Learn SQL, Python, statistics, BI and machine learning foundations, and use them to answer the questions businesses actually ask.
Leaders do not need another chart — they need someone who can frame the question, test it properly and recommend what to do next. Decision-focused analysts and data scientists are the people who get listened to.
Every week has a hands-on lab and an assignment. Open a week to see both.
Query, clean and explore data
Rebuild a spreadsheet report as SQL queries.
Answer five business questions about payments with SQL.
Build a checkout funnel from payment attempts.
Find the step with the biggest drop-off and size it.
Clean a messy wallet transactions export in pandas.
Document every cleaning decision and its impact.
Explore what separates active and churned wallet users.
Write up three findings with confidence intervals.
Communicate findings that drive action
Build a payments conversion dashboard in Power BI.
Add drill-through by method and bank, plus a KPI dictionary.
Turn your dashboard into a one-page insight summary.
Present your findings in three minutes to a mock leadership team.
Analyse a checkout A/B test end to end.
Recommend ship or no-ship and explain the risks.
Use ML to support decisions
Predict which card transactions are fraudulent.
Compare two models and explain the trade-offs.
Explain your fraud model with SHAP values.
Write a threshold memo based on the cost of errors.
Frame your capstone decision question and success metrics.
Deliver the decision pack and recorded presentation.
Learners new to coding can take up to 12 weeks; the extra two weeks are reserved for SQL and Python practice.
Plan for 8–10 hrs/week, including the live session.
A dashboard tracking authorisation rates, failure reasons and conversion by method and bank.
Checkout sessions and payment attempts
Cohort and survival analysis showing when and why wallet users stop transacting.
Wallet users, top-ups and transactions
Segment-level fraud rates with a cost-based review threshold recommendation.
Labelled card transactions
A five-slide pack that recommends one decision, with evidence and expected impact.
Combined outputs from your projects
Combine business intelligence and a predictive model to recommend which merchants a payments company should grow, monitor or offboard.
Dataset: Merchant profiles, monthly volumes, refunds and chargebacks
Prefer another domain? Choose SaaS, E-commerce, Logistics or Healthcare at the capstone kick-off.
Answer business questions with SQL and BI
Measure and improve products and operations
Build and explain predictive models
These are the roles this track prepares you for. AI4Impact provides career support; it does not guarantee employment, interviews or a particular salary.
Completion records that you did the work. The professional certificate is awarded only after an independently reviewed capstone and a recorded defence, so it means something to the people hiring.
Add it to any track. Five modules that take you from a finished capstone to real interviews, for ₹24,999.
Each week has short recorded lessons, a hands-on lab and an assignment, plus a live clarification session with your trainer. Plan for 8–10 hours a week, including the live session.
They are part of Programme+: five one-to-one 30-minute sessions you book through your learner dashboard and use for scoping, reviews or career direction.
Fintech is the default. If you prefer another domain, you can choose SaaS, E-commerce, Logistics or Healthcare at the capstone kick-off.
No. AI4Impact provides career support and recommendations, but it does not guarantee employment, interviews or a particular salary. Hiring decisions are made by employers.
Book a free 20-minute call. We look at your background and tell you honestly which track fits, or if none of them do yet.
