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Duration 14 hours
Course Outline
Introduction to Databricks and Financial Applications
- Exploring 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
- Utilizing Python and SQL within Databricks
- Collaborating through comments and version history
Data Ingestion and Cleaning
- Importing financial data from CSVs, databases, and APIs
- Leveraging Spark DataFrames for cleaning and preparation
- Addressing missing values and outliers
Transforming and Aggregating Financial Data
- Computing KPIs and financial ratios
- Filtering, grouping, and pivoting datasets
- Manipulating and resampling time series data
Visualizing Financial Insights
- Building dashboards with Databricks visual tools
- Tailoring charts for financial reporting
- Exporting visuals for presentations or regulatory review
Optimizing Queries and Leveraging Delta Lake
- Fundamentals of Delta Lake architecture
- ACID transactions and data integrity
- Enhancing performance through data partitioning
Collaboration, Scheduling, and Sharing
- Managing access and permissions for finance teams
- Scheduling jobs for automated reporting
- Securely exporting data and results
Summary and Next Steps
Requirements
- A foundational understanding of data analysis concepts
- Practical experience with Python or SQL
- Familiarity with financial data types and reporting standards
Target Audience
- Financial analysts and business intelligence specialists
- Data analysts operating within the finance sector
- Data engineers supporting financial teams
