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Course Outline
Introduction to Databricks and Financial Use Cases
- Understanding the Databricks ecosystem
- Overview of financial data analysis workflows
- Use case examples: risk modeling, financial reporting, audit logs
Getting Started with Databricks Notebooks
- Creating and navigating notebooks
- Using Python and SQL in Databricks
- Collaborating with comments and version history
Data Ingestion and Cleaning
- Importing financial data from CSV, databases, and APIs
- Using Spark DataFrames for cleaning and preparation
- Handling missing values and outliers
Transforming and Aggregating Financial Data
- Calculating KPIs and financial ratios
- Filtering, grouping, and pivoting datasets
- Time series manipulation and resampling
Visualizing Financial Insights
- Creating dashboards with Databricks visual tools
- Customizing charts for finance reporting
- Exporting visuals for presentations or regulatory review
Optimizing Queries and Using Delta Lake
- Introduction to Delta Lake architecture
- ACID transactions and data reliability
- Improving performance with data partitioning
Collaboration, Scheduling, and Sharing
- Managing access and permissions for finance teams
- Scheduling jobs for automated reporting
- Exporting data and results securely
Summary and Next Steps
Requirements
- An understanding of data analysis concepts
- Experience with Python or SQL
- Familiarity with financial data types and reporting
Audience
- Financial analysts and business intelligence professionals
- Data analysts working in the finance sector
- Data engineers supporting financial teams
14 Hours
