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T5· Engineering: tech

Business Analytics with AI

You will turn seller data into decisions a founder can act on Monday, with AI helping you frame, analyse, automate and write.

Choose a planRead the syllabus
Who it is for
Students from any branch, and BBA or MBA students, who want to answer business questions with numbers and recommend what to do. Comfortable with spreadsheets; SQL helps but is taught as needed. Cross-listed in the Business domain.
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
  • Business Analyst
  • Operations Analyst
  • Associate Consultant

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

02Tools you will use
  • Google SheetsFree tier
  • DuckDBFree 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
  • Tableau with Tableau AICollege licence
  • n8n (Saarthi-hosted sandbox)Saarthi sandbox
  • Saarthi MCP server (anonymised seller data)Saarthi sandbox
  • Claude free plan (chat and connectors)Free tierVerified
  • ChatGPT free planFree tier
  • statsmodels (Python)Free tier
  • 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 · Business Analytics with AI

01Framing the business question2 lessonsDecision briefs, issue trees and hypotheses, with AI as a sparring partner rather than a source of facts.
  1. Decisions, issue trees and hypotheses9 min
  2. Lab: frame the channel-mix question12 min
02Unit economics across channels2 lessonsContribution margin per unit, effective fee rates from data, returns and ad costs, cash cycles and sensitivity.
  1. What a seller keeps per unit9 min
  2. Lab: the contribution-margin model15 min
03Did it work? Causal thinking2 lessonsWhy before-and-after misleads, difference-in-differences, comparison groups, spillover and honest causal claims.
  1. Effects, not coincidences9 min
  2. Lab: did the price cut pay?15 min
04BI for decision-makers2 lessonsKPI trees, leading and lagging indicators, meaningful comparisons and dashboards that end in a 'so what'.
  1. KPI trees and weekly reviews9 min
  2. Lab: the weekly business review page14 min
05Automating the weekly business review2 lessonsReporting automations that validate, compute, draft, check, wait for approval and log, built in n8n.
  1. Automations you can trust with business data9 min
  2. Lab: the CEO brief in n8n16 min
06Memos and recommendations2 lessonsAnswer-first decision memos, honest confidence, AI drafting and red-teaming, and keeping your own voice.
  1. Writing the recommendation8 min
  2. Lab: the channel-mix memo and mock review15 min
04Live projects

Two briefs. Real-shaped work.

  1. Project 1

    Amazon versus Flipkart: a channel-mix recommendation

    A seller lists the same SKUs on Amazon and Flipkart and wants to know where to put stock, ad spend and effort. Compare contribution per unit for every SKU sold on both channels over the same period, covering realised price, fees derived from settlement data, returns cost, ad cost and cash cycle, and recommend a channel mix the founder can act on. Your fee figures are checked against Saarthi's engine for the same data-pack version.

    You hand in

    • A decision brief and issue tree agreed before analysis
    • A spreadsheet contribution model with separate Inputs, Assumptions and Model tabs and no hard-coded numbers
    • A fee reconciliation against engine_fees_v1.csv with every difference above ₹1 explained
    • A sensitivity table showing where the better channel flips under plausible assumptions
    • A one-page decision memo with an answer-first recommendation, a number audit and an AI-use disclosure
    • A mock client review with Saarthi, plus a live defence with a human panel

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

    Data: Anonymised seller data pack v1: settlement_v2.csv (Amazon), flipkart_settlement.csv, orders.csv, returns.csv, ads_spend.csv, sku_master.csv, with engine_fees_v1.csv (Saarthi engine output) for checking. Reviewed by Saarthi against a published rubric, signed off by a mentor.

  2. Project 2

    An automated weekly 'CEO brief' with human approval

    Every Monday the founder wants a short brief: how last week went, why, and what to do. Build an n8n automation that pulls the seller's anonymised data from the sandbox, validates it, computes a KPI tree with comparisons, drafts the brief with an LLM using only computed numbers, checks the draft in code, and sends it only after a human approves. Run it over several weeks of data, break it on purpose, and show that it fails safely.

    You hand in

    • A KPI tree with definitions and SQL that adds up from drivers to contribution
    • An n8n workflow (sandbox) with trigger, validation, compute, LLM draft, number and direction checks, approval, test delivery and logging
    • Logs of at least six weekly runs with drafts, check results and approval decisions, including edits
    • Failure tests: a missing day, an implausible spike and a planted wrong number, each with the outcome
    • A short design note covering failure modes, credentials, recipients and data protection
    • A live demo in which an examiner changes an input and you explain what the workflow does

    Data: Anonymised seller data pack v1, loaded in the sandbox Postgres and exposed through the sandbox MCP server: orders, settlements (Amazon and Flipkart), returns, ads_spend and sessions tables. Reviewed by Saarthi against a published rubric, signed off by a mentor.

05Practise out loud

In voice mock interviews and client calls, Saarthi plays: Kavita, COO of a Jaipur apparel brand selling on Amazon and Flipkart, in a weekly review meeting. She wants a recommendation, not a dashboard tour, asks 'so what do I do on Monday?', challenges every fee and returns assumption, and asks how confident you are and what would change your mind.

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

06Enrol

Pick a plan for Business 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