Skip to content
Launchpad
Enrol

T4· Engineering: tech

Data Analytics with AI

You will turn messy marketplace data into reconciled, trusted dashboards and weekly notes, using AI to go faster and checks to stay right.

Choose a planRead the syllabus
Who it is for
Students from any branch who like numbers and want to answer business questions with SQL, spreadsheets, BI tools and AI assistants. No prior SQL needed, but you should be comfortable with spreadsheets.
AI core
9 units · 20 h
This course
6 modules · 12 lessons · 2.3 h
Live projects
2, on Professional and Career
01Roles it prepares you for
  • Data Analyst
  • MIS Analyst
  • Junior BI Developer

The skills these roles ask for. Preparation, not a promise of a job.

02Tools you will use
  • DuckDBFree tier
  • Google SheetsFree tier
  • Power BI Desktop (Windows)Free tier
  • Google Data Studio (formerly Looker Studio)Free tier
  • Copilot in Power BI (needs paid Fabric F2+ or Premium P1+ capacity)College licenceVerified
  • Copilot in ExcelCollege licence
  • Claude free plan (chat and connectors)Free tierVerified
  • ChatGPT free planFree tier
  • Gemini app free planFree tier
  • n8n (Saarthi-hosted sandbox)Saarthi sandbox
  • Anonymised seller data pack v1 (Saarthi sandbox)Saarthi sandbox

You never pay for a tool subscription. “Verified” means we checked the free access on the vendor’s own site this month. Tool names describe what you learn; no vendor endorses Launchpad.

03Syllabus

What you will learn, in order.

Lesson films are narrated with an AI voice, over visuals made with AI tools and checked by our team. Saarthi, the coach, is an AI. Mentors and reviewers are people.

Part 1 · The AI core, shared by every stream

00Set up your coach1 hLearn how the core works, write the learning-context document that tells every AI tool who you are, and set Saarthi up as a coach that makes you think instead of thinking for you.
  1. How this core works6 min
  2. Write your learning-context document8 min
  3. Seven roles for AI, and study modes7 min
01How LLMs work and fail2.5 hBuild a working mental model of a large language model: tokens, prediction, context windows and memory. Then learn its predictable failures (hallucination, sycophancy, stale knowledge, bad arithmetic) and reproduce them yourself.
  1. Tokens and next-word prediction7 min
  2. Context windows and memory7 min
  3. Hallucination, sycophancy and other failures8 min
  4. Lab: one prompt, three models, five failures8 min
02Description and context engineering3.5 hWrite briefs that get the right output the first time: role, task, context, examples and format. Then make them reusable with Claude and ChatGPT Projects, Gemini Gems, custom GPTs and NotebookLM, and learn to work with PDFs, spreadsheets and images.
  1. The brief: role, task, context, format8 min
  2. Examples and output formats7 min
  3. Projects in Claude and ChatGPT8 min
  4. Gems, custom GPTs and NotebookLM8 min
  5. Working with files: PDFs, spreadsheets, images8 min
03Verify everything2.5 hCheck AI output the way fact-checkers do: lateral reading, tracing citations and numbers to the original, using deep-research modes without trusting them blindly, and testing a prompt properly with a 10-case golden set.
  1. Lateral reading7 min
  2. Checking citations and numbers8 min
  3. Deep-research modes: use and limits7 min
  4. Your first eval: a 10-case golden set8 min
04Connectors and MCP2 hConnect AI to the places your work lives (Drive, Gmail, Sheets, business data) without writing code. Learn what an MCP server is, why a reviewed directory differs from unreviewed community servers, how to grant the least access that works, and how to keep browser extensions on a short leash.
  1. What connectors and MCP are8 min
  2. Trust, least privilege and the reviewed directory8 min
  3. Browser extensions and AI browsers7 min
  4. Lab: ask the seller-data MCP server, check every answer8 min
05Agents and automations3.5 hHand multi-step work to AI safely. Tell an agent from an automation, decide what to delegate, use browser and coding agents with care, and build an n8n workflow that uses AI with a human-approval step before anything leaves the system.
  1. Agents vs automations: what to delegate8 min
  2. Browser agents, safely7 min
  3. Automation basics with n8n and Zapier8 min
  4. Lab: build an automation with a human-approval step8 min
  5. Failure modes and your failure-mode note7 min
06AI for learning and careers1.5 hUse AI to learn faster without outsourcing the learning, write applications that still sound like you, and practise interviews out loud with Saarthi.
  1. Learning with AI, not instead of learning7 min
  2. Applications in your own voice7 min
  3. Mock interview with Saarthi8 min
07Diligence: law, ethics, integrity2.5 hThe rules that apply when you use AI in India: the DPDP Act 2023 and its 2025 Rules, academic integrity and disclosure, copyright (including where Indian law is still unsettled), and bias. You finish by writing your own AI-use policy.
  1. DPDP Act 2023: personal data and AI8 min
  2. Academic integrity and disclosure7 min
  3. Copyright and AI in India8 min
  4. Bias and fairness7 min
  5. Write your personal AI-use policy7 min
08Core capstone1 hPut all four Ds together on one anonymised marketplace settlement file: ask good questions, automate one step, verify every number against Saarthi's P&L engine, and defend your work live in five minutes.
  1. The capstone brief6 min
  2. Lab: ask, automate, verify8 min
  3. Prepare your 5-minute defence7 min

Part 2 · Data Analytics with AI

01The marketplace money trail2 lessonsHow an order becomes a payout: fees, withheld taxes, refunds, reimbursements, sign conventions and date bases.
  1. From order value to net payout9 min
  2. Lab: explore the data pack with DuckDB and an AI assistant12 min
02SQL with a copilot2 lessonsGrain, fan-out, joins, NULLs and window functions, and directing an AI assistant to write SQL you can trust.
  1. Grain, joins and the fan-out trap9 min
  2. Lab: the SKU P&L in SQL15 min
03Spreadsheets and AI data analysis2 lessonsWhen to use spreadsheets, how chat assistants analyse files, and the habit of reproducing every key number.
  1. AI analysis you can stand behind8 min
  2. Lab: one question, three tools, one answer14 min
04Dashboards that get read2 lessonsOne question per page, metric definitions, the waterfall, Indian number formats and trust signals.
  1. Designing a seller dashboard9 min
  2. Lab: the one-page SKU P&L dashboard14 min
05Reconciliation and margin of error2 lessonsProving numbers against an independent source, classifying differences and stating uncertainty honestly.
  1. Reconcile, then report9 min
  2. Lab: reconcile to the Saarthi engine15 min
06Automated narratives2 lessonsWeekly notes that compute first and write second, with number checks and human approval.
  1. Numbers by SQL, words by AI9 min
  2. Lab: a weekly returns note in n8n15 min
04Live projects

Two briefs. Real-shaped work.

  1. Project 1

    SKU-level P&L dashboard reconciled to the rupee

    A seller wants to know which SKUs actually make money after every marketplace fee, refund and reimbursement. Build a SKU-by-month P&L from the anonymised settlement, orders and returns files, from gross sales through each fee type, refunds and withheld tax to net payout, and present it as a one-page dashboard. Your numbers are checked against Saarthi's P&L engine output for the same data-pack version and must reconcile to the rupee, with every difference along the way explained.

    You hand in

    • pnl.sql built from named CTEs, with fee_map.csv and passing check queries
    • A one-page dashboard in Power BI Desktop or Data Studio: headline cards, waterfall, sorted SKU table, filters, and a footer with basis, version and reconciliation status
    • A metric dictionary defining every number on the page
    • A reconciliation report against engine_outputs_v1.json at four levels, listing every difference and its cause
    • A log of AI assistance: which queries or measures were AI-drafted and how each was verified
    • A 5-minute walkthrough for a non-technical seller, plus questions

    Built on Saarthi’s engines. Your key numbers are checked against Saarthi's own P&L logic, run on the same anonymised data.

    Data: Anonymised seller data pack v1: settlement_v2.csv, orders.csv, returns.csv, sku_master.csv, with engine_outputs_v1.json (frozen Saarthi P&L engine output) for grading. Reviewed by Saarthi against a published rubric, signed off by a mentor.

  2. Project 2

    Refund and returns-leak analysis with an automated weekly narrative

    Returns quietly eat margins: refunds, return fees, fees not reversed, and items that come back damaged or never come back at all. Find where the seller is losing money to returns, by category, SKU and reason, and size each leak with a stated margin of error. Then build an automated weekly note that tells the seller what changed and what to do, computed in SQL, drafted by an LLM, checked in code and approved by a human before sending.

    You hand in

    • A returns-leak analysis in SQL: cost by category, SKU and reason, with fee-reversal gaps and unreturned items quantified
    • A margin-of-error method for in-transit returns and unposted reimbursements, applied to every headline figure
    • An n8n workflow (sandbox) with schedule, SQL, freshness check, LLM draft, number check, approval and test send
    • Four weekly notes generated from the data pack, with their JSON inputs and check results
    • A failure-test log: missing field, zero-returns week and planted wrong number
    • A two-page memo with the top three leaks and recommended actions, plus a live defence

    Data: Anonymised seller data pack v1: returns.csv, settlement_v2.csv, orders.csv, inventory_adjustments.csv, sku_master.csv. Reviewed by Saarthi against a published rubric, signed off by a mentor.

05Practise out loud

In voice mock interviews and client calls, Saarthi plays: Rohan, founder of a home-décor brand selling on Amazon and Flipkart, on a client call. He only knows his bank balance went down, distrusts dashboards after a bad experience, and keeps asking 'in rupees, where did my money go, and how sure are you?' He interrupts jargon and wants one thing to do this week.

On plans that include mock interviews. You get written feedback after every call.

06Enrol

Pick a plan for Data Analytics with AI.

One payment. The access deadline for each plan is shown before you pay.

  • Foundation

    01

    Learn the AI your field now runs on.

    ₹2,999

    One payment. GST included.

    • Access for 120 days (about 4 months) from the day you pay
    • No 1:1 mentor sessions
    • The common AI core and your domain course
    • Saarthi, your AI coach, any hour
    • Hands-on labs on free tools
    • Verifiable certificate
    Enrol on Foundation

  • Professional

    02

    Prove it on real work.

    ₹7,999

    One payment. GST included.

    • Access for 180 days (about 6 months) from the day you pay
    • One 1:1 session with a mentor
    • Everything in Foundation
    • Two live projects, reviewed by Saarthi and a mentor
    • Public portfolio page of verified work
    • Voice mock interviews
    • One 1:1 coaching session
    Enrol on Professional

  • Career

    03

    Work alongside our team.

    ₹14,999

    One payment. GST included.

    • Access for 240 days (about 8 months) from the day you pay
    • Four 1:1 sessions with a mentor
    • One 1:1 call with an industry expert
    • Everything in Professional
    • Four-week supervised practicum with the Saarthi team
    • Four 1:1 coaching sessions with your mentor
    • An exclusive 1:1 call with an industry expert in your field
    • CV and LinkedIn review
    • Priority mentor replies
    Enrol on Career

Your access period starts the day you pay and is shown before you pay. One exam pause per enrolment, for up to 15 days. When access ends the course locks, and your portfolio page and certificate stay live. Full refund within 7 days, pro-rata after that, and an automatic refund for any practicum, 1:1 mentor or expert session we do not deliver. Refund policy