparisons and growth rates
Filter context vs Row context in depth
Variables (VAR) and iterator functions (SUMX, AVERAGEX, FILTER)
DAX performance optimisation patterns
Custom calendars and fiscal year handling
Year-over-year, month-over-month, rolling averages
Hands-on — build a complete time-intelligence pack for a sales semantic model
Custom visuals from AppSource and Microsoft AppSource certified visuals
Advanced chart types — waterfall, funnel, decomposition tree, key influencers
R and Python integration for statistical and custom visuals
AI Visuals — Key Influencers, Q&A natural language, Smart Narratives
Dynamic visuals with parameters and field parameters
Personalised visuals and per-user customisation
Smart Narratives gives auto-generated executive summaries — use them as starting
points, not final copy
Publishing to the Power BI Service and workspace management
Dashboards vs Reports — the conceptual difference
Data refresh and gateway configuration
Sharing strategies — workspaces, apps, direct sharing, embedded reports
Integration with Teams, SharePoint, Excel, PowerPoint
Power BI Apps for packaged, versioned distribution
Subscriptions, alerts, and email delivery
Hands-on — publish a complete workspace, package as an app, and distribute to a
test audience
Power BI admin portal and tenant settings
Row-Level Security (RLS) — static and dynamic patterns
Object-Level Security (OLS) — hiding tables and columns from users
Incremental refresh and aggregations
Dataflows for reusable ETL logic
Deployment pipelines (Dev → Test → Prod)
Performance optimisation and capacity management
Enterprise licensing models — Pro, Premium Per User, Premium Capacity
APIs and embedded analytics for custom applications
03
Excel & Advanced Excel for Data Analysis
Microsoft Excel remains the world's most widely used data analysis tool,
combining accessibility with powerful analytical capabilities — progressing from fundamental
spreadsheet skills through advanced formulas, PivotTables, and automation that feed directly into
Power BI.
Ribbon and interface mastery
Cell References — Relative, Absolute, Mixed
Basic formulas and operators; essential functions — SUM, AVERAGE, COUNT
Named ranges for readable formulas
Formatting — Number, Currency, Date/Time formats, custom number formats,
conditional formatting
Cell and sheet protection; file formats — .xlsx, .xlsm, .csv
Data validation rules, sorting and filtering, Excel Tables (structured
references)
Removing duplicates and Flash Fill for pattern-based extraction
Standard charts — Column, Bar, Line, Pie, Scatter with proper formatting; titles,
axes, legends, data labels
Advanced charts — Combo charts with secondary axes, sparklines for inline
visualizations
Dynamic dashboards combining multiple visualizations with text and date functions
Text functions — LEFT, RIGHT, MID, CONCATENATE, TEXTJOIN, TRIM
Date/Time functions — TODAY, NOW, DATE, DATEDIF, WORKDAY, NETWORKDAYS
Logical functions — IF, AND, OR, NOT, IFS, SWITCH
Advanced Logic — nested IF, IFS, SWITCH, error handling with IFERROR, IFNA,
ISERROR
Lookup Functions — VLOOKUP, HLOOKUP, modern XLOOKUP and XMATCH, INDEX/MATCH
combinations for flexible retrieval
Dynamic Arrays — Excel 365 spill ranges, FILTER, SORT, SORTBY, UNIQUE
Statistical Functions — SUMIF, SUMIFS, COUNTIF, COUNTIFS, AVERAGEIF, AVERAGEIFS
Specialized Functions — Mathematical (ROUND, MOD, ABS), Financial (PMT, FV, PV,
NPV, IRR), Date (EDATE, EOMONTH)
Once you know XLOOKUP, you almost never need VLOOKUP again — but learning both
helps maintain legacy spreadsheets
Creating PivotTables from various sources; Fields — Rows, Columns, Values,
Filters
Value settings — Sum, Count, Average, % of Total; grouping dates, numbers, custom
groups
Calculated fields and calculated items; styles, formatting, layout choices
Slicers and Timelines for interactive filtering; PivotCharts for visual analysis
Power Query — Get & Transform interface, connecting to Excel, CSV, Folders,
databases
Data transformation steps and the M language; combining multiple sources (Append,
Merge)
Hands-on — build a Power Query pipeline that ingests 12 monthly CSVs, cleans
them, and outputs a single fact table
Excel Data Model & Power Pivot — import multiple tables, create relationships,
analyse millions of rows
DAX Fundamentals in Excel — calculated columns and measures; bridge from Excel
DAX to Power BI DAX
Goal Seek — find input values to hit a target output
Scenario Manager — compare multiple alternative scenarios
Data Tables — sensitivity analysis with one or two variables
Solver add-in for finding optimal solutions to complex problems with constraints
Power Pivot and DAX in Excel are the bridge to Power BI — the same engine, the
same language, scaled up
04
Python for AI & Data
Python has become the dominant programming language for data science and AI —
from basic syntax through object-oriented programming, teaching you to manipulate data structures,
work with files, handle exceptions, and write efficient, maintainable code that powers data analysis
and AI applications.
Environment setup — Python 3.12+, virtual environments, pip
IDE setup — VS Code with Python extensions, Jupyter notebooks
Syntax basics, code style, PEP 8 conventions
Variables, data types — int, float, string, bool, None
Operators — arithmetic, comparison, logical, identity, membership
Conditionals — if, elif, else, ternary expressions
Loops — for, while, break, continue, else clauses on loops
User input and output formatting
String operations — concatenation, repetition, slicing
Indexing and slicing — positive and negative indices
String formatting — % formatting, .format(), f-strings
String methods — case conversion (.upper(), .lower(), .title())
Searching and replacing — .find(), .index(), .replace()
Splitting and joining — .split(), .join()
Stripping whitespace and unwanted characters
Text processing patterns for data cleaning
Lists — creation, indexing, slicing; methods: append, insert, extend, remove,
pop, clear; list comprehensions
Tuples — creation, operations, immutability; packing and unpacking; when to use
tuples vs lists
Dictionaries — creation, access; methods: keys, values, items; dictionary
comprehensions; nested dictionaries
Sets — creation, Unique/Unordered/Unindexed properties; mathematical operations
(union, intersection, difference)
Subset and superset checks; frozen sets for immutable, hashable collections
The collections module — Counter, defaultdict, OrderedDict, namedtuple, deque
Iterators and the iterator protocol
Generators and yield for memory-efficient iteration
Lambda functions for inline anonymous logic
Functional programming — map, filter, reduce
When to use generators vs list comprehensions
Function definition, parameters, return values
Default arguments, *args, **kwargs
Scope — local, enclosing, global, built-in (LEGB rule)
Modules and the import system; packages and the __init__.py pattern
pip for package management and requirements.txt
Working with CSV files using the csv module
Working with JSON files using the json module
Classes and objects — the building blocks of OOP
Methods — instance methods, @classmethod, @staticmethod
Special / magic methods — __init__, __str__, __repr__, __len__
Encapsulation — public, protected (_), private (__) access modifiers
Inheritance — single, multi-level, multiple inheritance, super()
Abstraction — Abstract Base Classes (abc module), abstract methods
Polymorphism — method overriding, duck typing, flexible interfaces
Opening, reading, writing files — open(), with statements
File modes — r, w, a, b, x, +
Reading line by line vs reading entire file
Writing and appending data; working with binary files
Working with paths using pathlib
Handling encoding (UTF-8, latin-1)
Exception Handling — try/except/else/finally blocks, catching specific
exceptions, raising and re-raising, custom exception classes
Decorators — function decorators, decorators with arguments, multiple decorators,
class decorators; practical applications (logging, timing, authentication, caching)
Generators Deep Dive — generator expressions, infinite generators, memory
efficiency, advanced iteration patterns for large datasets
Context Managers — with statement, custom context managers via __enter__ and
__exit__, resource management, automatic cleanup
These four patterns are what separate scripting Python from production Python
NumPy as the foundation for numerical computing in Python
Array Creation — zeros(), ones(), arange(), linspace(), random()
Array Operations — indexing, slicing, fancy indexing, reshaping, transposing
Broadcasting — operations on arrays of different shapes without explicit loops
Mathematical Operations — universal functions (ufuncs), element-wise operations
Linear Algebra — dot products, matrix multiplication
Statistical Analysis — mean, median, standard deviation, variance, percentiles
If you understand broadcasting deeply, your data code will be 10-100x faster than
equivalent loops
05
SQL for AI & Data
Structured Query Language is the universal language for working with relational
databases — covering PostgreSQL from foundations through advanced programming: database design,
complex queries, joins, transactions, stored procedures, triggers, and optimization. Skills transfer
directly to Power BI semantic models and AI applications.
Databases, DBMS, RDBMS — concepts and terminology
ACID properties — Atomicity, Consistency, Isolation, Durability
PostgreSQL setup and installation (local + cloud); connecting via psql, pgAdmin,
and DBeaver
Data types — numeric, character, date/time, boolean, JSON, arrays
Constraints — PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, CHECK
Creating, altering, and dropping tables
PostgreSQL is the de facto open-source enterprise RDBMS — same skills transfer to
MySQL, SQL Server, Snowflake
SELECT statements and column projection; WHERE clauses with operators and
conditions
Built-in functions — string, numeric, date, conditional
Aggregates — COUNT, SUM, AVG, MIN, MAX; GROUP BY for aggregation; HAVING for
post-aggregation filtering
Window functions — ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, running totals
JOIN operations — INNER, LEFT, RIGHT, FULL OUTER, CROSS, SELF
Window functions are the single most underused SQL feature — master them and
solve analytics problems in one query that others write 50 lines for
Subqueries — scalar, row, table subqueries
CTEs (Common Table Expressions) — WITH clauses for readable complex queries
Recursive CTEs for hierarchical data
Set operators — UNION, UNION ALL, INTERSECT, EXCEPT
DML — INSERT, UPDATE, DELETE patterns and best practices
Transactions — BEGIN, COMMIT, ROLLBACK, savepoints
Isolation levels — Read Uncommitted, Read Committed, Repeatable Read,
Serializable
Concurrency control — locks, deadlocks, MVCC
ALTER TABLE for schema evolution
Indexes — B-tree, Hash, GiST, GIN; when to use each; creating, modifying,
dropping indexes
Views — virtual tables, materialized views, refresh strategies
Stored functions — CREATE FUNCTION in PostgreSQL
PL/pgSQL — variables, control structures, loops, exception handling
Stored procedures vs functions
Triggers — BEFORE, AFTER, INSTEAD OF triggers; row-level vs statement-level
Exception handling and error management in stored code
ER (Entity-Relationship) modelling — entities, attributes, relationships,
cardinality
Normalization — 1NF, 2NF, 3NF (and when to deliberately denormalize)
Design best practices for OLTP vs analytics workloads
Naming conventions, surrogate vs natural keys
Query plan analysis with EXPLAIN and EXPLAIN ANALYZE
Index strategies — selectivity, covering indexes, multi-column indexes; query
rewriting for performance
Statistics, VACUUM, ANALYZE; partitioning for large tables
An index makes reads fast and writes slow — never add an index without measuring
both
06
Generative AI & Agentic AI
The production AI engineering destination of the programme — from the 70-year
arc of AI history to deploying production RAG-powered agents. Builds the complete 2026 GenAI
engineering stack: frontier models, prompt engineering, multimodal AI, LLM APIs, vector databases,
agentic frameworks, and the Model Context Protocol.
Narrow AI — image classifiers, speech recognition — the pre-2022 era of
task-specific intelligence
Generative AI — LLMs, image/video/audio generation — the post-2022 era unleashed
by ChatGPT
Agentic AI — Plan / Reason / Act / Learn loops, tool use — the post-2024 era of
autonomous systems
2022 inflection point — ChatGPT launch: Generative AI enters mainstream
consciousness and professional workflows
2024 inflection point — Agentic emergence: AI systems begin planning, using
tools, and completing multi-step tasks autonomously
What's Coming 2026–2030 — increasingly capable reasoning models, deeper tool
integration, multi-agent collaboration at scale, systems that learn continuously from real-world
feedback
GPT-5.5 — The Autonomous Agent. Natively omnimodal, Terminal-Bench 2.0 leader at
82.7%, OSWorld-Verified at 78.7%. Best for autonomous agents, computer use, terminal automation.
40% token efficiency gain over GPT-5.4. Pricing: $5/$30 standard, $30/$180 Pro
Claude Opus 4.7 — The Precision Coder. Hybrid reasoning with extended thinking
mode. SWE-bench Pro leader at 64.3%, lowest hallucination rate on long-form work at 36%. Deepest
native MCP support. Best for production code review and complex software engineering. Pricing:
$5/$25
Gemini 3.1 Pro — The Context Giant. Natively multimodal, GPQA Diamond leader at
94.3%, ARC-AGI-2 leader at 77.1%. 2M+ token context window. Three thinking levels
(low/medium/high). Best for high-volume batch jobs and multimodal media analysis. Pricing: $2/$12
Open-Source Frontier — Llama 4 (Meta), DeepSeek (reasoning-focused), Mistral
(GDPR-aligned, multilingual), Qwen (Alibaba's frontier-competitive open weights)
Intelligent Routing — Opus 4.7 for writing and code review; GPT-5.5 for
autonomous agents; Gemini 3.1 Pro for cost-sensitive and multimodal work; mini/nano sub-models for
high-volume orchestration
Microsoft Copilot Suite — Word, Excel, PowerPoint, Outlook, Teams integration;
Copilot Studio for enterprise no-code agent building
Specialised Tools — Perplexity (citation-grounded AI search), NotebookLM
(long-document analysis), ChatGPT Codex (agentic coding environment)
Fundamentals — anatomy of a great prompt: Context + Task + Examples + Format +
Constraints
Core Techniques — zero-shot, few-shot, Chain-of-Thought (CoT), ReAct,
Tree-of-Thought, role-based, output format, negative prompting
System Prompts — architectural difference, persistent persona design, guardrails,
extended thinking modes
Multimodal prompting — vision, image generation, audio, video; Gemini 3.1 Pro's
native video and audio analysis
Hallucination & Context — grounding, self-verification, citation prompting, token
budgeting, lost-in-the-middle mitigation
Context Engineering — governs everything that enters the context window:
retrieved documents, memory, tool outputs, conversation history, system instructions
Domain-Specific Patterns — marketing, research, code generation, customer
service, legal and finance review
Project — ship a 30+ prompt library on GitHub that recruiters will actually open
Using ChatGPT, Claude, and Gemini for everyday productivity
Document drafting, summarisation, and editing workflows
Research workflows with Perplexity and NotebookLM
Email, meeting, and task management with AI assistants
Microsoft Copilot in Word, Excel, PowerPoint, Outlook, Teams
Copilot for Power BI — generating DAX, summarising reports, narrative insights
AI for code — GitHub Copilot, Cursor, Claude Code
Building personal AI workflows that compound over weeks
Vision capabilities — image understanding, OCR, chart reading, screenshot
analysis
Image generation — Stable Diffusion, DALL-E, Midjourney, Imagen
Audio — speech-to-text (Whisper), text-to-speech, voice cloning
Video — generation (Sora, Runway), analysis (Gemini 3.1 Pro native video)
Native multimodal vs adapter-based multimodal — architectural differences
Practical applications — accessibility, content creation, data extraction from
images
Cost and latency considerations for multimodal workloads
Bias in AI systems — sources, detection, mitigation
Hallucination — why it happens, how to reduce it, how to verify
Privacy considerations — PII, data residency, on-prem vs cloud
Security — prompt injection, jailbreaks, data exfiltration risks
Regulatory landscape — EU AI Act, India DPDP Act, sector-specific rules
Ethical AI principles — fairness, transparency, accountability, human oversight
Building human-in-the-loop systems for high-stakes decisions
Responsible AI is not a separate workstream — it's woven into every design
decision
Streamlit — rapid AI app prototyping in pure Python
Building chat interfaces, dashboards, and demos
Session state, callbacks, and reactive patterns
FastAPI — production-grade Python API framework
Async endpoints for high-throughput AI workloads; request validation with
Pydantic
Authentication, rate limiting, observability
Deploying to Render, Railway, Vercel, or a VPS
Hands-on — build and deploy a Streamlit + FastAPI chatbot that connects to your
Power BI semantic model
LLM APIs in Production — OpenAI, Anthropic, Google GenAI, DeepSeek Python SDKs;
completions, chat, streaming, function calling, structured outputs; rate limits, retries, cost
tracking
Function Calling & Structured Outputs — Pydantic-validated structured outputs,
type-safe AI, handling function calling failures
Embeddings — OpenAI text-embedding-3-large, Voyage, Cohere embedding models
Vector Databases — ChromaDB (local/dev), Pinecone (managed), Qdrant
(open-source), pgvector (PostgreSQL); indexing strategies: HNSW, IVF
RAG Pipeline — Chunk → Embed → Index → Retrieve → Augment → Generate; chunking
strategies (fixed-size, semantic, hierarchical); hybrid search (BM25 + embeddings); re-ranking
with cross-encoders
Agentic RAG — self-improving retrieval, multi-step retrieval, MCP-enhanced RAG
Project — Production RAG App with ChromaDB or Pinecone, hybrid search,
re-ranking, Streamlit UI, FastAPI backend, cost tracking, observability. Deployed to a public URL
LangGraph 1.0 — complex stateful workflows, graph-based state machines,
human-in-the-loop, LangSmith observability. The production default
Claude Agent SDK — powers Claude Code, deepest MCP integration, extended
thinking, Anthropic-native production
CrewAI — role-based multi-agent crews, fastest prototyping, native MCP + A2A
support
Semantic Kernel / Microsoft Agent Framework — enterprise .NET stacks, AutoGen +
Semantic Kernel merged, GroupChat debate pattern
Pydantic AI — type-safe Python, validation-first agent design
Core Agent Patterns — ReAct (Reasoning + Acting), Plan-and-Execute, Reflection
loops, Multi-agent collaboration (supervisor, swarm, debate), Human-in-the-loop checkpoints
Production agents are 90% about state management, observability, and
human-in-the-loop checkpoints — the LLM is the easy part
What MCP Is — the open standard for connecting agents to tools, data, and
systems; proposed by Anthropic in late 2024; now stewarded by the Linux Foundation with 200+
server implementations and 97M+ monthly SDK downloads
Adoption — Anthropic, OpenAI, Google, Microsoft, AWS, and 50+ partners; write the
integration once, every agent uses it
Build an MCP server exposing tools and resources with authentication
Connect LangGraph agents to multiple MCP servers via adapters
Use Claude Agent SDK's deepest native MCP integration
Connect CrewAI via crewai-tools[mcp]
Build MCP-enhanced RAG pipelines
Tools you'll master
32+ Power BI & AI tools, one production project.
Real-time projects
You don't watch videos. You ship software.
Three full-production projects, each threaded through the entire curriculum. By the project, you've built the whole stack around them.
Hero project · weeks 3–12
Sales & Finance executive dashboard on Fabric
Build an end-to-end executive analytics platform — Fabric lakehouse ingestion, a Direct Lake semantic model, and a Copilot for Power BI experience that drafts variance narratives and answers natural-language questions live on the dashboard.
01Live Fabric workspace with bronze/silver/gold lakehouse, Dataflows Gen2 ingestion, and Direct Lake semantic model.
02Composite DAX model with row-level security, calculation groups, and field parameters wired to a Power BI Service workspace.
03Copilot for Power BI drafting variance narratives, suggesting visuals, and generating natural-language Q&A on the fly.
04Refresh + alert pipeline that flags revenue/forecast gaps and writes to Teams + email with a Fabric Activator trigger.
Outcome: ~60% faster monthly close
Report build time: −45%
Reviewer: Microsoft MVP panel
Power BIFabricDAXCopilotDirect Lake
Enterprise · weeks 6–11
Finance forecasting + RLS workspace
Multi-perspective DAX model with row-level security, scenario calculation groups, what-if parameters, and a Copilot agent that drafts FP&A commentary on every refresh.
DAXRLSCalc GroupsCopilot
Real-time · weeks 8–12
Real-time ops streaming dashboard
Stream KQL events into a Power BI real-time dashboard, classify anomalies with Fabric ML, and auto-create alerts with AI-drafted root-cause summaries.
KQLReal-Time IntelligenceFabric MLActivator
Project · weeks 11–12
Your AI Power BI agent in a real partner org.
Pick a real partner workflow. Deploy a production Power BI + Fabric solution — Direct Lake semantic model, Copilot agent, Activator alerts — that handles board-level reporting and accelerates decisions.
Download the real world project
Full scope, sample partner orgs, weekly milestones, and grading rubric — PDF, 14 pages.
2026: 220+ deployed76% → placement offers
Your instructor
Taught by engineers who shipped agentic AI to production.
MK
Manikanta Kona
Founder, Digital Lync · Principal Power BI Architect
Power BI · DAX · Microsoft Fabric · Copilot · Sales / Finance / Operations / Marketing analytics
"Power BI at enterprise scale is where AI earns its keep — Copilot plugged into board-level dashboards, Fabric orchestrating Sales, Finance, Operations, and Marketing analytics as one semantic fabric. That's the bar I teach to, every cohort."
15 yrs
POWER BI
2,400+
LEARNERS
4.9 /5
RATING
Manikanta is the founder of Digital Lync and brings 15 years of enterprise data & analytics architecture from AT&T, Salesforce, Cox Communications, and Broadcom — where he led Power BI, Fabric, and data-warehouse rollouts for Fortune-500 banks, telcos, and insurers. Most recently he architected production Copilot for Power BI + Microsoft Fabric deployments that replaced static monthly decks with live, agent-assisted decision dashboards.
His classes get you two things other programs don't give you: a founding architect who's shipped enterprise Power BI + AI from inside the Fortune 500, and a curriculum rewritten every release — so when hiring managers ask about Direct Lake semantic models, calculation groups, RLS, or Copilot governance, you've already built it. M.S. in Engineering, Purdue University.
RK
Ravi Krishna
Chief Technologist, Digital Lync · Data & Fabric Lead
Power BI · DAX · Microsoft Fabric · Copilot · OneLake · Azure Synapse · KQL
"Power BI stops being a chart tool and starts being the nervous system of the
enterprise the moment Fabric is wired in — OneLake feeding a Direct Lake semantic model you can trust,
DAX that holds up under RLS, and Copilot drafting the narrative before the meeting starts. That's what
I teach."
10 yrs
DATA & BI
1,800+
LEARNERS
4.8 /5
RATING
Ravi is Chief Technologist at Digital Lync, where he leads the Data & Fabric practice. After 8 years building and running production data platforms on Azure Synapse, Databricks, and Power BI, he stepped into the Chief Technologist seat to wire Microsoft Fabric, Copilot, and OneLake into the way analytics teams actually work — semantic models that survive a re-org, DAX that scales beyond a billion rows, and governance so finance leadership trusts the numbers.
His Fabric and DAX modules are built from real production rebuilds, not slide decks. Expect to leave with working Direct Lake semantic models, calculation groups + field parameters, row-level security patterns, and a Copilot agent you can actually demo. Ten years at Digital Lync, eight of them shipping data & BI in production — Hyderabad-based, hands-on, and known for the unglamorous parts of analytics that everyone else skips.
HIRING PARTNERS · INDUSTRY VOICES
What Power BI employers say about Digital Lync grads.
Real feedback from talent leaders at Microsoft Fabric partners and the firms hiring our Power BI + AI Copilot graduates.
Digital Lync grads ramp 40% faster on Fabric and Copilot for Power BI projects than typical hires. Best Power BI training pipeline in India.
Aakash Mehta, Partner Programme Lead, Microsoft
We've onboarded 80+ Digital Lync alumni in 18 months. Lowest ramp time we've seen for Power BI plus Microsoft Fabric practices.
Anita Sharma, Senior Manager, Deloitte
The Power BI programme is comprehensive — DAX, Fabric, plus Copilot governance. Grads come pre-trained for enterprise analytics teams.
Rahul Bhatt, Solutions Lead, Mphasis
Their DAX + Fabric track produces engineers who write production-grade semantic models on day one. Genuinely rare.
Deepak Pillai, Senior Architect, TCS
What sets Digital Lync apart is the Copilot for Power BI layer baked into the Fabric track. Our enterprise clients ask for exactly this profile.
Suresh Menon, Practice Lead, Accenture
Their PL-300 + DP-600 prep is rigorous, and the project deployment on a real Fabric tenant is what closes interviews for us.
Vikram Iyer, Director, Infosys
Digital Lync's Power BI grads cut report build time in half in the first 90 days. Our internal metrics back this up clearly.
Lakshmi Nair, VP Engineering, Wipro
Best Power BI + AI Copilot pipeline we've sourced from in India. Their projects are production work on Fabric, not toy dashboards.
Karthik Subramanian, Engineering Director, Cognizant
Strong DAX and semantic model foundation. Their Power BI grads need almost zero ramp time on enterprise Fabric engagements with us.
Arun Joshi, Practice Director, Capgemini
We've placed 40+ Digital Lync alumni across our Power BI and watsonx-on-Fabric teams. Strong DAX fundamentals, sharp on the Copilot stack.
Sanjay Verma, Talent Director, IBM
Direct Lake + Real-Time Intelligence is exactly the talent gap we've been struggling to close. Digital Lync is filling it for us reliably.
Anjali Desai, Practice Head, LTIMindtree
Their Power BI track delivers consultants who navigate DAX, Power Query, and Copilot governance on customer engagements unsupervised.
Ramesh Iyer, Senior Manager, Tech Mahindra
Hired 25+ Digital Lync graduates for our Power BI practice. Strong DAX, strong Fabric depth, sharp on the Copilot layer.
Geetha Pillai, Talent Acquisition Lead, Cyient
Digital Lync grads who blend Power BI with Azure OpenAI and Copilot land production-ready on day one. Rare combination, well-trained.
Priya Reddy, Talent Lead, Microsoft
03Program certifications
An Agent‑Ready credential, not a participation trophy.
Digital Lync · Institute Certificate
Agent‑Ready Power BI Engineer
Presented to
Spandana Bala
For the successful design, build, and production deployment of a Direct Lake
semantic model and Copilot for Power BI agent on Microsoft Fabric, evaluated against the 2026 Agent‑Ready
rubric and PL‑300 / DP‑600 competencies.
Manikanta Kona
CEO · Digital Lync
AGENT
READY
2026
READY
2026
01
Industry‑recognized
Co‑branded with the Microsoft Fabric partner ecosystem and mapped to PL‑300 and DP‑600 —
names that hiring managers already scan for on resumes.
02
Project artifact included
Every certificate carries your project project name, the partner org, and a link to the
deployed Power BI + Fabric Copilot artifact — proof, not a promise.
03
Enhanced skill validation
Graded against the 2026 Agent‑Ready rubric: DAX modelling, Fabric pipeline build, Copilot
deployment, governance and monitoring. No pass/fail — a level 1‑5 band.
04
Verifiable on a public URL
Each credential has a public verification page recruiters can check in 10 seconds — no PDF
back‑and‑forth.
04Job placement support
Your first Power BI offer isn't a lottery ticket. It's a built process.
GitHub, LinkedIn, resume — and most importantly, warm intros into Microsoft Fabric partners. Our
placement team works your search like an account, not a helpdesk.
01 / GITHUB PROFILE
A portfolio, not a graveyard.
Guidance on building a GitHub that showcases your PBIX files, Direct Lake semantic models, Fabric workspace artifacts, DAX patterns, and a working Copilot agent — reviewed 1:1, not via template.
02 / RESUME PREP
Rewrite, don't proofread.
A one-page resume rebuilt around the Power BI stacks you implemented (DAX, Fabric, Direct Lake), the Copilot agent you deployed, and the business outcome. Reviewed by engineers who've read 10,000+ resumes.
03 / LINKEDIN + INTROS
Where most opportunities actually live.
Profile tuning plus direct warm introductions into the Microsoft Fabric partner network — Deloitte, Accenture, Cognizant, Capgemini, EY, KPMG, Microsoft, Wipro, Infosys, TCS. You leave with recruiter contacts, not a generic "good luck."
Power BI alumni
Hundreds of Power BI careers launched — here are eight.
NV
Naveen Vedala
Fabric Data Engineer
Hyderabad · India
Now at · TCS
TA
Tejashwini Addla
BI Analytics Lead
Hyderabad · India
Now at · Deloitte
TD
Tharunesh Dillikar
DAX Specialist
Seattle · United States
Now at · Microsoft
MM
Mujahed Mohammed
Copilot Solutions Engineer
Hyderabad · India
Now at · Accenture
BK
Bhargav Kumar Murala
BI Consultant
Hyderabad · India
Now at · Capgemini
SL
Sai Manasa Leburi
Real-Time Intelligence Lead
New York · United States
Now at · EY
RD
Rahul Dhamma
Data Visualization Engineer
Hyderabad · India
Now at · Cognizant
Our locations
Come chat with us — over coffee, or over Zoom.
One flagship campus in Hyderabad, plus online Power BI cohorts running on Indian and US timezones.
Flagship campus
Hyderabad
2nd Floor, Hitech City Road · Above Domino's · Opp. Cyber Towers, Jai Hind Enclave ·
Hyderabad, Telangana
Call
+91 90003 29956
US desk
+1 858 666 6719
Hours
Mon–Sat · 9am–9pm
Online class
Global
Weekend and evening Power BI cohorts running on IST and PST. Every online cohort ships
the same three production projects and Direct Lake + Copilot project as the on‑campus track.
Timezones
IST & PST
Format
Live + 1:1 mentorship
Next class
25 May 2026
FAQ
Questions we actually get — answered honestly.
Straight answers on prerequisites, the platform, certifications, and placement. If something's missing, book a 20-minute advisor call — no slides, no pitch.
Do I need an analytics or SQL background?
No. Roughly 40% of every class comes from non-analytics streams — mechanical, electrical, BCom, BBA — and zero Power BI exposure is assumed. Weeks 1–2 cover Excel modelling, basic SQL, and the Power Query / DAX foundations from scratch. What you do need: consistency and 12–15 hours a week.
Do I get a real Power BI / Fabric tenant to build on?
Yes. Every learner provisions a free Microsoft 365 developer tenant with a Fabric trial workspace from week 1 and keeps it for the full program. Every lab, project, and the project are built on your own live workspace — not a sandbox simulator — so the artifacts you ship are demonstrable to recruiters.
Which tools and AI capabilities will I actually build with?
Core: Power BI Desktop, DAX, Power Query, Power BI Service, Microsoft Fabric, OneLake, Dataflows Gen2, Direct Lake. AI track: Copilot for Power BI (narratives, Q&A, visual generation), Azure OpenAI for custom agents, Fabric Real-Time Intelligence for streaming, and Activator for AI-driven alerting.
Will I be ready for PL-300 and DP-600 certifications?
Yes. The curriculum is mapped to PL-300 (Power BI Data Analyst Associate) and DP-600 (Fabric Analytics Engineer Associate); enterprise-track learners also prep for DP-500. We run two full mock exams and reimburse the voucher fee on first-attempt pass.
What's the time commitment per week?
Plan for 12–15 hours: 2 live classes × 2 hours, 1 lab × 3 hours on your Fabric workspace, and ~5 hours of project work. Saturday office hours with the TA team are optional, but most learners use them.
Is placement support really 1:1, and which companies hire?
Yes — a dedicated placement advisor from week 8, not a helpdesk. Power BI hiring partners include Deloitte, Accenture, Cognizant, Capgemini, EY, KPMG, Microsoft, Wipro, Infosys, TCS, plus the Microsoft Fabric partner network. Resume, LinkedIn, mock interviews, and warm intros are individual.
Online, weekend, or on-campus?
All three. On-campus at the Hyderabad flagship, live online (IST and PST cohorts), and a weekend track for working professionals. Every format ships the same three projects and the same project — only the schedule changes.
What if I fall behind, or can't continue mid-class?
Freeze your seat for up to 90 days and rejoin the next class — no extra fee. TAs run catch-up sessions every Saturday for anyone more than a week behind, and recordings of every live session are available for the lifetime of your account.
Still have a question? Talk to an advisor — no slides, no pitch.
Class PBI-018 starts 25 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 projects from class PBI-017.
