SB
Spandana Bala
Data Analyst
Hyderabad · India
Now at · Microsoft
Data Analyst · SQL · Power BI · Tableau · AI copilots · Agentic analytics · Enrolling now
Data Analyst & AI
A career-focused, hands-on program: build strong SQL, Excel, Power BI and Tableau foundations, then add Python, statistics, dbt semantic layers and AI copilots — delivering trusted metrics, dashboards and agentic analytics the way AI-era analytics teams now work.
100K+
alumni community
1,000+
hiring partners
4.8/5
avg class rating
12
partner centres
7
Digital Edify centres
Where our Data Analyst alumni work
Microsoft
Amazon
Salesforce
ServiceNow
Deloitte
Infosys
Accenture
TCS
Wipro
Capgemini
Cognizant
HCL
MicrosoftAmazonSalesforceServiceNowDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
Direct answer
What is the Data Analyst & AI program?
A data analyst turns business questions into trusted answers — querying and modelling the data, building the dashboards and metrics people rely on, and now directing the AI copilots and analytics agents that do the first pass. Most analyst courses end at a dashboard. This program ends only when you have shipped a governed metrics layer, a dashboard suite stakeholders use, an agentic self-serve analytics experience with guardrails, and a presented business recommendation.
The complete delivery chain
Twelve links, one owner — end to end.
01ASK & ACQUIRE
- •Business question, metric & hypothesis
- •SQL across warehouses & sources
- •Excel & Power Query for fast analysis
- •Data quality, lineage & definitions
02MODEL & ANALYSE
- •dbt models & semantic layers
- •Statistics, cohorts, funnels & A/B tests
- •Python for analysts — pandas & Polars
- •AI copilots for exploration & code
03DELIVER & AUTOMATE
- •Power BI & Tableau dashboards that get used
- •Agentic analytics — text-to-SQL & insight agents
- •Insight narratives & recommendations
- •Governance, refresh & monitoring
Data analytics in 2026
Dashboards got copilots. Analysts became the people who make them trustworthy.
AI copilots in every toolWorkflow shift
Power BI Copilot, Tableau Agent, Excel Copilot, ChatGPT and Claude write SQL, DAX and first-draft charts. Analysts now direct, verify and explain — and spend the saved time on the question and the decision.
Agentic & conversational analyticsNew '26
Databricks Genie, Snowflake Cortex Analyst, Tableau Knowledge, ThoughtSpot and Slackforce Surfaces let stakeholders ask the data directly. Analysts build the semantic layers and guardrails that make the answers right.
Semantic layers & dbtTrust layer
Metrics defined once in dbt, Tableau Knowledge or Power BI semantic models and reused by dashboards, copilots and agents. The analyst's most valuable new skill is owning those definitions.
Modern data stackData layer
Snowflake, BigQuery, Databricks and DuckDB with Polars and Python alongside SQL. Cloud warehouses replaced spreadsheets as the system of record for analysis.
Product & experimentation analyticsDecision layer
Funnels, cohorts, retention and A/B testing with Amplitude, Mixpanel, GA4 and warehouse-native tools. Experimentation literacy is now expected of every analyst.
Governance, privacy & data qualityGovernance layer
Data contracts, lineage, PII handling and the EU AI Act and DPDP obligations that now apply to analytics outputs and AI-generated insights.
What this means for your career: data analyst roles now ask for SQL, a BI tool, Python and AI copilots together — the differentiator is a governed metrics layer, a dashboard suite people use and an agentic self-serve experience you can prove is accurate, not a certificate in one tool.
Who should join
Built for people moving into AI-era data analytics.
Graduates in commerce, engineering, science or arts
Excel-heavy business & finance professionals
Operations, marketing & sales staff
MIS & reporting analysts upgrading to BI + AI
Career switchers into data
Non-technical managers who want data fluency
Prior experience: none required — SQL, Excel, Python and statistics are taught from scratch. The program builds analysis foundations before BI, the modern data stack and AI copilots.
What you will be able to do
Answer the question — and make the answer trusted.
Query and model dataAdvanced SQL, window functions, data modelling and dbt on PostgreSQL, Snowflake and BigQuery.
Analyse with Excel & PythonPower Query, pivots, pandas, Polars, visualisation and reproducible analysis.
Reason with statisticsDescriptive stats, inference, cohorts, funnels and A/B experiments.
Build dashboards people usePower BI and Tableau — data models, DAX, design, performance and governance.
Direct AI copilots and agentsCopilot, ChatGPT and Claude for analysis; text-to-SQL and insight agents on semantic layers with guardrails.
Tell the storyInsight narratives, executive summaries, recommendations and stakeholder management.
Course curriculum
Twelve sections. 52 modules. SQL → Excel → Statistics → Python → BI → Semantic layers → AI agents.
01
Fundamentals of Data & Analytics
How analytics works in an organisation, the modern data stack and where AI copilots fit.
Analyst, analytics engineer, data scientist and BI developer roles
Descriptive, diagnostic, predictive and generative analytics
The modern data stack — warehouse, dbt, BI, copilots
Career pathways and certifications
From business question to metric to analysis plan
Hypotheses, assumptions and bias
Data literacy and common pitfalls
Documenting analysis
Structured, semi-structured and unstructured data
Relational models, keys and grain
Data quality dimensions and profiling
Lineage and definitions
PostgreSQL, DBeaver, Excel, Power BI and Tableau setup
Python and Jupyter for analysts
ChatGPT, Claude and Copilot as analysis partners
Verifying AI-generated work
02
SQL for Analysts
The core skill — from first SELECT to analytical patterns on warehouses.
SELECT, WHERE, ORDER BY and LIMIT
Aggregations and GROUP BY
Joins and set operations
NULL handling
Subqueries and CTEs
CASE logic and conditional aggregation
Date and string functions
Views
Ranking, running totals and moving averages
LAG / LEAD and period-over-period
Cohort, retention and funnel queries
Sessionisation
Star schemas, facts and dimensions
Slowly changing dimensions
Indexes, EXPLAIN and query tuning
Warehouse SQL dialects — Snowflake, BigQuery
Generating and reviewing SQL with ChatGPT, Claude and Copilot
Text-to-SQL accuracy and validation
Prompting with schema context
Debugging generated queries
03
Excel & Spreadsheet Analytics
Still the fastest tool for many business questions — used properly.
Tables, formulas and lookups — XLOOKUP, INDEX / MATCH
Conditional logic and text functions
Data validation and error handling
Keyboard-first workflows
Importing and cleaning with Power Query
Merges, appends and transformations
Data model and relationships
Refreshable reports
Pivot tables and slicers
Chart selection and formatting
Dashboard layouts in Excel
Sharing and protecting workbooks
Copilot for formulas, analysis and charts
Python in Excel for advanced analysis
Verifying AI suggestions
When to move to SQL or BI
04
Statistics & Experimentation for Analysts
The reasoning behind trustworthy conclusions.
Central tendency, spread and shape
Outliers and missing data
Percentiles and segments
Summarising with visuals
Probability basics
Sampling and confidence intervals
Hypothesis tests and p-values
Common misinterpretations
Experiment design and sample size
Metrics and guardrails
Analysing and reporting results
Sequential testing basics
Correlation vs causation
Simple and multiple regression
Trend and seasonality forecasting
Explaining models to stakeholders
05
Python for Data Analysts
Enough Python to analyse anything — and to work with AI coding assistants.
Variables, types, control flow and functions
Lists, dictionaries and comprehensions
Files, CSV and JSON
Environments and notebooks
DataFrames, filtering and joins
Group-by, pivots and reshaping
Time series and dates
Polars and DuckDB for larger data
Matplotlib, Seaborn and Plotly
Chart design principles
Interactive charts and Streamlit apps
Exporting for reports
Scheduled scripts and notebooks
Connecting to warehouses and APIs
Automated Excel and email reports
Version control with Git
Claude Code, Cursor and Copilot for analysis code
Generating, testing and reviewing pandas code
Avoiding silent errors in generated code
Documentation with AI
06
Power BI
Microsoft's BI platform end to end — modelling, DAX, design, governance and Copilot.
Desktop, Service and workspaces
Connecting and transforming data
Data types and query folding
Refresh and gateways
Star schema in Power BI
Relationships, cardinality and direction
Calculated columns vs measures
Model optimisation
Measures, CALCULATE and filter context
Time intelligence
Iterators and variables
Common patterns
Visual selection and layout
Interactions, bookmarks and drill-through
Accessibility and mobile layouts
Performance Analyzer
Workspaces, apps and sharing
Row-level security
Deployment pipelines
Semantic models and certified datasets
Copilot for DAX, visuals and narratives
Microsoft Fabric overview — OneLake, Lakehouse
Copilot-ready semantic models
Verifying Copilot output
07
Tableau & Visual Analytics
Tableau for exploration, storytelling and the new Tableau Next.
Connecting data and the Tableau data model
Dimensions, measures and marks
Filters, sorting and groups
Calculated fields
Level of detail expressions
Table calculations and parameters
Sets, actions and dashboards
Performance
Design principles and visual hierarchy
Story points and narratives
Mobile and accessibility
Publishing to Cloud and Server
Tableau Next on the Salesforce platform
Tableau Agent for natural-language analysis
Tableau Knowledge as a semantic layer
Slackforce Surfaces for sharing insights
08
Modern Data Stack, dbt & Semantic Layers
Own the definitions dashboards, copilots and agents all reuse.
Warehouse concepts and architecture
Loading and querying data
Cost and performance basics
Sharing and governance
Models, sources and refs
Tests and documentation
Incremental models and snapshots
dbt Cloud and CI
Why metrics need one definition
dbt Semantic Layer and MetricFlow
Power BI semantic models and Tableau Knowledge
Metrics for copilots and agents
Tests, freshness and anomaly detection
Data contracts
Lineage and impact analysis
Incident handling for broken dashboards
09
AI Copilots & Agentic Analytics
Direct the AI that does the first pass — and build the agents stakeholders will use.
ChatGPT, Claude, Gemini and Copilot workflows
Prompting for analysis, code and narratives
Data privacy when using AI tools
Verification habits
How LLMs work at an intuition level
Structured outputs and tool calling
Hallucination and its consequences
Cost and model choice
Classifying and summarising text at scale
Sentiment and theme extraction
Embeddings and semantic search basics
Evaluating LLM outputs
Databricks Genie, Snowflake Cortex Analyst and ThoughtSpot
Power BI Copilot and Tableau Agent as agents
Semantic layers as the accuracy foundation
Governance and access
Schema and semantic-layer grounding
Guardrails — read-only, validation, approvals
Evaluating accuracy with golden questions
Deploying in Slack or a web app
Model Context Protocol basics for analysts
Connecting Claude and Cursor to warehouses and dbt
Enterprise MCP — Salesforce, ServiceNow, Snowflake
Security and permissions
10
Product, Marketing & Business Analytics
The analyses businesses ask for most — and how to run them.
Event data and tracking plans
Funnels, cohorts and retention curves
Amplitude, Mixpanel and GA4
Warehouse-native product analytics
Attribution and channel analysis
Segmentation and RFM
Campaign measurement
Customer lifetime value
Revenue, margin and unit economics
Forecasting and variance analysis
Operational KPIs and SLAs
Scenario modelling
North-star metrics and metric trees
Leading vs lagging indicators
Metric definitions and ownership
Avoiding vanity metrics
11
Storytelling, Communication & Governance
Turn analysis into decisions — and keep it trustworthy.
Structuring an insight narrative
Executive summaries and one-pagers
Presenting uncertainty and caveats
Handling questions and pushback
Intake, scoping and prioritisation
Requirements for dashboards and metrics
Managing expectations
Self-serve enablement
PII, GDPR and DPDP in analytics
Access control and row-level security
Responsible use of AI-generated insights
EU AI Act awareness
12
Capstone, Portfolio & Career
A governed metrics layer, a dashboard suite and an agentic analytics experience — verifiable by employers.
Business framing and tracking plan
dbt models and semantic layer on a warehouse
Power BI and Tableau dashboard suite with RLS
Public verification URL
Text-to-SQL and insight agent on the semantic layer
Guardrails and golden-question evaluation
Slack or web delivery
Demo and write-up
Portfolio of dashboards, analyses and agent demos
Resume rewrite around business impact
Analyst interview practice — SQL, case studies, presentations
Warm introductions to hiring partners
Tools you'll master
32+ analytics & AI tools, one production project.
Real-time projects
You don't watch videos. You ship software.
Three portfolio projects and a partner capstone, each threaded through the entire curriculum — SQL, dbt, BI, statistics and AI copilots all land in real deliverables.
Hero project
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
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
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
Your AI analyst workspace in a controlled project environment.
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 deployment contexts, project milestones, and grading rubric — PDF, 14 pages.
Production-style capstoneCareer support included
Your instructor
Taught by engineers who shipped agentic AI to production.
MK
Manikanta Kona
Founder, Digital Edify · 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 class."
15 yrs
AI & DATA SCIENCE
2,400+
LEARNERS
4.8 /5
RATING
Manikanta is the founder of Digital Edify 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 Edify · 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 Edify, 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 Edify grads.
Real feedback from analytics and BI leaders at AI-first companies and the firms hiring our Data Analyst & AI graduates.
Digital Edify 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 Edify 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 Edify 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 Edify'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 Edify 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 Edify 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 Edify graduates for our analytics practice. Strong on SQL, sharp on DAX, fluent in LLM copilots.
Geetha Pillai, Talent Acquisition Lead, Cyient
Digital Edify 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 Edify · 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 Edify
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.
Job roles
Roles this program prepares you for.
Data Analyst
Answer business questions with SQL, BI and statistics.
Business Intelligence Developer
Build and govern Power BI and Tableau dashboards and semantic models.
Analytics Engineer
Own dbt models, metrics and data quality on the warehouse.
Product Analyst
Funnels, cohorts, retention and experimentation for product teams.
Marketing / Growth Analyst
Attribution, campaign measurement and customer analytics.
Financial / Operations Analyst
Revenue, margin, forecasting and operational KPIs.
AI-Enabled Analyst / Analytics Copilot Specialist
Direct AI copilots and build agentic self-serve analytics.
Reporting & MIS Analyst (upgraded)
Move from spreadsheets to governed BI and automation.
Data Governance Analyst
Definitions, quality, lineage and access.
Senior / Lead Analyst (career path)
Grow toward analytics leadership and decision science.
What employers should see in your portfolio: that you can take a business question to a trusted answer — query and model the data in SQL and dbt, analyse it with statistics and Python, build dashboards people use in Power BI or Tableau, deliver an agentic self-serve experience with guardrails, and present the recommendation.
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 classes 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 8142998866
US desk
+1 256 388 7766
Hours
Mon–Sun · 7 AM–9 PM
Online class
Global
Weekend and evening Data Analyst classes running on IST and PST. Every online class 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
Admissions
ENROLLING NOW
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. The opening modules cover the SQL fundamentals, data modeling, and dashboard craft from scratch. What you do need is consistency and regular practice.
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.
How is the learning workload structured?
The program combines live mentor-led classes, guided labs, project work, and optional support sessions. An advisor can explain the current class format before enrolment.
Is placement support really 1:1, and which companies hire data analysts?
Yes. Career support includes portfolio and profile preparation, interview practice, and role-fit introductions where available. Digital Edify does not guarantee an interview, offer, salary, employer, location, or timeline.
Online, weekend, or on-campus?
All three. On-campus at the Hyderabad flagship, live online (IST and PST classes), 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 learners needing additional support, 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.
One million AI‑native professionals by 2027.
Let's put you in that number.
Book a 20‑minute advisor call. We'll map your current role to the right program, talk honestly about timelines, and walk you through a real class's project.
