Data Analytics for Finance
Learn end-to-end financial analytics with Python, SQL, Power BI, and real banking datasets. Build dashboards, perform portfolio and credit-risk analysis, and automate financial insights using data-science workflows.
Analytics, specialized for finance
General analytics skills become far more valuable when they are specialized to a domain, and finance is one of the richest. This program takes the core analytics toolkit and applies it to real financial data: banking datasets, market data, and the KPIs and risk measures that finance actually runs on. The result is capability that maps directly to roles in banks, fintechs, and finance functions.
The program is deliberately hands-on and portfolio-driven, built around real datasets and a capstone rather than abstract exercises. Because it works with the kinds of data and questions a finance analyst faces daily, portfolio performance, credit risk, profitability, it produces work that is immediately recognizable and credible to a finance employer.
The domain specialization is what makes this program's graduates immediately credible to a finance employer. Producing a real bank-performance analysis, with the KPIs and risk measures finance actually uses, is recognizable in a way that generic analytics work is not, which is what turns skills into interviews.
The financial analytics workflow
The program builds the full workflow: Python and Pandas for finance, SQL for financial data, and Excel for FP&A models, then the data work of cleaning and reconciling financial data and building KPI models. This foundation is what makes the later analysis trustworthy, because financial data is unforgiving of quality problems and controls matter.
On top of that foundation, you build the analyses that define financial analytics roles: Power BI dashboards and executive KPI reporting, portfolio analysis, credit-risk analysis, and profitability and P&L analytics. Automating recurring financial reporting is treated as a core skill, because so much of a finance analyst's value is in turning a manual monthly grind into a reliable automated flow.
The workflow focus reflects how finance analytics really works: the data cleaning and controls matter as much as the analysis, because financial numbers are scrutinized and errors are costly. Learning to produce trustworthy, controlled financial data is a large part of what the role actually values.
A capstone that proves it
The program culminates in a bank-performance analytics capstone that ties everything together: a governed dataset, a dashboard of the key metrics, portfolio and credit-risk analysis, and an automated reporting flow. Building it end to end is what turns a set of techniques into demonstrable capability.
That capstone is designed to be an interview-ready portfolio piece, because the point of the program is not only to learn financial analytics but to be able to show it. Walking into a finance-analytics interview with a real bank-performance analysis you built and can explain is worth more than any list of topics covered.
The capstone is the point of the whole program, because in analytics a portfolio beats a transcript. Walking into an interview with a bank-performance analysis you built and can defend is the most direct evidence of capability a finance-analytics candidate can offer.
See the method, not just the topic
A representative worked example from the program, so you can see the level of concreteness the curriculum works at.
-- Non-performing loan (NPL) ratio by customer segment.
-- A core credit-quality KPI on any bank dashboard.
SELECT
c.segment,
COUNT(*) AS total_loans,
SUM(CASE WHEN l.days_past_due > 90
THEN 1 ELSE 0 END) AS npl_count,
ROUND(
100.0 * SUM(CASE WHEN l.days_past_due > 90
THEN 1 ELSE 0 END) / COUNT(*),
2
) AS npl_ratio_pct
FROM loans l
JOIN customers c ON c.customer_id = l.customer_id
GROUP BY c.segment
ORDER BY npl_ratio_pct DESC;
-- The 90-days-past-due threshold is the standard NPL definition.
-- This single query feeds a KPI card and a segment breakdown.The full syllabus
Four modules of five chapters each, sequenced so the material builds cumulatively. Each chapter carries a note on what it teaches.
Module 1Foundations of financial analytics
- 01Introduction to financial analyticsFinancial analytics applies data skills to money. Domain focus is what makes general skills valuable.
- 02The banking and markets data landscapeYou learn the banking and markets data landscape. Knowing the data landscape orients everything that follows.
- 03Python and Pandas for financePython and Pandas are tuned to financial workflows. Finance workflows have their own patterns and pitfalls.
- 04SQL queries for financial dataSQL is applied to real financial data. SQL is where most financial data questions are answered.
- 05Excel for FP&A modelsExcel remains central to FP&A modeling. Excel and code coexist in real finance work.
Module 2Financial data workflows
- 06Working with real banking datasetsReal banking datasets ground the whole program. Real datasets make the practice indistinguishable from the job.
- 07Cleaning and reconciling financial dataCleaning and reconciling financial data is unforgiving work. Financial data is unforgiving of quality problems.
- 08Building KPI and metric modelsKPI and metric models are the language of finance reporting. KPI models are how finance speaks to itself.
- 09Automating recurring financial reportingAutomating reporting turns a monthly grind into a flow. Automation is much of a finance analyst's real value.
- 10Data quality and controls in financeData quality and controls matter especially in finance. Controls are part of the work, not an afterthought.
Module 3Analysis and dashboards
- 11Power BI dashboards for financePower BI dashboards make finance data actionable. Dashboards turn finance data into decisions.
- 12KPI models and executive reportingKPI models drive executive reporting. KPI models drive the reporting executives read.
- 13Portfolio analysisPortfolio analysis is a core finance-analytics skill. Portfolio analysis is a signature finance skill.
- 14Credit-risk analysisCredit-risk analysis quantifies lending quality. Credit-risk analysis quantifies the lending book.
- 15Profitability and P&L analyticsProfitability and P&L analytics explain the numbers. P&L analytics explains where the money went.
Module 4Bringing it together
- 16Time-series analysis of financial metricsTime-series analysis tracks financial metrics over time. Trends over time reveal what a snapshot hides.
- 17Scenario and sensitivity analysisScenario and sensitivity analysis stress the assumptions. Scenarios test how robust the numbers are.
- 18Automating financial insights end to endInsights are automated end to end. End-to-end automation is the mark of maturity.
- 19Presenting to finance stakeholdersYou learn to present to finance stakeholders. Presenting to finance stakeholders is its own skill.
- 20Capstone: bank performance analyticsThe capstone is a full bank-performance analysis. The capstone is interview-ready portfolio proof.
How the program is taught
The program is hands-on and portfolio-driven, built on real banking and markets datasets and culminating in a bank-performance analytics capstone. The most effective approach is to treat the capstone and its components as interview evidence from the start, because in analytics a portfolio is worth more than a list of topics.
It teaches the full workflow, data cleaning and controls through analysis and automated reporting, because financial analytics is as much about trustworthy data as about the analysis itself. Working that whole chain, rather than just the analytical end, is what makes the resulting skills match the real role.
Where finance analytics leads
Financial analytics skills map to well-defined roles across banks, fintechs, and finance functions, from finance data analyst to FP&A and risk analyst. Because the program produces recognizable finance deliverables, its graduates can demonstrate capability that a finance employer immediately understands.
Within the journey, this is the first applied program, taking the core analytics and data-science skills into banking and markets. It pairs with the Machine Learning & AI in Finance program, which pushes the same domain into predictive modeling.
What makes this program different
General analytics courses are common; this one specializes the whole toolkit to finance, with real banking data and the KPIs and risk measures finance actually uses. That domain grounding is its distinguishing feature, and it is what makes graduates credible to finance employers.
The capstone is the second distinction. A complete, defensible bank-performance analysis is exactly the kind of portfolio evidence that wins finance-analytics interviews, which generic coursework cannot provide.
What you will be able to do
- Run end-to-end financial analytics in Python and SQL
- Build finance dashboards and KPI models in Power BI
- Perform portfolio and credit-risk analysis
- Automate recurring financial reporting
- Deliver a bank-performance analytics capstone
Who should take it
- Finance professionals and analysts
- Data engineers and scientists in BFSI
- MBA and finance students
- Risk, compliance, and BI professionals in finance
Tools and how they are used
The program uses Python and Pandas, SQL, Power BI, and Excel, the standard finance-analytics stack, applied to real banking datasets. Each tool is taught for its role in the finance workflow rather than in isolation.
The emphasis is on producing trustworthy, controlled financial data and clear reporting, because in finance the quality and defensibility of the numbers matter as much as the analysis. The tools serve that end.
Common questions and how to prepare
A common question is whether a finance background is required; it is not, though it helps. The program builds the financial context as it goes, and the core analytics skills, ideally from the earlier programs, are the more important preparation. Comfort with SQL and Python accelerates progress.
The common pitfall is treating the capstone as an exercise rather than a portfolio piece. Building it as something you can show and defend, on real data, is what turns the program into a career result rather than a certificate.
How it fits the wider track
This is the first applied program, the point where general analytics becomes domain expertise. It draws on the SQL foundation, business intelligence, and data science, and specializes them to banking and markets.
It pairs with the Machine Learning & AI in Finance program, and its finance grounding connects to the firm's broader BFSI and risk offerings, making it a bridge between analytics and financial domain work.
What you build and keep
Build a bank-performance analytics capstone on real banking and markets data: a governed dataset, a Power BI dashboard of the key KPIs, a portfolio and credit-risk analysis in Python and SQL, and an automated reporting flow, delivered as an interview-ready portfolio piece.
Format: Self-paced with 300+ lessons, 50+ hours, real datasets, and a capstone; builds on core analytics skills.
Run this program for your team
Every program can be delivered as a private, tailored cohort for your organization, aligned to your systems, policies, and career frameworks.
Scope a corporate cohortFrequently asked questions
What is the Data Analytics for Finance program?
Learn end-to-end financial analytics with Python, SQL, Power BI, and real banking datasets. Build dashboards, perform portfolio and credit-risk analysis, and automate financial insights using data-science workflows.
Who is this program for?
It suits finance professionals and analysts, along with others described on this page.
How is it delivered?
Self-paced with 300+ lessons, 50+ hours, real datasets, and a capstone; builds on core analytics skills.
Is there a project or capstone?
Build a bank-performance analytics capstone on real banking and markets data: a governed dataset, a Power BI dashboard of the key KPIs, a portfolio and credit-risk analysis in Python and SQL, and an automated reporting flow, delivered as an interview-ready portfolio piece.
How does this fit the wider journey?
The first applied program. It takes the core analytics and data-science skills into banking and markets, and it pairs with the Machine Learning & AI in Finance program, which pushes the same domain into predictive modeling.
Can my organization run this as a private cohort?
Yes. Every program can be delivered as a tailored corporate cohort. Contact us to scope it.