📊 3 Month Job-Ready Data Analyst Program

Become a Data Analyst.
Structured. Live. Job-Ready in 90 Days.

Python · SQL · DA Fundamentals · Data Modeling · Power BI · Azure Synapse Project · AI Use Cases · Interview Prep — everything a hiring manager checks before shortlisting you, taught live with real datasets.

90Days
180+hrs Live
7Tracks
₹5999Full Course
Login to Enroll See Full Curriculum ↓
Who Is This For?

This program was built for one specific person

Not for everyone — for the professional who is serious about building a data analyst career with real skills and a real portfolio.

🎓
Fresher · 0–2 Years

Starting Fresh in Data

You're entering the data world — fresh grad or early career. You know Excel or basics of SQL but need structured real-world training to land your first analyst role.

  • ✓ No prior analytics job experience needed
  • ✓ Basic Excel / SQL is enough to start
  • ✓ Targeting first DA job at 5–12 LPA
  • ✓ Need a real project + dashboard on your resume
See full 90-day curriculum →
🔄
Career Switcher · 2–6 Years

Switching Into Data Analytics

You have professional experience in another domain — support, ops, finance, testing — and want to pivot into data with a structured portfolio that replaces your previous title.

  • ✓ Finance / Ops / Support → DA switch
  • ✓ Comfortable with Excel, want to level up to SQL + Power BI
  • ✓ Want a cloud project to prove technical credibility
  • ✓ Targeting 10–20 LPA roles post-switch
See full 90-day curriculum →
📈
Working Analyst · 1–5 Years in Data

Already in Data, Ready to Level Up

You're working as a junior analyst, BI developer, or reporting analyst — but stuck at the same level and need cloud + Python + Azure to break the ceiling.

  • ✓ Working as Analyst / BI / Reporting Developer
  • ✓ Know SQL, want to add Python + Power BI + Azure
  • ✓ No hands-on cloud project experience yet
  • ✓ Targeting 15–28 LPA senior analyst roles
See full 90-day curriculum →

Questions you're probably asking right now

I have no Python experience — is that okay? ＋
Day 1 starts from installation and variables — literally zero assumed. By Day 10 you'll be writing functions, handling files, and querying databases with Python. The course is sequenced so each day builds on the previous one. If you can use Excel, you're ready.
I'm from Finance / Operations — is this relevant to me? ＋
Finance and ops professionals already think in numbers, KPIs, and business problems — that's half the job. What you're adding is the technical layer: SQL for querying, Python for automation, Power BI for dashboards, and Azure for cloud projects. Most Finance-to-DA switchers find the business fundamentals module especially fast to complete.
I already know SQL — will I be bored in the SQL track? ＋
The SQL track goes from basics to advanced window functions, CTE patterns, query optimisation, and indexing over 25 days. If you know SELECT and basic JOINs, you'll fly through the first week and get deeply challenged by window functions, multi-CTE patterns, and script optimisation in weeks 2–4. Most "I know SQL" candidates discover gaps by Day 12.
Is the Azure project on a real cloud account? ＋
Yes. You set up your own free-tier Azure account and build the full pipeline — Azure Synapse → transformation → Power BI report — in your own subscription. The GitHub repo and live Power BI link go on your resume and LinkedIn. Interviewers can click through and verify your work.
How much time per day do I need to commit? ＋
Live sessions run weekday evenings (9–11 PM IST) and weekends (4–7 PM IST) so you keep your current job throughout. Plan 1–1.5 hours of practice after each session. Total: roughly 15–18 hours per week. Recordings are always available so you never fall behind if life gets busy.
How is this different from the DE roadmap? ＋
Different tools, different roles, different salary bands. The DE roadmap targets engineers who build data pipelines (Spark, Databricks, Airflow, dbt). This program targets analysts who generate and present business insights — SQL depth, Power BI reporting, Azure Synapse project, and AI use cases for interviews. Analysts typically target 8–25 LPA; DEs target 15–40 LPA. Both are valuable career paths — it depends on what kind of work you want to do daily.
Track 1 · Days 1–14

Python for Data Analysts

Not a generic Python course — 14 focused days covering exactly what analysts use: core language, data types, file handling, database connectivity, control flow, and Pandas fundamentals.

🐍 14 Days📅 28 hrs Live🎯 Beginner → Intermediate
🐍 Python Core Days 1–14 📅 View Day-by-Day Plan ▼
Day-by-Day Python Foundations

Setup to Pandas fundamentals — everything in sequence, nothing skipped, 4 extra days for practice + NumPy + Pandas intro.

D1
Installation, Environment & First ScriptPython install, VS Code + Jupyter setup, pip & virtualenv, variables, operators (arithmetic, comparison, logical), input/output — write your first working script
D2
Data Typesint, float, str, bool, type casting, type() function — understand how Python stores and interprets different kinds of data before you touch any dataset
D3
List Data Type — Methods, Slicing & IterationCreate, index, slice, append, remove, sort, reverse, nested lists, list comprehensions preview — lists are the backbone of data processing in Python
D4
String, Tuple & Set Data TypesString methods (split, strip, replace, upper/lower, format), string slicing, immutable tuples, set for deduplication — the types you'll use every time you clean data
D5
Dictionary Data TypeKey-value pairs, CRUD operations, nested dicts, dict comprehensions, iterating with .items()/.keys()/.values() — dicts map directly to JSON and database records
D6
File Handlingopen(), read/write/append modes, with context manager, CSV read/write without libraries, JSON load/dump, handling file-not-found errors — read raw data files before Pandas exists
D7
Database Connection Using Pythonsqlite3 built-in, connect → cursor → execute → fetchall workflow, insert/select/update via Python, parameterised queries to prevent injection — the pattern that underpins every data pipeline
D8
Control Flow — if/elif/else, for loop, while loopConditional logic, loop over lists and ranges, nested loops, while with break condition — the logic layer that makes your scripts intelligent
D9
List Comprehension, break / continue / passOne-line list transforms, filter with conditions inside comprehensions, break to exit early, continue to skip, pass as placeholder — write 5-line loops in one readable line
D10
Functions, Lambda & Map/Filterdef with args/kwargs, return values, lambda for one-liners, map/filter, reusable transformation functions — DRY code for data tasks
D11
NumPy BasicsArrays vs lists (performance), vectorised operations, reshape, slicing, statistical functions (mean/std/percentile) — the numerical foundation that Pandas is built on
D12
Pandas Introduction — DataFrames & Seriesread_csv / read_excel, dtypes, head/describe/info, loc/iloc, boolean filtering, basic column operations — the entry point for every data analyst's daily work
D13
Pandas — Cleaning, GroupBy & Mergefillna/dropna, drop_duplicates, astype, groupby + agg, pd.merge (inner/left/outer) — the Pandas operations every analyst runs on every dataset
D14
Mini End-to-End ProjectRead raw CSV → clean with Pandas → compute GroupBy summary → write output + a 5-insight summary. Your first complete Python data pipeline — goes straight to GitHub.
🎯 Milestone: A complete Python + Pandas script that reads raw data, cleans it, computes aggregated insights, and writes a report — committed to GitHub on Day 14
Track 2 · Days 15–39

SQL — From Basics to Query Optimisation

SQL is tested in every single analytics interview. 25 structured days — SELECT to window function patterns (10+ scenarios) to indexing and script optimisation — the full depth hiring managers test.

🗄️ 25 Days📅 50 hrs Live🎯 Beginner → Advanced
🗄️ SQL Basics Days 15–17 📅 View Day-by-Day Plan ▼
SELECT Statements — Reading & Filtering Data

Every SQL journey starts here. Write queries that retrieve exactly what you need from any table.

D15
Basic SELECT StatementsSELECT, FROM, column aliases, SELECT DISTINCT, LIMIT/TOP, ORDER BY ASC/DESC — read any table, control what you see and how it's sorted
D16
Filters — WHERE ClauseComparison operators, AND/OR/NOT, IN, BETWEEN, LIKE with wildcards, IS NULL / IS NOT NULL, CASE WHEN — filter any dataset down to exactly the rows you need
D17
String, Date & Numeric FunctionsUPPER/LOWER/TRIM/CONCAT/SUBSTRING, DATEPART/DATE_TRUNC/DATE_DIFF/FORMAT, ROUND/FLOOR/CEIL/ABS — the built-in functions every analyst uses weekly
🎯 Milestone: Write any filter + sort query from a plain-English business requirement in under 2 minutes
🔗 JOINs Days 18–20 📅 View Day-by-Day Plan ▼
Joins — Combining Tables Correctly

The most-tested SQL concept in interviews. Know every join type, when to use each, and what the output looks like.

D18
INNER, LEFT, RIGHT, FULL OUTER JOINVisual Venn diagram approach, multi-table joins, self join for hierarchies (manager → employee), choosing the right join type for business questions
D19
Subqueries, EXISTS & CROSS JOINInline subqueries in WHERE/FROM/SELECT, correlated subqueries, EXISTS vs IN performance, CROSS JOIN for cartesian products — the advanced join patterns interviewers probe
D20
Join Practice — Real Business Scenarios10 business SQL problems using only joins: customers with no orders, order summary with product names, employee manager hierarchy, multi-table dashboard query — timed practice, debrief, common mistakes
🎯 Milestone: Given any two tables and a business question, write the correct join without needing to think about which type to use
📊 Aggregations Days 21–22 📅 View Day-by-Day Plan ▼
GROUP BY, HAVING & Aggregate Functions

Turn rows of data into business summaries — the queries that power every dashboard and report.

D21
GROUP BY & Aggregate FunctionsCOUNT, SUM, AVG, MIN, MAX with GROUP BY, grouping on multiple columns, NULL handling in aggregations, order of SQL execution — internalize WHY GROUP BY works the way it does
D22
HAVING, ROLLUP & GROUPING SETSHAVING vs WHERE (post vs pre-aggregation), ROLLUP for subtotals and grand totals, GROUPING SETS for multi-level summaries — the reporting SQL that powers executive dashboards
🎯 Milestone: Write a revenue-by-region-by-month report with subtotals from a single GROUP BY ROLLUP query
🪟 Window Fn I Days 23–24 📅 View Day-by-Day Plan ▼
Window Functions Part 1 — Ranking & Row Numbering

The single most-asked advanced SQL topic. Ranking functions written cold under interview conditions.

D23
OVER(), PARTITION BY, ORDER BY in Window ContextHow window functions differ from GROUP BY, OVER() syntax, PARTITION BY to reset per group, ORDER BY inside OVER — build the mental model before using any specific function
D24
ROW_NUMBER, RANK, DENSE_RANK, NTILEWhen RANK skips vs DENSE_RANK doesn't, top-N per category, deduplicate keeping latest row, split data into quartiles with NTILE — the 4 ranking functions interviewers test
🎯 Milestone: Solve top-3-products-per-category and deduplicate-keeping-latest without reference — two of the most common interview problems
🪟 Window Fn II Days 25–26 📅 View Day-by-Day Plan ▼
Window Functions Part 2 — Offset & Aggregate

LAG/LEAD for time-series comparisons, running totals and moving averages — the patterns that appear in every analytics dashboard query.

D25
LAG, LEAD & FIRST_VALUE / LAST_VALUEMonth-over-month growth with LAG, detect consecutive drops, compare current row to first/last in partition — the offset functions for any time series analysis
D26
Running Totals, Moving Averages & Frame ClausesSUM() OVER with ROWS BETWEEN, running revenue, 3-month rolling average, cumulative count — ROWS vs RANGE frame clause, the performance gotcha everyone gets wrong
🎯 Milestone: Write MoM growth, running total, and 3-month rolling average queries — all three in one session, no reference
🪟 Window Patterns Days 27–30 📅 View Day-by-Day Plan ▼
Window Function Patterns — 10+ Real-World Scenarios

Move from knowing the syntax to solving business problems. These patterns appear in actual take-home tests and live coding rounds.

D27
Patterns 1–4: Ranking, Dedup, Percentile, GapsTop-N per group, keep-latest dedup, PERCENT_RANK for percentile bands, detect gaps in sequences — the 4 most-asked patterns in SQL screening rounds
D28
Patterns 5–8: Cohort, Retention, Sessionisation, IslandsFirst-purchase cohort, 30-day retention flag, session grouping with LAG, consecutive-days-island problem — product analytics patterns every analyst at a tech company writes
D29
Patterns 9–12: Funnel, Pivot, % of Total, YoYFunnel drop-off with conditional aggregation, PIVOT without PIVOT keyword, % of total using SUM() OVER(), year-over-year using LAG with PARTITION BY year — 3-hour timed practice session
D30
Window Function Mock Test6 unseen business problems — ranking, running total, MoM, cohort, funnel, and one open-ended — under time pressure. Every answer reviewed live with explanations for common mistakes.
🎯 Milestone: Clear a full window function mock test — 6 problems, 90 minutes, no reference
📝 CTEs Days 31–33 📅 View Day-by-Day Plan ▼
CTEs, Subqueries & Query Readability

Write SQL that a colleague can read and maintain — not 8-level nested subqueries. CTEs are how production queries are structured.

D31
WITH Clause — Single & Multi-Step CTEsCTE vs subquery readability, chained CTEs for multi-step logic, CTE that references another CTE, when CTEs improve vs hurt performance
D32
Recursive CTEs & HierarchiesRecursive CTE syntax (anchor + recursive member), org chart traversal, category path building, BOM explosion — the one CTE type that has no non-recursive equivalent
D33
CTE Practice — Refactoring & Complex Business QueriesTake 5 deeply nested subquery messes, refactor each into clean CTE chains. Then build a 4-step CTE: filter → enrich → aggregate → rank — the pattern behind every complex analytics report.
🎯 Milestone: Refactor a 4-level nested subquery into clean CTEs and write a 4-step analytical CTE from scratch — readable, debuggable, correct
⚡ Optimisation Days 34–39 📅 View Day-by-Day Plan ▼
SQL Script Optimisation & Indexing

A query that's correct but takes 4 minutes is a query that gets replaced. Learn how to make queries fast — the skill that separates junior from senior analysts.

D34
Query Execution Plan — EXPLAIN & ANALYZERead an execution plan, identify full table scans, understand cost estimates, spot the expensive node — EXPLAIN ANALYZE in PostgreSQL / execution plan in SQL Server / BigQuery job stats
D35
SQL Script Optimisation TechniquesAvoid SELECT *, push filters early, avoid functions on indexed columns in WHERE, use EXISTS over IN for large sets, materialise CTEs vs inline — 8 rules, applied to slow queries live
D36
Indexing — B-Tree, Composite & Covering IndexesHow B-tree indexes work, when queries use vs ignore an index, composite index column order, covering index to avoid table lookups, index on foreign keys — the decisions that make a 10M-row query fast
D37
Stored Procedures, Views & Temporary TablesCREATE VIEW for reusable queries, temp tables vs CTEs (performance trade-offs), stored procedures for repeatable logic, parameterised stored procedures — the SQL objects production teams actually use
D38
Transactions, Error Handling & Data ModificationINSERT, UPDATE, DELETE safely, BEGIN TRANSACTION / COMMIT / ROLLBACK, TRY-CATCH for error handling, MERGE (upsert) — write SQL that doesn't corrupt production data
D39
SQL Mock Test — Full Optimisation ChallengeGiven 5 slow queries and a schema: identify bottleneck, rewrite using CTEs + correct joins, add indexes, verify plan improved — exactly how a technical interview "SQL round" is structured at product companies
🎯 Milestone: Take a slow multi-join query, read its execution plan, rewrite it, and add the right index — explain every decision out loud
Track 3 · Days 40–52

DA Fundamentals — Mathematics, Modeling & Business Thinking

The conceptual layer most analysts skip. Statistics, graph types, ETL, data modeling, database architecture, and business requirements — the knowledge that moves you from "data puller" to decision-maker.

📊 13 Days📅 26 hrs Live🎯 Conceptual + Applied
📐 Maths & Stats Days 40–43 📅 View Day-by-Day Plan ▼
Mathematics & Statistics for Data Analysts

Applied — every concept is tied to a real dataset or business question. Not academic theory.

D40
Descriptive Statistics — Central Tendency & SpreadMean, median, mode (when each misleads), variance, standard deviation, IQR, skewness, kurtosis — applied in Python on a real dataset, not a textbook
D41
Graph Representations & When to Use EachBar (comparison), line (trend), scatter (correlation), histogram (distribution), box plot (spread + outliers), heatmap (correlation matrix), pie (only when appropriate) — choose the right chart, not just any chart
D42
Probability, Distributions & Hypothesis TestingNormal distribution and why it matters, p-value interpretation without the jargon, t-test and chi-square in plain English, A/B test design — answer "was this experiment significant?" correctly
D43
Correlation, Regression & Statistical PracticePearson/Spearman correlation, simple linear regression with scikit-learn, R-squared interpretation, predict sales from ad spend — plus a 2-hour timed statistical problem set covering all 4 days
🎯 Milestone: Design an A/B test, determine sample size, run it in Python, and interpret the result in one clear business sentence
🔄 ETL & Pipelines Days 44–45 📅 View Day-by-Day Plan ▼
ETL, ELT, Pipelines & Data Quality

Understand how data moves from source to your dashboard — and why it's wrong half the time.

D44
ETL vs ELT, Batch vs Streaming, Pipeline AnatomyExtract-Transform-Load vs Extract-Load-Transform (cloud-native), batch scheduled pipelines vs real-time streams, source → staging → warehouse → reporting — draw and explain any pipeline from scratch
D45
Data Quality — Completeness, Consistency, AccuracyThe 6 dimensions of data quality, how to audit a dataset (null %, duplicate %, referential integrity), write a DQ SQL report, what to do when data is wrong — the skill that stops you publishing incorrect dashboards
🎯 Milestone: Write a full SQL data quality audit report for a given schema — nulls, dupes, orphan keys, format violations — one query per check
🏗️ Data Modeling Days 46–48 📅 View Day-by-Day Plan ▼
Star Schema, Snowflake Schema & Data Modeling

How data warehouses are designed — the schema knowledge that makes your Power BI models and SQL queries correct and performant.

D46
Dimensional Modeling — Facts, Dimensions, Star SchemaFact table (metrics + FK), dimension table (descriptive attributes), star schema design, grain definition, additive vs semi-additive vs non-additive facts — design a sales warehouse schema from scratch
D47
Snowflake Schema, SCD & Data Vault BasicsSnowflake schema (normalised dimensions), when to use star vs snowflake, Slowly Changing Dimensions Type 1/2/3, what Data Vault solves — the modeling vocabulary every analytics engineer uses
D48
Data Modeling Hands-On PracticeDesign 3 schemas from scratch: e-commerce, healthcare, SaaS subscription — choose fact grain, identify dimensions, handle many-to-many relationships. Reviewed and critiqued live.
🎯 Milestone: Design a star schema for an e-commerce business — identify fact and dimension tables, define grain, draw the model
🏢 Architecture & Business Days 49–52 📅 View Day-by-Day Plan ▼
Database Architecture, Statistics & Business Fundamentals

The conceptual knowledge interviewers test when they want to know if you understand the bigger picture — beyond writing queries.

D49
Database Architecture UnderstandingOLTP vs OLAP, row-store vs column-store, data warehouse vs data lake vs data lakehouse, partitioning and clustering, why BigQuery/Snowflake/Redshift exist — explain any architecture on a whiteboard
D50
Statistics in Practice — Grouping, Sums, Aggregation, SegregationWhen to sum vs average vs median, weighted averages, segmentation by percentile, cohort grouping, sub-totals vs grand totals — the statistical thinking behind every business metric
D51
Business Fundamentals & Requirements GatheringRevenue, margin, churn, CAC, LTV — the 10 metrics every DA must know cold. Requirements gathering: translate "sales are down" into a specific analysis. Write a BRD one-pager from a stakeholder conversation.
D52
DA Fundamentals Capstone — Analysis Plan & Presentation"Our conversion rate dropped last month" — produce a complete analysis plan: hypothesis, SQL queries, metrics to check, chart types, and a 5-slide presentation. Peer review and debrief session.
🎯 Milestone: Given "our conversion rate dropped last month — find out why", produce a complete structured analysis plan — SQL, metrics, charts, and stakeholder presentation
Track 4 · Days 53–60

Power BI — Data Modelling, DAX & Dashboard Design

The most-requested BI tool in Indian analytics JDs. 8 focused days: connect → model → measure → visualise → publish. Build a portfolio-ready dashboard by Day 60.

📈 8 Days📅 16 hrs Live🎯 Beginner → Job-Ready
📈 Power BI Days 53–60 📅 View Day-by-Day Plan ▼
Power BI — End-to-End in 8 Days

From raw data to a published interactive dashboard — every step, live, using a real business dataset.

D53
Connect, Transform & Load — Power QueryConnect to CSV/Excel/SQL, Power Query transformations (split, merge, unpivot, type fixes), append & merge queries, loading to the model — clean messy data before it reaches your visual
D54
Data Model — Relationships & Star Schema in PBIFact + dimension tables in Power BI, relationship cardinality, active vs inactive relationships, cross-filter direction — build the model correctly so DAX works correctly
D55
DAX — Measures, CALCULATE & Time IntelligenceMeasures vs calculated columns (performance), CALCULATE + FILTER, ALL/ALLEXCEPT, TOTALYTD, DATEADD for MoM/YoY, SELECTEDVALUE for dynamic titles — the DAX every analyst must know
D56
Visuals — Charts, KPI Cards, Slicers & DrillthroughBar, line, matrix, KPI card, conditional formatting, sync slicers, bookmarks, drill-through pages, tooltips — visuals that answer questions, not just display numbers
D57
Publish to Power BI Service + Workspace SetupPublish to workspace, schedule dataset refresh, configure gateway, share report link, row-level security basics — production deployment workflow from desktop to cloud
D58
Advanced DAX — RANKX, SWITCH, Dynamic TitlesRANKX for ranking in visuals, SWITCH for multi-condition logic, SELECTEDVALUE for dynamic measure names, CALCULATE with multiple filters — the DAX that separates intermediate from advanced PBI developers
D59
Power BI Performance TuningReduce model size (remove unused columns, aggregate tables), avoid bi-directional relationships, use Import vs DirectQuery correctly, Performance Analyzer to find slow visuals — make large models fast
D60
Portfolio Dashboard — End-to-End ProjectRaw data → Power Query clean → star schema model → DAX measures (YTD, MoM, % of total, rank) → 3-page interactive dashboard → published to Power BI Service. Portfolio-ready .pbix + shareable link.
🎯 Milestone: A published Power BI dashboard with advanced DAX, time intelligence, and drill-through — live link and .pbix file ready for your resume
Track 5 · Days 61–80

Azure Synapse → Power BI — End-to-End Analytics Project

The project that makes your resume stand out. Build a complete cloud analytics pipeline: raw data in Azure Data Lake → transformed in Synapse Analytics → reported in Power BI. Real cloud, real architecture, real portfolio.

☁️ 20 Days📅 40 hrs Live🎯 Cloud + Project Portfolio
☁️ Azure Setup Days 61–63 📅 View Day-by-Day Plan ▼
Azure Fundamentals for Data Analysts

Get hands-on with Azure services that appear in DA job descriptions — no prior cloud experience assumed.

D61
Azure Portal, Subscriptions & Resource GroupsFree-tier Azure account setup, resource groups, storage accounts, Azure Data Lake Gen2 — create your project environment, upload raw CSV data, understand how cloud storage is organised
D62
Azure Data Factory — Pipeline BasicsADF concepts (pipelines, activities, datasets, linked services), copy data from HTTP/blob to ADLS, parameterised pipelines, trigger on schedule — the ingestion layer every cloud DA works with
D63
Azure Synapse Analytics — Workspace & SQL PoolsSynapse workspace setup, serverless SQL pool vs dedicated pool, query files in ADLS using OPENROWSET, external tables, PolyBase basics — run SQL directly on cloud storage without moving data
🎯 Milestone: Raw CSV in Azure Data Lake → queryable via Synapse Serverless SQL — end-to-end in your own Azure subscription
🔧 Transform Days 64–70 📅 View Day-by-Day Plan ▼
Data Transformation & Warehouse Layer in Synapse

Clean, model, and store data in a structured warehouse layer — the step between raw files and a polished Power BI report.

D64
Bronze → Silver → Gold ArchitectureMedallion architecture pattern: raw (bronze), cleaned (silver), aggregated (gold), why this structure makes debugging and governance easier — apply it to the project dataset
D65
SQL Transformations in Synapse — Views & CETASCREATE EXTERNAL TABLE AS SELECT, views over external tables, incremental load patterns, partitioning output by date — write the transformation SQL that builds your silver and gold layers
D66
Synapse Spark Pool — Python/PySpark BasicsSpark notebook in Synapse, read Parquet from ADLS, basic DataFrame transformations (filter, groupBy, withColumn), write back to gold layer — understand when Spark is the right tool vs SQL
D67
Dedicated SQL Pool — Fact & Dimension TablesCreate fact and dimension tables in Synapse dedicated pool, distribution strategies (hash vs round-robin), COPY INTO for bulk load, table statistics — production-quality warehouse design
D68
Pipeline Orchestration — ADF End-to-EndWire together: ADF ingestion → Synapse SQL transformation → gold layer output, dependency between activities, error handling, email notification on failure — your first automated data pipeline
D69
Data Testing & Pipeline MonitoringWrite SQL validation checks post-load (row counts, null checks, range checks), ADF monitoring dashboard, set up email alerts on pipeline failure — production-grade pipeline reliability
D70
Pipeline Troubleshooting PracticeIntentionally broken pipeline exercises: wrong schema, missing file, transformation error, failed SQL — diagnose and fix each one. Learn to read ADF error logs and Synapse query diagnostics.
🎯 Milestone: A running, monitored ADF pipeline — ingests raw data, transforms through bronze/silver/gold, loads star schema in Synapse, and alerts on failure
📊 PBI on Cloud Days 71–74 📅 View Day-by-Day Plan ▼
Power BI — Live Connection to Azure Synapse

Connect Power BI directly to your Synapse warehouse — no data import, always-fresh, production architecture.

D71
DirectQuery to Synapse + Model OptimisationConnect Power BI Desktop to Synapse via DirectQuery, manage query folding, import vs DirectQuery trade-offs, aggregation tables for performance — architect the reporting layer correctly
D72
Advanced DAX for the Project DatasetWrite DAX measures specific to the project KPIs: YoY growth, rolling 3-month average, % of total, customer retention rate — real measures on real cloud data
D73
Executive Dashboard — 4-Page Report DesignPage 1: KPI summary. Page 2: trend analysis. Page 3: segment breakdown. Page 4: drill-through detail. Designed to answer the 5 questions a business stakeholder actually asks.
D74
Publish, Schedule & Share — Power BI ServicePublish to Power BI Service, configure gateway for scheduled refresh from Synapse, workspace permissions, embed link for portfolio — your dashboard is live and shareable
🎯 Milestone: A live Power BI dashboard connected to your Azure Synapse warehouse — shareable URL, scheduled refresh, ready to demo in any interview
🏁 Project Wrap Days 75–80 📅 View Day-by-Day Plan ▼
Project Documentation, GitHub & Architecture Walkthrough

A project that can't be explained is a project that doesn't get you hired. Document it, publish it, and practice walking through it.

D75
Architecture Diagram & READMEDraw the end-to-end architecture (source → ADF → ADLS → Synapse → Power BI), write a clear README with problem statement, architecture, key insights, and tech stack — the document interviewers read before the interview
D76
GitHub Portfolio — Push & StructureGit init, commit SQL scripts and notebooks, push to GitHub, structure the repo cleanly (data/, sql/, notebooks/, reports/, README.md), tag the release — your project as a professional open-source repository
D77
Project Walkthrough Practice — 2-min and 10-min versions"Walk me through your project" — practiced in 2-min (recruiter screen) and 10-min (technical interview) formats. Anticipate follow-up questions: "Why Synapse over Databricks?", "How would you handle late-arriving data?"
D78
Project Extended — Add Second DatasetIntegrate a second data source (e.g. HR data or web analytics) into the existing pipeline — new ADF source, new silver layer table, new Power BI page. Practice extending a live project as you would in a real job.
D79
Cost Optimisation & Security BasicsAzure cost monitoring (Cost Management), pause dedicated SQL pool when not in use, ADLS access tiers (hot/cool), service principal for ADF authentication, Managed Identity — the cloud cost and security awareness every DA needs
D80
Project Final Review & Portfolio LockFinal walkthrough of all deliverables: GitHub repo, PBI live link, architecture diagram, README — polish anything rough, verify all links work. Project is locked and ready to go on your resume tomorrow.
🎯 Milestone: A polished GitHub repo with architecture diagram, SQL scripts, PBI live link — locked and on your resume
Track 6 · Days 81–85

AI Use Cases for Data Analysts — Interview-Ready

Every interviewer now asks "how have you used AI in your work?" Be ready with real answers — not buzzwords. 5 days of practical AI application on your project data.

🤖 5 Days📅 10 hrs Live🎯 Practical + Interview-Ready
🤖 AI for DA Days 81–85 📅 View Day-by-Day Plan ▼
AI Use Cases — Applied on Your Project

Each day applies an AI technique to real data — so when an interviewer asks "show me a project where you used AI", you have a concrete answer.

D81
AI-Assisted SQL & Data ExplorationUse GitHub Copilot / ChatGPT to generate complex SQL, validate AI-generated queries (spot the errors), prompt engineering for data tasks, auto-generate column descriptions — be the analyst who uses AI to move 3× faster
D82
Anomaly Detection with PythonIQR-based outlier detection, Z-score method, Isolation Forest (scikit-learn), flag anomalies in your project dataset, visualise them — "our revenue spiked on Tuesday — was it real or dirty data?" answered automatically
D83
Forecasting — Time Series PredictionProphet (Facebook) for business forecasting: install, fit on monthly sales data, generate 3-month forecast, plot with confidence intervals — add a "next quarter forecast" page to your Power BI report
D84
Customer Segmentation with ClusteringK-means clustering on customer behaviour data (RFM — Recency, Frequency, Monetary), choose K with elbow method, label clusters meaningfully, add segment column to your warehouse — "who are our high-value customers?" answered with ML
D85
NLP for Text Data & AI Interview AnswersSentiment analysis on customer feedback (pre-trained model, 5 lines of code), keyword extraction, summarise survey responses — PLUS: how to articulate these AI projects in an interview answer format: "I used X to solve Y, the result was Z"
🎯 Milestone: 3 AI techniques applied to your project dataset — anomaly detection, forecasting, segmentation — each described in one clear interview answer
Track 7 · Days 86–90

Interview Preparation — 5-Day Final Sprint

Technical skills got you this far. Now convert them into offers. 5 days of mock interviews, resume polish, and the soft skills that separate candidates who get hired from those who don't.

🎤 5 Days📅 10 hrs Live🎯 Job Offer Ready
🎤 Interview Prep Days 86–90 📅 View Day-by-Day Plan ▼
5-Day Interview Sprint — Days 86–90

Resume, SQL mock, case study, portfolio walkthrough, and salary negotiation — every step of the real interview process, rehearsed. Walk out on Day 90 ready to apply.

D86
Resume & LinkedIn for Data AnalystsBullet your SQL, Python, Power BI, and Azure project with quantified impact, write a headline that passes ATS, LinkedIn optimisation (keywords, featured section, project links), live resume review and edit
D87
SQL Mock Interview — Live Round3 business SQL problems under time pressure: one aggregation + GROUP BY, one window function, one multi-CTE — evaluated cold, debriefed in real time, common mistakes flagged
D88
Case Study Round — Business Problem Solving"Retention dropped 12% last quarter — what would you do?" Framework: clarify → hypothesise → define metrics → write queries → present insight. Practice 3 case studies in structured STAR format.
D89
Project & Portfolio Walkthrough"Walk me through your Azure Synapse project" — practiced in 3 formats: 2-min recruiter screen, 10-min technical deep-dive, answer-to-follow-up. The questions interviewers always ask about your project, answered smoothly.
D90
Job Strategy, Offer Evaluation & Salary NegotiationWhere to apply (product vs service vs startup), how to use referrals, how to evaluate an offer (not just CTC), salary negotiation script that works, counter-offer handling — the business side of your job search
🎯 Final Milestone: Walk out on Day 90 with a polished resume, a live Azure Synapse project on GitHub, a published Power BI dashboard, and a mock interview you didn't fail — ready to apply from Day 91
Choose Your Path

DA Only — or DA + DE?

Python and SQL you learn in the DA course are the same skills DE interviews test. Add the full DE project track — Snowflake, dbt, Airflow, Databricks, Advanced Masterclass — and you can appear for both roles from one investment.

What's Included 📊 DA Only 🚀 DA + DE Combo
⭐ Best Value
🐍 Python — Core to Pandas (14 Days) ✓ ✓
🗄️ SQL — Basics to Advanced (25 Days) ✓ ✓
📊 DA Fundamentals + Data Modeling (13 Days) ✓ ✓
📈 Power BI — DAX, Reports & Dashboards (8 Days) ✓ ✓
☁️ Azure Synapse → Power BI End-to-End Project (20 Days) ✓ ✓
🤖 AI Use Cases for DA Interviews (5 Days) ✓ ✓
🎤 DA Interview Prep & Mock Rounds (5 Days) ✓ ✓
📹 Lifetime Recording Access ✓ ✓
📄 Resume Review + LinkedIn Optimisation ✓ ✓
💼 Live Zoom Sessions (Weekday evenings + Weekends) ✓ ✓
⚡ PySpark Fundamentals (10 Days) — ✓
🏛️ DE Fundamentals — Warehouse, Lakehouse & Modelling (5 Days) — ✓
🗄️ ADLS Gen2 — Storage Architecture & Ingestion (5 Days) — ✓
⚙️ Azure Data Factory — Pipelines & Orchestration (5 Days) — ✓
🔥 Databricks — PySpark, Delta Lake & Transforms (5 Days) — ✓
☁️ Azure Cloud DE Project — ADF · ADLS · Databricks · Delta Lake (18 Days) — ✓
❄️ Snowflake — Architecture, Internals & Cloud Data Warehouse (5 Days) — ✓
🔧 dbt — Models, Tests, Macros & SCD Snapshots (5 Days) — ✓
🌬️ Airflow — DAGs, Operators & Pipeline Orchestration (5 Days) — ✓
📊 Snowflake + dbt + Airflow End-to-End Project (10 Days) — ✓
🔥 Spark Performance Masterclass (7 Days) — ✓
🔷 Databricks Lakehouse — Unity Catalog, DLT, Medallion (8 Days) — ✓
🌊 Airflow at Scale — Dynamic Tasks, SLA, Backfill, CI/CD (7 Days) — ✓
🎤 DE Interview Prep — System Design, Spark Rounds, Pipeline Walkthroughs — ✓
Price ₹5999
₹8999
Save ₹3000 		₹9999
₹15998
Save ₹5999
💡 Python & SQL from DA are the exact skills DE interviewers test — add DE tracks and apply for both roles. Login to Enroll Login to Enroll

If you bought these separately

₹15,998+ DA course + Full DE roadmap

🎁 Enroll together today

₹9,999 Save ₹5,999

DA alone if you prefer

₹5,999 ₹8,999

Common Questions

Do I need prior experience in SQL or Python?
No. Day 1 Python starts from installation and variables. Day 11 SQL starts from basic SELECT. If you can write an Excel formula, you're ready to start.
Is the Azure project on a real cloud account?
Yes. You'll set up your own free-tier Azure account and build the pipeline in your subscription. The GitHub repo and Power BI dashboard link go on your resume — interviewers can verify them.
How much time per day do I need?
Live sessions run weekday evenings (9–11 PM IST) so you keep your current job. Plan 1–1.5 hours of practice after each session. About 15–18 hours per week — designed for working professionals.
Is this different from the DE roadmap?
Yes — different tools, different roles, different salary bands. The DE roadmap targets engineers who build pipelines (Spark, Databricks, Airflow). This program targets analysts who generate insights (SQL depth, Power BI, Azure project, AI use cases). See the DE Roadmap →