Fintech Data Analytics Course

Lesson 1 • Data as a Fintech Asset

Explains how fintech firms generate, capture, and monetise data. Connects data strategy to competitive advantage.

Lesson 2 • Data Lifecycle in Financial Services

Traces data from ingestion to decision-making in a fintech context. Grounds students in operational data realities before tool use.

Lesson 3 • The Fintech Landscape Today

Covers major fintech verticals and their business models. Establishes the industry context needed for all subsequent analytics work.

Lesson 1 • Business Intelligence Platforms

Introduces BI tools for building interactive fintech dashboards. Students connect data sources and create shareable visual reports.

Lesson 2 • Spreadsheet Analytics for Finance

Covers advanced spreadsheet techniques applied to financial metrics. Bridges familiar tools with professional analytical workflows.

Lesson 3 • Relational Databases and SQL Basics

Introduces relational data models and SQL querying for financial datasets. Enables students to extract and filter data independently.

Lesson 4 • Cloud Data Platforms in Fintech

Surveys cloud-based data warehouses and their fintech use cases. Prepares students to work within modern cloud analytics environments.

Lesson 1 • Designing a Metrics Framework

Guides students through building a structured, goal-aligned KPI system. Applies all prior metrics concepts into a unified reporting structure.

Lesson 2 • Unit Economics and LTV

Builds the analytical framework for evaluating per-customer profitability. Students compute LTV:CAC ratios and assess business viability.

Lesson 3 • Retention and Engagement Metrics

Examines metrics that track user loyalty and product engagement over time. Connects retention data to revenue sustainability.

Lesson 4 • Customer Acquisition Metrics

Covers metrics that measure the efficiency of acquiring new users. Links acquisition cost to lifetime value for strategic evaluation.

Lesson 1 • Churn Prediction and Prevention

Applies predictive logic to identify at-risk customers before they leave. Connects churn signals to retention intervention strategies.

Lesson 2 • Customer Lifetime Value Modelling

Builds probabilistic and historical LTV models for fintech customers. Supports resource allocation and acquisition budget decisions.

Lesson 3 • Customer Segmentation Methods

Introduces demographic, behavioural, and value-based segmentation approaches. Enables targeted product and marketing decisions.

Lesson 4 • Cohort Analysis Techniques

Teaches cohort construction and retention curve interpretation. Reveals how user behaviour evolves over time by acquisition group.

Lesson 1 • Credit Risk Fundamentals

Defines probability of default, loss given default, and exposure at default. Establishes the risk vocabulary used in lending analytics.

Lesson 2 • Operational and Liquidity Risk Metrics

Examines metrics for operational failures and liquidity stress in fintech firms. Prepares students to monitor non-credit risk dimensions.

Lesson 3 • Portfolio Risk Metrics

Covers metrics for monitoring a lending portfolio's health over time. Connects individual loan risk to aggregate portfolio performance.

Lesson 4 • Fraud Detection Analytics

Introduces rule-based and statistical approaches to detecting fraudulent transactions. Builds skills for monitoring and alerting on anomalous activity.

Lesson 1 • A/B Testing and Experimentation

Covers the design, execution, and analysis of controlled product experiments. Students apply statistical significance to real fintech test scenarios.

Lesson 2 • Growth Accounting Frameworks

Introduces growth accounting to decompose user base changes into components. Reveals the drivers of net growth for strategic planning.

Lesson 3 • Referral and Viral Growth Metrics

Measures the effectiveness of referral programmes and organic viral loops. Connects viral coefficient to sustainable user acquisition.

Lesson 4 • Pricing Analytics

Applies data analysis to fintech pricing decisions and fee optimisation. Students evaluate price elasticity and competitive positioning.

Lesson 1 • Data Lineage and Audit Trails

Explains how to document data transformations for regulatory auditability. Ensures students can trace any reported figure back to its source.

Lesson 2 • Regulatory Data Requirements

Maps the data fields and formats required by financial regulators. Establishes the compliance context for all reporting analytics work.

Lesson 3 • Anti-Money Laundering Analytics

Applies data analytics to detect and report suspicious financial activity. Connects transaction pattern analysis to compliance obligations.

Lesson 4 • Automated Compliance Reporting

Introduces tools and pipelines for automating recurring regulatory submissions. Reduces manual error and improves reporting cycle efficiency.

Lesson 1 • Strategic Dashboards and Storytelling

Teaches how to design executive dashboards and present data-driven narratives. Bridges analytical output with strategic decision-making.

Lesson 2 • Predictive Modelling for Fintech

Covers supervised learning models applied to fintech prediction problems. Students evaluate model performance using financial business metrics.

Lesson 3 • Natural Language Processing in Finance

Introduces NLP techniques for analysing financial text and customer feedback. Connects sentiment and topic analysis to business decisions.

Lesson 4 • Unsupervised Learning Applications

Applies clustering and dimensionality reduction to fintech datasets. Reveals hidden patterns in customer and transaction data.