*We customize the course outline and content to your specific needs and relevant use cases.
Module 1: SQL basics in a finance context
- Understanding tables, rows, columns, and keys in transaction, client, controls, and risk data
- Reading schema structures and recognizing how business entities connect
- Writing basic SELECT queries with filtering, sorting, and simple conditions
- Translating common finance questions into SQL query logic
Module 2: Filtering, calculated fields, and data quality checks
- Using WHERE conditions for dates, products, channels, regions, status, and account types
- Creating calculated fields for balances, exposures, variances, and simple ratios
- Handling null values, incomplete records, and inconsistent source data
- Running simple validation checks to improve trust in query results
Module 3: Joining finance datasets
- Combining transaction, client, account, controls, and reference tables with the right join type
- Understanding one to one, one to many, and many to one relationships in finance data
- Avoiding duplicate rows and misleading totals after joins
- Building readable multi table queries for reporting and control use cases
Module 4: Grouping, summarizing, and business metrics
- Aggregating by client segment, product, region, desk, channel, and reporting period
- Using COUNT, SUM, AVG, MIN, and MAX for finance reporting questions
- Creating grouped views for volumes, balances, exceptions, and activity levels
- Interpreting grouped outputs in a business meaningful way
Module 5: Transaction analysis with SQL
- Querying transaction datasets by type, channel, status, amount band, and time period
- Building views for trends, spikes, reversals, breaks, and exceptions
- Identifying unusual patterns and operational anomalies in large transaction populations
- Structuring transaction queries so they support dashboards and review packs
Module 6: Client and account analysis
- Analyzing client activity, segmentation, tenure, and product holding patterns
- Linking client and account data to transaction and revenue behavior
- Building views for concentration, inactivity, churn signals, and account development
- Preparing outputs that support relationship management and service reviews
Module 7: Controls and reconciliation reporting
- Querying control datasets for failed checks, missing fields, threshold breaches, and process exceptions
- Building views for reconciliations, break analysis, and unresolved items
- Comparing source and target values across reporting steps
- Supporting governance and control oversight with clear SQL outputs
Module 8: Risk and exposure oriented queries
- Working with simple risk indicators, exposure values, limits, and threshold logic
- Segmenting results by counterparty, desk, product, geography, and time
- Building business views that support monitoring and escalation discussions
- Presenting exception based outputs carefully so they guide investigation without overstating conclusions
Module 9: Date logic, trends, and period based analysis
- Working with dates for transaction time, reporting cutoffs, month end, quarter end, and aging
- Creating month over month and period over period views
- Building rolling summaries and time based comparisons
- Avoiding common mistakes in time filtering and reporting windows
Module 10: Subqueries, common table expressions, and window functions
- Using subqueries to isolate logic in manageable steps
- Applying common table expressions for readability and maintainability
- Introducing window functions for ranking, running totals, moving views, and partition based analysis
- Choosing the right query structure for more complex finance questions
Module 11: Query quality, performance, and validation
- Writing queries that remain understandable for colleagues, reviewers, and auditors
- Checking totals, joins, filters, and business definitions before sharing results
- Recognizing common performance issues in large finance datasets
- Using practical habits to balance speed, accuracy, and maintainability
Module 12: From query output to decision support
- Turning SQL results into clear business answers and action points
- Preparing result sets for BI dashboards, control reporting, and management packs
- Documenting assumptions, definitions, and limitations in SQL based analysis
- Building a practical checklist for future financial data analysis work
