6 months: deepen analysis and connect it to decisions
Complete the 3-month core first. Continue at the same assumed pace—10 hours a week, for about 240 hours total by the end of month six. Weeks 13–24 strengthen payment analytics while introducing practical corporate-finance analysis. These are study estimates; extend any week whose acceptance check is not met.
Each week is planned for 10 hours: approximately 6 hours learning, 3 applied work, and 1 communication/review. Continue the weekly English explanation and data-quality/reconciliation checks from the core; every two weeks, replan using finished work and application or interview feedback.
Dependencies and remediation: complete the weeks in order; each deliverable builds on the previous one and the 3-month core. If a completion check is missed, pause, revisit the linked resource, reduce the work to one small example, verify one calculation by hand, and request a review. Repeat the check before moving on.
| Week (10 h) | Objective and learning resource | Practice and deliverable | Completion check and remediation |
|---|---|---|---|
| 13 | Review feedback and choose a narrowly scoped question. Recheck current role requirements on employer career pages, such as Citi’s early-career programs, rather than following an old job list. | Write a project brief with user, decision, dataset, definitions, and limits. | One decision question and measurable output; remove features that do not answer it. |
| 14 | Improve SQL quality using SQLBolt review and PostgreSQL’s official tutorial where useful. | Build clean queries for transaction, date, and category summaries. | Reconcile row counts and totals after each join; document filter logic. |
| 15 | Understand payment flow with Banco de México public datasets and its card-transaction data description. | Add an aggregate context view, with data dictionary and import notes. | Clearly distinguish public aggregate context from synthetic transaction-level data. |
| 16 | Measure a payment funnel responsibly; use the Banco de México card-transaction data definitions to compare data categories with your own definitions. | Calculate submitted, approved, declined, refunded, and disputed transaction measures using synthetic records. | Denominators, exclusions, and time windows are explicit; test edge cases. |
| 17 | Analyze transaction economics. Review selected NYU Stern corporate-finance sessions and tests. | Model gross value, fees, reversals, and contribution per transaction under stated assumptions. | Independent calculation matches model; label hypothetical assumptions as hypothetical. |
| 18 | Explore segments and cohorts without overclaiming. Review Power BI reporting lessons. | Compare a small set of synthetic merchant/product/time segments; note sample-size limits. | Report sample size and avoid attributing causality to descriptive comparisons. |
| 19 | Improve a Power BI report. Use Microsoft Learn: Power BI. | Add clear filters, measures, and a data-quality view. | Measures match a direct SQL check; user can distinguish time periods and units. |
| 20 | Add reproducibility and refresh instructions. Use the PostgreSQL official tutorial as needed for query and data structures. | Document source, transformation steps, assumptions, and how to rebuild the report. | A reviewer can reproduce key measures using included files and instructions. |
| 21 | Introduce corporate-finance decisions with selected sessions from NYU Stern’s Corporate Finance Online Class. | Draft a working-capital or cost scenario based on clearly labeled synthetic assumptions. | Explain effects on profit, cash, and timing separately. |
| 22 | Learn forecast and scenario structure. Use selected NYU Stern corporate-finance sessions and post-class exercises. | Compare base, upside, and downside cases; vary a small number of drivers. | Scenario outputs respond to documented assumptions; avoid false precision. |
| 23 | Communicate a recommendation and alternatives. Review the NYU Stern corporate-finance course selectively, then practice with a reviewer. | Write a one-page decision memo linking evidence, assumptions, risk, and next question. | A reader can identify the recommendation and what evidence would change it. |
| 24 | Conduct a portfolio and interview review. Use Citi’s early-career program page as one example of employer-published eligibility and role information. | Present both projects; do a timed Excel/SQL case and a behavioral example from real experience. | Fix top three issues from reviewer feedback; separate learning projects from internship or employment history. |
Choose a secondary branch
At month four, choose one focus for the next four weeks of extension:
- Corporate finance / planning: deepen budgeting, forecast drivers, reconciliations, working capital, and variance explanation.
- Investment analysis: add statement analysis and valuation foundations using NYU Stern finance and valuation resources. Do not describe a classroom valuation as investment advice.
- Payments / FinTech analysis: deepen transaction-funnel definitions, cohort comparisons, controls, and decision communication.
Use the same evidence standard in every branch: traceable inputs, reproducible calculations, explicit assumptions, reconciliations, and a clear explanation. The 9-month extension offers further valuation and strategy practice.
Month-six checkpoint
By month six, aim to show two reproducible learning projects, one based on a finance case and one on a chosen specialty. Begin or continue applications when eligibility fits; do not postpone them solely to collect certificates. Reassess every two weeks using actual project quality and interview feedback.