Finance industry basic to advance topics that need to learn?

If you're specifically interested in using SQL for data analysis in the finance industry, here are some basic to advanced topics you may need to learn: 

•  Basic financial concepts: To effectively analyze financial data, it's important to have a basic understanding of financial concepts such as accounting, financial statements, and financial ratios. 

•  Data extraction and transformation: You will need to learn how to extract and transform financial data from various sources such as accounting systems, trading systems, and risk management systems. 

•  Data Warehousing: You will need to learn how to extract, transform, and load financial data into a data warehouse for reporting and analysis. 

•  Financial analysis: You will need to learn how to use SQL to perform financial analysis such as ratio analysis, trend analysis, and variance analysis. 

•  Risk management: You will need to learn how to use SQL to analyze risk-related data such as credit risk, market risk, and operational risk. 

• Time-series analysis: You will need to learn how to use SQL to analyze time-series data such as stock prices, interest rates, and currency exchange rates. 

•  Algorithmic trading: You will need to learn how to use SQL to extract, transform and load data for backtesting and executing algorithmic trading strategies. 

•  Performance measurement: You will need to learn how to use SQL to analyze and measure the performance of financial portfolios, investments, and trading strategies.

• Compliance and regulatory reporting: You will need to learn how to use SQL to extract, transform and load data for compliance and regulatory reporting such as Basel III, Solvency II, and MiFID II. 

• Data Governance: You will need to learn how to use SQL to enforce data governance policies and compliance, for example, by using constraints and indexes to maintain data integrity and improve query performance.  Advanced SQL: You will need to learn more advanced SQL features such as window functions, recursive queries, and analytics functions.

• Financial data visualization: You will need to learn how to use SQL to extract data from databases and data warehouses and then use visualization tools like Tableau, Power BI, etc. to create interactive and informative financial data visualizations.

• Portfolio optimization: You will need to learn how to use SQL to analyze and optimize financial portfolios using techniques such as mean-variance optimization and Monte Carlo simulations.  Derivatives pricing: You will need to learn how to use SQL to extract, transform and load data for pricing derivatives such as options, futures, and swaps. 

• Credit risk modeling: You will need to learn how to use SQL to extract, transform and load data for credit risk modeling such as credit scoring, default prediction, and loss given default.

• Fraud detection: You will need to learn how to use SQL to extract, transform and load data for detecting financial frauds such as insider trading, money laundering, and fraud detection.

• Financial data governance: You will need to learn how to use SQL to implement data governance policies and controls to ensure compliance with financial regulations such as Basel III, Solvency II, and MiFID II.

• Cloud integration: You will need to learn how to use SQL to access and manage financial data stored in cloud databases such as Amazon RDS, Azure SQL, and Google Cloud SQL.

• Big data: You will need to learn how to use SQL to extract, transform, and load large datasets from various sources such as Hadoop, Spark, and Kafka for analytics and machine learning.

• Data pipeline: You will need to learn how to use SQL to build data pipelines for extracting, transforming, and loading financial data from various sources into data warehouses or data lakes. 

These are some topics you may need to learn when using SQL for data analysis in the finance industry, but the specific topics you need to learn will depend on the role and the company you work for. It's worth noting that these topics are not limited to the finance industry only, but are also widely used in other industries as well.