SB
Spandana Bala
Data Analyst
Hyderabad · India
Now at · Microsoft
Class 014 · DATA ANALYST & AI AGENTS · ANALYTICS + AGENTIC AI
Data Analyst
+ AI
Agents
Master end-to-end analytics with Agentic AI. Build the full toolkit — Excel through PivotTables, production Power BI dashboards with DAX and RLS, Pandas data wrangling, PostgreSQL queries — and deploy a Data Analyst AI Agent that generates SQL and reports from natural language.
3mo
duration
30+
modules
4.7/5
class rating
100k+
enrolled
Where our Data Analyst alumni work
Microsoft
Amazon
Salesforce
ServiceNow
Deloitte
Infosys
Accenture
TCS
Wipro
Capgemini
Cognizant
HCL
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
What you leave with
Four things every Data Analyst grad walks away with.
01
Agent-Ready analyst skills
The complete analyst toolkit — Excel, Power BI (DAX, RLS), SQL, Pandas — plus LLM-generated SQL and an MCP-driven AI layer that real GCC and Big-4 panels expect in 2026.
02
A shipped project
A production-deployed Data Analyst AI Agent built on LangGraph + Claude Agent SDK + MCP that drafts SQL, builds dashboards, and answers ad-hoc questions — with a public verification URL.
03
Verifiable credential
2026 Agent-Ready rubric mapped to PL-300, DP-900, MO-201 Excel Expert, IBM and Google Data Analytics, graded 1–5, with a public verification URL recruiters can check in 30 seconds.
04
Direct placement pipeline
GitHub + LinkedIn portfolio rewrite, analyst-tuned resume rebuild, and warm intros into our 1,000+ hiring partners actively staffing Data Analyst, BI, and AI-Augmented Analyst roles.
3 MONTHS · FOUR PHASES · ONE DATA ANALYST AGENT
From “writes a VLOOKUP” toships autonomous analytics agents..
Weeks 1–2 · Foundations
IT & AI Foundations + Analyst Mindset
- Cloud computing models (IaaS, PaaS, SaaS) and the analyst mindset
- Introduction to AI, ML, Generative AI, and Agentic AI
- From data to decisions — analyst workflow fundamentals
- Toolchain setup — Excel, Power BI Desktop, Python in VS Code, pgAdmin
YOU SHIPA configured analyst toolchain — Excel, Power BI Desktop, Python, pgAdmin — plus your first Excel exploration of the reference dataset.
Weeks 3–8 · BI Tools (Power BI + Excel)
Power BI + Excel for Data Analysis
- Power BI Desktop, Power Query, and data modelling with star schemas
- DAX measures, CALCULATE, FILTER, and time intelligence
- Power BI Service with workspaces, sharing, and Row-Level Security
- Advanced Excel — XLOOKUP, PivotTables, Power Pivot, and dashboards
YOU SHIPA Power BI dashboard suite with star-schema modelling, DAX, and RLS, plus an Excel workbook with PivotTables and an executive dashboard.
Weeks 9–14 · Programming + Data Layer
Python & SQL for the Modern Analyst
- Python fundamentals, data structures, and file I/O for analysts
- Pandas DataFrames, groupby, merging, and Matplotlib visualisation
- PostgreSQL — DDL, DML, JOINs, GROUP BY, HAVING, and CTEs
- Advanced SQL with window functions and EXPLAIN ANALYZE optimisation
YOU SHIPA Pandas data manipulation portfolio plus a PostgreSQL analytics portfolio connected to a real e-commerce dataset with automated refresh.
Weeks 15–18 · GenAI + Agentic AI
Master the 2026 GenAI + Agentic AI stack — and ship a Data Analyst AI Agent that drafts SQL from natural language, automates Power BI reports, and answers ad-hoc data questions autonomously.
Engineer with LLM APIs from OpenAI, Anthropic, Google GenAI, and DeepSeek. Master prompt engineering (zero-shot, few-shot, CoT, ReAct) and context engineering — the 2026 frontier discipline. Build production RAG pipelines with ChromaDB and pgvector over your data dictionary, business glossary, and historical reports. Master the 2026 production agent stack — LangGraph 1.0 (#1 production default), Claude Agent SDK (#2 MCP-native), CrewAI (#3 multi-agent crews). Wire it all through the Model Context Protocol (MCP) — 200+ server implementations, 97M+ monthly SDK downloads. Final project — a deployed Data Analyst AI Agent with MCP servers exposing your databases, Power BI workspaces, and Excel files. The analyst’s force multiplier.
Partner orgs
(2026)48
Data Analyst
projects deployed350+
→ Placement
offers88%
Course curriculum
Nine sections. 75+ modules. The full AI-native stack.
01
Fundamentals of IT & AI
Foundational track building the conceptual bedrock every modern data analyst
needs — 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 (PostgreSQL, MySQL) and NoSQL (MongoDB)
The seven SDLC phases — Planning, Analysis, Design, Implementation, Testing,
Deployment, Maintenance
The analyst sits at the seam of business and tech — knowing how applications work
makes you a better requirements partner
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 with story points
Backlog management with Google Sheets and Azure Boards
CPU vs GPU — when each matters for analytics workloads
Memory, storage, and network basics
Why these matter for query performance and Power BI Service capacity
IaaS, PaaS, SaaS — the three deployment models
Public, private, hybrid cloud
Cloud data warehouses (Snowflake, BigQuery, Redshift) — analyst-relevant context
AI is reshaping analytics in 2026 — from AI-generated SQL to autonomous report
drafting to RAG-powered data dictionaries
Machine Learning — algorithms that improve through experience
Deep Learning — neural networks for complex pattern recognition
Generative AI — systems that generate SQL, narratives, dashboards
Large Language Models — LLMs that draft analyses from data summaries
Agentic AI — autonomous systems that plan, reason, act, and learn — the future of
analytics
CRM — Salesforce, Dynamics, HubSpot — analyst-facing data
HRMS — Workday, SAP SuccessFactors — sensitive HR analytics
Retail & E-Commerce — Omnichannel commerce metrics
Healthcare Applications — regulated workloads, HIPAA/DPDP compliance
Industry domain knowledge multiplies your analyst salary — invest in domain depth
alongside tool depth
02
Power BI for Data Analysis
Microsoft Power BI is the industry-leading business intelligence platform. This
section takes you from fundamentals through enterprise deployment — connecting to data sources,
modelling relationships, creating interactive dashboards, and publishing insights with proper
governance. 10 modules grouped into four progressive units.
BI fundamentals and modern analytics approaches that turn data into competitive
advantage
Power BI components — Desktop, Service, Mobile, Gateway architecture
Interface navigation — master the workspace and create your first report
Understanding Desktop vs Service capabilities
File sources — Excel, CSV, JSON
Database connections — SQL Server, Oracle, PostgreSQL
Cloud services — Azure, AWS, Google Cloud
Web sources and APIs
Import — data copied into Power BI (best performance for most scenarios)
DirectQuery — live connection to source (real-time data, large volumes)
Live Connection — Analysis Services models
Master data transformation with Power Query's intuitive interface
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
Star schema versus snowflake schema design
Creating and managing table relationships
Primary and foreign keys
Hierarchies and date dimension tables
Data model optimisation strategies
Get the data model right and every later DAX measure becomes easier
Data visualisation principles and chart selection
Core visualisations — charts, tables, maps, KPIs
Interactive elements — slicers, filters, bookmarks, drill-through
Dashboard layout, mobile optimisation, storytelling with data
DAX syntax and structure
Calculated columns vs measures
Essential functions — aggregation, logical, text, date/time
CALCULATE and FILTER functions — the heart of DAX
Creating KPIs and business metrics
Year-over-year, quarter-over-quarter comparisons
Year-to-date, quarter-to-date, month-to-date
Period-over-period growth calculations
Custom calendar handling
Iterator functions — SUMX, AVERAGEX, COUNTX
ALL, ALLEXCEPT for filter manipulation
Variables in DAX for performance and readability
AI visuals — Q&A, Key Influencers, Decomposition Tree
Smart Narrative for auto-generated insights
Workspaces — types and roles
Apps for distribution
Sharing and access management
Subscriptions and alerts
Row-Level Security (RLS) — securing data at the row level
Dynamic security with USERPRINCIPALNAME()
Sensitivity labels and information protection
DAX performance tuning
Composite models and aggregations
Foundation for the Microsoft Certified: Power BI Data Analyst Associate (PL-300)
certification
03
Excel & Advanced Excel for Data Analysis
The section unique to this programme. Microsoft Excel remains the world's most
widely used data analysis tool, combining accessibility with powerful analytical capabilities. This
comprehensive section progresses from fundamental spreadsheet skills through advanced formulas,
PivotTables, and automation — equipping you with the expertise to handle complex data analysis tasks
and create sophisticated analytical models that no other Indian programme covers at this depth.
Ribbon and interface navigation
Cell references — Relative, Absolute, Mixed
Basic formulas and operators
Essential functions — SUM, AVERAGE, COUNT, MIN, MAX
Named ranges for self-documenting spreadsheets
Number, Currency, Date/Time formats
Custom number formats
Conditional formatting — data bars, colour scales, icon sets
Cell and sheet protection
File formats — .xlsx, .xlsm, .csv
Data validation — dropdown lists, input restrictions
Sorting and filtering
Excel Tables (Ctrl+T) — structured references and automatic expansion
Removing duplicates
Flash Fill for pattern-based data entry
Excel Tables — advanced features
Structured references in formulas
Slicers connected to tables
Dynamic ranges for charts
Text functions — CONCATENATE, LEFT, RIGHT, MID, LEN, FIND, SUBSTITUTE, TEXT
Date functions — TODAY, NOW, DATE, YEAR, MONTH, DAY, WEEKDAY, NETWORKDAYS
DATEDIF for age and tenure calculations
Logical functions — IF, AND, OR, NOT
Nested IF statements
IFS, SWITCH (Excel 2019+)
Choosing the right chart type
Column, bar, line, pie, scatter charts
Combo charts and dual-axis
Sparklines for in-cell trends
Chart formatting and best practices
Nested IF statements, IFS, SWITCH functions
Comprehensive error handling with IFERROR, IFNA, ISERROR
VLOOKUP, HLOOKUP — classic lookups
XLOOKUP and XMATCH — modern lookups (Excel 365)
INDEX-MATCH combinations for flexible data retrieval
Approximate vs exact match strategies
Spill ranges and # operator
Array formulas
Modern dynamic array functions — FILTER, SORT, SORTBY, UNIQUE, SEQUENCE,
RANDARRAY
Dynamic data manipulation patterns
SUMIF, SUMIFS for conditional summing
COUNTIF, COUNTIFS for conditional counting
AVERAGEIF, AVERAGEIFS for conditional averaging
MAXIFS, MINIFS for conditional extremes
Mathematical functions — ROUND, MOD, ABS, CEILING, FLOOR
Financial functions — PMT, FV, PV, NPV, IRR
Advanced date functions — EDATE, EOMONTH, WORKDAY
The shift from VLOOKUP to XLOOKUP is one of the highest-leverage upgrades in any
analyst's toolkit
Creating PivotTables from various sources
Fields — Rows, Columns, Values, Filters
Value settings — Sum, Count, Average, % of Total, Running Total
Grouping — Dates, Numbers, Custom groups
Calculated fields and items
Styles and formatting
Slicers and Timelines for interactive filtering
PivotCharts for visual analysis
Get & Transform data interface
Connecting to Excel, CSV, Folders
Database connections
Data transformation steps
Query editing and refresh
Combining multiple sources
Automated data preparation — the Excel-side counterpart to Power BI Power Query
Once you've used Power Query in Excel, you'll never paste-and-clean a CSV by hand
again
Import multiple tables into the Data Model
Create relationships between tables
Analyze data across millions of rows with Power Pivot
The database-in-Excel pattern that scales beyond the 1M-row limit
Learn Data Analysis Expressions basics for Power Pivot
Calculated columns and measures
DAX learned in Excel transfers directly to Power BI — you're learning two tools
at once
Goal Seek to find input values that produce a target result
Scenario Manager for comparing alternatives
Data Tables for sensitivity analysis with one or two variables
Apply the Solver add-in to find optimal solutions for complex problems
Working with constraints
Maximizing or minimizing objective functions
Linear programming patterns for analysts
Foundation for the Microsoft Office Specialist: Excel Expert (MO-201)
certification
04
Python for AI & Data
The dominant language for data analysis automation. 10 modules from environment
setup through OOP — the language fluency that lets you go beyond Excel and SQL when the analysis
demands it.
Python interpreter installation for Windows and Mac
Visual Studio Code IDE + Jupyter for analysis workflows
Python's 35 essential keywords
Variables and memory management
Simple and complex data types
Type conversion and casting
Arithmetic, comparison, and logical operators
Conditional statements — if, elif, else, match-case
while and for loops with range()
break, continue, pass statements
String fundamentals — indexing, slicing, concatenation
f-strings and .format() for analysis output
String methods — case conversion, search, trimming, replacement
.split() and .join() for text data preprocessing
Essential for cleaning text data from APIs and reports
Lists — creation, indexing, slicing, modification
List comprehensions for elegant data transformation
Sorting, reversing, copying patterns
Tuples — creation and operations
Tuple packing and unpacking
Use cases for immutability
Dictionaries — creation, access, operations
Dictionary comprehensions
Nested dictionaries for structured data
Essential for representing JSON API responses
Sets and the UUU properties (Unique, Unordered, Unindexed)
Mathematical operations — union, intersection, difference
Frozen sets for immutability
Collections module — namedtuple, Counter, defaultdict, deque
Iterators & Generators — memory-efficient streaming
Generator expressions and pipelines
Functional programming — lambda, map, filter, reduce
Function definition, parameters, return values
Default arguments, *args, **kwargs
Variable scope (LEGB rule)
First-class functions and higher-order patterns
Type hints (Python 3.5+) for self-documenting analysis code
Documenting functions with docstrings
Built-in modules, user-defined modules, packages
pip for package management
requirements.txt for reproducible analyses
Virtual environments for project isolation
Reproducibility is everything in analytics — invest here
CRUD operations with open()
File modes and pathlib
Directory management with os and shutil
Python's csv module — reader, writer, DictReader, DictWriter
The most common analyst data format
JSON operations — dump(), dumps(), load(), loads()
API responses, configuration files
openpyxl for .xlsx files
Reading and writing Excel programmatically
Excel automation patterns
Exception Handling — robust error handling for unreliable data sources
Decorators — for logging analysis runs, timing functions, caching expensive
queries
Generators deep dive — memory efficiency for larger-than-RAM datasets
Context Managers — proper resource management for database connections, file
handles
Four patterns that separate scripting Python from production analyst Python
Classes & Objects
Methods — instance, class, static
Special methods (__init__, __str__, __repr__)
Encapsulation — controlling data visibility
Inheritance — single, multi-level, and multiple inheritance
Abstraction — abstract classes and methods
Polymorphism — method overriding and duck typing
Most analyst code doesn't need OOP — but custom analysis frameworks definitely do
05
SQL for AI & Data
The data backbone of every analyst's daily workflow. Five modules covering
PostgreSQL from foundations through advanced analytics queries — window functions, CTEs, and query
optimization. The data layer that powers your dashboards and reports.
Databases, DBMS, RDBMS — concepts and terminology
ACID properties — Atomicity, Consistency, Isolation, Durability
PostgreSQL setup, psql, pgAdmin 4, DBeaver
Data types — numeric, character, date/time, boolean, JSON, arrays
Constraints — PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, CHECK
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 and HAVING
Window functions — ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD
JOIN operations — INNER, LEFT, RIGHT, FULL OUTER, CROSS, SELF
Subqueries — scalar, row, table subqueries
CTEs (Common Table Expressions) — the analyst's favourite for readable queries
Recursive CTEs for hierarchical data
Set operators — UNION, UNION ALL, INTERSECT, EXCEPT
DML — INSERT, UPDATE, DELETE patterns
Transactions — BEGIN, COMMIT, ROLLBACK
ALTER TABLE for schema evolution
Indexes — B-tree, Hash, GiST, GIN
Views — virtual tables, materialized views for caching analytical aggregates
Stored functions with CREATE FUNCTION
PL/pgSQL — variables, control structures, exception handling
Triggers — automation for data quality checks
ER (Entity-Relationship) modelling
Normalization — 1NF, 2NF, 3NF
OLTP vs analytics workload patterns
Star schema for analytics
Query plan analysis with EXPLAIN and EXPLAIN ANALYZE
Index strategies
VACUUM, ANALYZE, partitioning
An analyst who can read EXPLAIN ANALYZE is twice as productive on slow queries
06
Generative AI & Agentic AI
The 2026 differentiator — and what separates an Excel-only analyst from an
AI-augmented analyst. 10 modules covering the complete GenAI engineering stack tuned for analytics
work: frontier models, prompt engineering, RAG over your data dictionary, agent frameworks, and the
Model Context Protocol. The named Data Analyst AI Agent project lives here.
Narrow AI — image classifiers, speech recognition (pre-2022)
Generative AI — LLMs, image/video/audio generation (post-2022)
Agentic AI — Plan / Reason / Act / Learn loops (post-2024)
2022 inflection point — ChatGPT launch
2024 inflection point — Agentic emergence
AI that drafts SQL from natural language
AI that summarises Power BI dashboards into executive narratives
Autonomous report generation (with human approval)
GPT-5.5 — The Autonomous Agent. Terminal-Bench 2.0 leader at 82.7%. Best for
autonomous analytical research
Claude Opus 4.7 — The Precision Coder. SWE-bench Pro leader at 64.3%, lowest
hallucination rate at 36%. Best for accurate SQL generation
Gemini 3.1 Pro — The Context Giant with 2M+ token context window. Best for
ingesting entire data dictionaries and historical reports
Open-source frontier — Llama 4, DeepSeek, Mistral, Qwen — for VPC deployments
with sensitive analytics data
Copilot in Excel — formula suggestions, formula explanations, data analysis
Copilot in Power BI — natural language Q&A, DAX suggestions, auto-generated
visuals
Copilot in Word — narrative writing for analyses
Copilot in PowerPoint — executive presentation drafts
Perplexity — citation-grounded research for industry benchmarks
NotebookLM — long-document analysis for RFPs and contracts
ChatGPT Codex — agentic environment for SQL and Python automation
Fundamentals — Context + Task + Examples + Format + Constraints
Core Techniques — Zero-shot, few-shot, Chain-of-Thought (CoT), ReAct
System Prompts — persistent persona design, guardrails
Multimodal — reading dashboards, charts, hand-drawn sketches
Hallucination & Context — grounding for accurate analytical statements
Domain & Library — analyst-specific prompt patterns
Context Engineering — managing what enters the LLM's context window for accurate
analysis
Project — ship a 20+ prompt library for analyst work (SQL generation, narrative
writing, dashboard descriptions, etc.)
Using ChatGPT, Claude, and Gemini for daily analyst work
AI for SQL writing, formula generation, narrative drafting
Research with Perplexity for industry benchmarks
Microsoft Copilot integration — Excel, Power BI, Word, PowerPoint, Outlook, Teams
AI for stakeholder communication — drafting emails, summaries, status reports
Building analyst-specific AI workflows that save 10+ hours per week
Reading dashboards and charts with vision models
Analysing screenshots of competitor dashboards
OCR for legacy reports and scanned documents
Image generation for analyst presentation materials
Multimodal LLMs now read your scatter plots, identify outliers, and write the
executive summary — your competitive advantage just changed
Hallucination — when an LLM invents a statistic that doesn't exist in your data
Prompt injection — when an attacker poisons your AI's context through documents
Privacy — keeping sensitive business data out of public LLMs
Security — secrets management when AI tools have access to databases
Regulatory landscape — EU AI Act, India DPDP Act
Always validate AI-generated SQL against actual data before sharing results
Streamlit — rapid prototyping for internal analytics apps
FastAPI — production-grade Python API for AI analytics services
Building chatbots for ad-hoc data Q&A
Building diagnostic agents for data quality monitoring
Build and deploy a Streamlit + FastAPI internal tool that answers analytics
questions from your team's data dictionary
LLM APIs in production — OpenAI, Anthropic, Google GenAI, DeepSeek Python SDKs
API patterns — completions, chat, streaming, function calling, structured outputs
Cost tracking for AI API spend
Function calling & structured outputs — the 2026 production pattern for reliable
JSON
Pydantic-validated structured outputs — type-safe AI
Embeddings — OpenAI text-embedding-3-large, Voyage, Cohere 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
Building RAG over your data dictionary, business glossary, and historical reports
Hybrid search (BM25 + embeddings) for technical analytics documentation
Agentic RAG — self-improving retrieval over your analytics knowledge base
Multi-step retrieval — find the right report, find the right table, find the
right metric
Project — Internal Analytics RAG App: RAG over your team's reports, data
dictionary, and historical analyses; deployed with hybrid search and ChatOps integration
LangGraph 1.0 — complex stateful workflows — the production default for agentic
analytics
Claude Agent SDK — deepest MCP integration, critical for analyst tool calls into
databases and BI workspaces
CrewAI — role-based multi-agent crews; use case: an "analyst team" of agents
(Query Writer, Visualisation Builder, Narrative Writer, Reviewer)
Semantic Kernel / Microsoft Agent Framework — enterprise .NET stacks
Pydantic AI — type-safe Python, validation-first agent design
ReAct — investigate a data question, then propose a query
Plan-and-Execute — generate a multi-step analysis plan
Reflection loops — agent reviews its own SQL before executing
Multi-agent collaboration — Query agent fetches data, Viz agent builds chart,
Writer agent drafts narrative
Human-in-the-loop checkpoints — humans approve every stakeholder-facing artefact
MCP — the open standard for connecting agents to tools, data, and systems
Proposed by Anthropic in late 2024, now stewarded by the Linux Foundation
200+ server implementations and 97M+ monthly SDK downloads
Adoption across Anthropic, OpenAI, Google, Microsoft, AWS, and 50+ partners
For analysts — MCP servers exist for PostgreSQL, MySQL, Snowflake, BigQuery,
Power BI, Tableau, Microsoft Teams, and dozens more
Build an MCP server exposing PostgreSQL for safe query execution by your agent
Build an MCP server exposing Power BI workspaces for dashboard automation
Build an MCP server exposing Excel files for spreadsheet automation
Connect LangGraph agents to multiple MCP servers
Use Claude Agent SDK's deepest native MCP integration
A2A Protocol — Google-led agent-to-agent communication standard with 50+ launch
partners, Linux Foundation governance
DATA ANALYST AI AGENT CAPSTONE — multi-agent Data Analyst AI Agent using
LangGraph + Claude Agent SDK with MCP servers exposing your PostgreSQL database, Power BI
workspaces, and Excel files
Agent generates SQL from natural language, automates Power BI report production,
drafts analytical narratives, and answers ad-hoc data questions
Frontend with Streamlit, backend with FastAPI, observability via LangSmith —
human approval gates for every stakeholder-facing artefact — the named project for the entire
Data Analyst & AI Agents programme
Tools you'll master
32+ analytics & 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
Executive analytics workspace with LLM copilot
Ship a full executive analytics workspace — a dbt-modeled warehouse, a Tableau / Power BI dashboard suite, and a Hex/Mode LLM copilot that lets execs ask analyst questions in plain English and get back the SQL, the rows, and the chart.
01Modeled SQL warehouse — fact/dim tables in Snowflake/BigQuery, dbt-tested, scheduled with Airflow.
02Tableau / Power BI dashboard suite — exec, ops, finance views with row-level security and what-if parameters.
03LLM copilot in Hex/Mode that answers analyst questions in natural language, citing the SQL it ran and the rows it scanned.
04Self-serve discovery layer — Looker explore + Streamlit decision app with Mixpanel telemetry showing usage.
Outcome: ~70% faster monthly close
Self-serve: 90% of execs
Reviewer: Analytics Engineering panel
SQLdbtTableauHexLLM Copilot
Product analytics · weeks 6–10
Funnel + cohort analytics
Build a product analytics workspace — event taxonomy, GA4/Amplitude/Mixpanel pipelines, retention & cohort dashboards, an LLM that explains drops in plain English.
GA4MixpanelPandasLLM
Real-time · weeks 8–12
Real-time finance dashboard
Stream order events into a near-real-time Power BI dashboard, automate variance flagging with a Python notebook + LLM commentary on every refresh.
Power BIStreamingPythonLLM
Project · weeks 11–12
Your AI analyst workspace in a real partner org.
Pick a real partner business problem. Ship a dbt-modeled warehouse, a Tableau / Power BI dashboard suite, and a Hex/Mode LLM copilot — into a partner team that's running it for real users.
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 · AI & Data Science Architect
Python · PyTorch · TensorFlow · scikit-learn · Hugging Face · LangChain
"A 2026 data scientist doesn't stop at notebooks. They ship the training pipeline,
stand up the model behind FastAPI, monitor drift in production, and wire an LLM into the loop so the
business actually understands what the model is saying. That's the bar I teach to, every cohort."
15 yrs
AI & DATA SCIENCE
2,400+
LEARNERS
4.9 /5
RATING
Manikanta is the founder of Digital Lync and brings 15 years of applied AI & data science from AT&T, Salesforce, Cox Communications, and Broadcom — where he led recommendation, fraud, forecasting, NLP and computer-vision systems for Fortune-500 banks, telcos, and insurers. Most recently he architected production ML pipelines that pair classical and deep models with an LLM augmentation layer that explains predictions to business stakeholders.
His classes get you two things other programs don't give you: a founding architect who still ships production ML, and a curriculum rewritten every quarter to match what hiring managers actually ask about — credentials like AWS Machine Learning Specialty, Azure AI Engineer, Databricks ML Associate, TensorFlow Developer, and Pragmatic AI Engineer included. M.S. in Engineering, Purdue University.
RK
Ravi Krishna
Chief Technologist, Digital Lync · Analytics Engineering Lead
SQL · dbt · Tableau · Power BI · DAX · Hex · LLM Copilots
"Analytics engineering is where dashboards stop being screenshots and start being a
production system — a dbt-modeled warehouse you can stake an SLA on, Tableau / Power BI suites with row-level
security, and an LLM copilot in Hex/Mode that cites the SQL it ran. That's the bar I teach to."
10 yrs
ANALYTICS
1,800+
LEARNERS
4.8 /5
RATING
Ravi is Chief Technologist at Digital Lync, where he leads the analytics engineering practice. After ten years building dbt-modeled warehouses across enterprise — finance, retail, telecom, and SaaS — he stepped into the Chief Technologist seat to wire dbt, Tableau, Power BI, and Hex into the way analyst teams actually work — semantic layers that stay accurate through schema changes, dashboard suites with row-level security, and LLM copilots that on-call analysts don't fight with.
His analytics modules are built from real production post-mortems, not slide decks. Expect to leave with working dbt projects, a Tableau / Power BI dashboard suite, a Hex/Mode LLM copilot wired into the warehouse, and an analyst workflow you can stake an SLA on. Ten years analytics engineering, most of them shipping dbt-modeled warehouses and LLM-augmented analyst workflows into enterprise — Hyderabad-based, hands-on, and known for the unglamorous parts of analytics that everyone else skips.
HIRING PARTNERS · INDUSTRY VOICES
What analytics employers say about Digital Lync grads.
Real feedback from analytics and BI leaders at AI-first companies and the firms hiring our Data Analyst + AI graduates.
Digital Lync grads ramp 40% faster on analytics deliveries than typical analyst hires. Best Data Analyst + AI pipeline in India.
Aakash Mehta, Analytics Director, Microsoft
We've onboarded 80+ Digital Lync alumni in 18 months. Lowest ramp time we've seen for production dashboards and LLM copilots practices.
Anita Sharma, Senior Manager, Deloitte
The Data Analyst + AI programme is comprehensive — SQL, dbt, Tableau, LLM copilots. Grads come pre-trained for self-serve analytics with AI.
Rahul Bhatt, Solutions Lead, Mphasis
Their dbt + LLM-copilot track produces PMs who ship production-grade dashboards on day one. Rare combination of data rigor and analytical craft.
Deepak Pillai, Senior Architect, TCS
What sets Digital Lync apart is the LLM-copilot layer baked into the analytics track. Our enterprise clients ask for exactly this profile.
Suresh Menon, Practice Lead, Accenture
Their Tableau Desktop Specialist + PL-300 prep is rigorous, and the shipped project — dbt warehouse, dashboard suite, LLM copilot — is what closes interviews for us.
Vikram Iyer, Director, Infosys
Digital Lync's Data analysts ship trustworthy dashboards twice as fast in the first 90 days. Our internal analytics metrics back this up clearly.
Lakshmi Nair, VP Analytics, Wipro
Best Data Analyst + AI pipeline we've sourced from in India. Their projects are real shipped dashboards, not screenshots.
Karthik Subramanian, Engineering Director, Cognizant
Strong SQL and analytics-engineering foundation. Their Data Analyst grads need almost zero ramp time on enterprise analytics engagements with us.
Arun Joshi, Practice Director, Capgemini
We've placed 40+ Digital Lync alumni across our analytics and watsonx BI teams. Strong fundamentals, sharp on eval and self-serve analytics.
Sanjay Verma, Talent Director, IBM
dashboards + LLM copilots 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 Data Analyst track delivers analysts who navigate SQL, dbt, and BI tools on customer engagements unsupervised.
Ramesh Iyer, Senior Manager, Tech Mahindra
Hired 25+ Digital Lync graduates for our analytics practice. Strong on SQL, sharp on DAX, fluent in LLM copilots.
Geetha Pillai, Talent Acquisition Lead, Cyient
Digital Lync grads who blend analytics with Azure OpenAI copilots 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 Data Analyst
Presented to
Spandana Bala
For the successful design, build, and shipping of a production analytics workspace —
dbt-modeled warehouse, dashboard suite, and an LLM copilot — evaluated against the Tableau Desktop Specialist, PL-300 (Power BI Data Analyst), and dbt Analytics Engineer credential rubrics.
Manikanta Kona
CEO · Digital Lync
AGENT
READY
2026
READY
2026
01
Industry‑recognized
Co‑branded with the analytics community and mapped to Tableau Desktop Specialist
and PL-300 (Power BI Data Analyst) credentials — names that hiring managers already scan for on resumes.
02
Project artifact included
Every certificate carries your shipped project — dbt warehouse, Tableau/Power BI dashboard suite, LLM copilot — with
a link to the live partner-org deployment. Proof, not a promise.
03
Enhanced skill validation
Graded against the 2026 Agent‑Ready rubric: SQL, dbt models, dashboards, LLM copilots,
self-serve enablement. 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 Data Analyst offer isn't a lottery ticket. It's a built process.
GitHub, LinkedIn, resume — and most importantly, warm intros into analytics-driven SaaS and enterprise teams. Our
placement team works your search like an account, not a helpdesk.
01 / GITHUB & PORTFOLIO
A portfolio, not a graveyard.
Guidance on building a portfolio that showcases your dbt warehouse, dashboard suite, LLM copilot dashboard, self-serve decision app, and a public verification URL — reviewed 1:1, not via template.
02 / RESUME PREP
Rewrite, don't proofread.
A one-page resume rebuilt around the analytics workspaces you shipped (dashboards, dbt warehouses, LLM copilots), the partner-org project, and the business outcome. Reviewed by analytics leaders who've read 10,000+ resumes.
03 / LINKEDIN + INTROS
Where most opportunities actually live.
Profile tuning plus direct warm introductions into analytics-driven SaaS and enterprise teams — Microsoft, Snowflake, Databricks, Salesforce/Tableau, Atlassian, Looker, Mode, Hex, Fivetran, dbt Labs, Anthropic, Hugging Face, Stripe, Razorpay, Freshworks, plus services that staff analytics teams (Deloitte, Accenture, Cognizant, TCS). You leave with recruiter contacts, not a generic "good luck."
Data Analyst alumni
Hundreds of analytics careers launched — here are eight.
NV
Naveen Vedala
Senior BI Analyst
Hyderabad · India
Now at · Atlassian
TA
Tejashwini Addla
Analytics Engineer
Hyderabad · India
Now at · Salesforce
TD
Tharunesh Dillikar
Lead Data Analyst
Seattle · United States
Now at · dbt Labs
MM
Mujahed Mohammed
Tableau Developer
Hyderabad · India
Now at · Databricks
BK
Bhargav Kumar Murala
Power BI Specialist
Hyderabad · India
Now at · Adobe
SL
Sai Manasa Leburi
dbt Analytics Engineer
New York · United States
Now at · Hugging Face
RD
Rahul Dhamma
Director of Analytics
Hyderabad · India
Now at · Snowflake
Our locations
Come chat with us — over coffee, or over Zoom.
One flagship campus in Hyderabad, plus online Lead Data Analyst 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 Data Analyst cohorts running on IST and PST. Every online cohort ships
the same shipped project — dbt warehouse, dashboard suite, LLM copilot, self-serve app — 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 analytics stack, certifications, and placement. If something's missing, book a 20-minute advisor call — no slides, no pitch.
Do I need a CS background or prior SQL experience?
No on both counts. Roughly 40% of every class comes from non-CS streams — commerce, finance, engineering, BCom, BBA, and first-time data folks. Weeks 1–2 cover the SQL fundamentals, data modeling, and dashboard craft from scratch. What you do need: consistency and 12–15 hours a week.
Will I actually ship dashboards, or only learn theory?
You actually ship. Every learner builds a dbt-modeled warehouse, a Tableau / Power BI dashboard suite with row-level security, and a Hex/Mode LLM copilot wired to OpenAI/Claude. The project is a deployed analytics workspace inside a partner org — not a screenshot deck.
Which tools, BI suites, and AI models will I use?
Warehousing & SQL: Snowflake, BigQuery, PostgreSQL, MSSQL, dbt. BI: Tableau, Power BI, Looker, Mode, Hex, Metabase. Code: Python, Pandas, Polars, NumPy, Streamlit, DAX. AI: OpenAI, Claude, LangChain, Cursor AI. Product analytics: GA4, Mixpanel, Amplitude, Segment.
Will I prep for AIPMM Data Analyst and Pragmatic Lead Data Analyst certs?
Yes. The curriculum is mapped to the AIPMM Data Analyst track and the Pragmatic Lead Data Analyst credential. 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 building dashboards in your training warehouse, and ~5 hours of project work (SQL, dbt, BI). Saturday office hours with the TA team are optional, but most learners use them.
Is placement support really 1:1, and which companies hire data analysts?
Yes — a dedicated placement advisor from week 8, not a helpdesk. AI product hiring partners include Microsoft, Adobe, Salesforce, Atlassian, Notion, Linear, Anthropic, Hugging Face, Databricks, Snowflake, Stripe, Razorpay, Freshworks, Zoho, and Postman. 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 shipped project — dbt warehouse, dashboard suite, LLM copilot, self-serve app — 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 DAA-024 starts 1 Jun 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 022.
