3 months: build a dependable analytical foundation

Schedule: 12 weeks × 10 hours = 120 estimated hours. Reserve about 6 hours for learning, 3 for applied work, and 1 for English communication, reflection, or feedback each week. The plan assumes access to a computer and spreadsheet software; confirm access before starting.

Create one portfolio case using synthetic finance data. Each week, keep source files, assumptions, calculations, validation checks, and a short explanation together. This is a learning project, not work experience.

Week (10 h)	Objective and resources	Practice and deliverable	Completion check and remediation
1	Diagnose the starting point. Try SQLBolt lessons and a small Excel task.	Save a reconciliation workbook, a SQL query, a metric definition, and a two-minute English explanation.	Identify what was completed independently and what required hints. If joins or formulas are unfamiliar, repeat introductory exercises before advancing.
2	Refresh financial statements and accounting connections with OpenStax Principles of Financial Accounting.	Create a small synthetic transaction ledger and map transactions to accounts and statements.	Totals are internally consistent; explain profit-versus-cash effects for three transactions. Remediate by reviewing the relevant accounting chapter and reworking the entries.
3	Make spreadsheet calculations traceable. Use Microsoft’s formulas and functions overview and practice workbook.	Build a monthly budget-versus-actual table with labeled inputs, formulas, and an exception tab.	Summary ties to transaction detail; no unexplained hard-coded result values. Rebuild calculations from source if they do not tie.
4	Use SQL to filter, join, group, and summarize. Continue SQLBolt.	Query synthetic transactions and categories; export a monthly summary for comparison with Excel.	Join does not duplicate rows; date filters and null handling are documented; total matches Excel. Review joins and aggregation if results differ.
5	Build a driver-based forecast. Use selected sessions from NYU Stern’s Corporate Finance Online Class and their post-class exercises.	Add a simple 12-month forecast with explicit volume, price, and cost assumptions.	A reviewer can change an input and reproduce the forecast; actuals and forecast periods are distinct. Simplify drivers if assumptions cannot be explained.
6	Interpret variance and uncertainty. Use a relevant NYU Stern corporate-finance session and post-class test.	Write a one-page memo describing three material differences, the calculation behind each, and one follow-up question.	Every claim traces to a figure or is labeled an assumption. Rewrite unsupported causal statements as hypotheses.
7	Model and visualize data. Complete relevant Microsoft Learn Power BI paths.	Create a small model and dashboard for the finance case; if Power BI is unavailable, use a clear spreadsheet report.	Dashboard totals tie to the workbook; every metric has a label, unit, and period. Trace each visual back to a source table if it does not reconcile.
8	Introduce data-quality checks and control evidence. Use the Microsoft Learn data-analytics path for data preparation.	Add checks for duplicates, missing dates, invalid categories, and out-of-period transactions. Document exceptions and corrections.	Tests flag planted errors; corrected totals reconcile to the source. If errors are missed, add one test at a time and record expected behavior.
9	Understand public financial/payment data. Explore the Banco de México SIDIE dataset portal.	Select one relevant aggregate dataset or series; record source, definitions, date range, units, and limitations.	Reader can find the source and explain what the data does and does not represent. Do not infer individual customer or merchant behavior from aggregate figures.
10	Define payment-analysis measures on synthetic data; review the Banco de México card-transaction data description.	Create a synthetic transaction funnel and define transaction count/value, approvals, refunds, chargebacks, and net contribution.	Each metric has a written numerator, denominator, population, and period; synthetic data is labeled. Revisit definitions before adding charts.
11	Join the evidence into a decision-ready report. Review Power BI data-modeling lessons.	Combine validated SQL summaries, a spreadsheet calculation, and a report; add a base and alternative scenario.	Three key figures reconcile across artifacts; assumptions and scenario changes are visible. Trace mismatches to source, formula, or filter.
12	Present, review, and prepare applications. Use Microsoft Learn’s Power BI report-building path as a final communication/report design reference.	Deliver a five-minute English presentation, one-page summary, and two portfolio case descriptions labeled as learning projects. Complete an Excel/SQL mock task.	Another person can reproduce one key result and identify limitations. Revise unclear steps; begin applying to suitable postings when eligibility matches, without waiting for a certification.

Each numbered week is planned for 10 hours: about 6 hours learning, 3 hours applied work, and 1 hour for communication, reflection, or feedback.

Dependencies and remediation: complete weeks in order; each deliverable builds on the previous one. 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.

Weekly routine

• Every week: spend about one hour explaining a finding in English, listening back, and revising unclear language. A test score is not a substitute for a current speaking/writing sample.
• Every week: check units, time periods, duplicates, nulls, formula lineage, and reconciliation to source.
• Every two weeks: review finished work, note recurring errors, and adjust the next two weeks.
• If the 10-hour budget is unavailable: preserve validation and communication work; extend calendar duration rather than skipping checks.

Month-three readiness checkpoint

Start role-matched applications before every course is finished when the candidate can:

1. Explain a finance calculation and reconcile it to source data.
2. Complete a basic Excel and SQL task under a reasonable time limit.
3. Show a clear report with defined metrics and caveats.
4. Explain the contribution and limits of each portfolio artifact honestly.
5. Meet the posting’s degree, graduation-date, work-authorization, location, schedule, and language criteria.

This checkpoint shows learning evidence, not guaranteed employability or professional experience. See resources and evidence for the role snapshot and assessment options.