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Duration 14 hours
Course Outline
Introduction to Databricks and Financial Applications
- Exploring the Databricks ecosystem
- Overview of workflows for financial data analysis
- Case studies: risk modeling, financial reporting, and audit logs
Getting Started with Databricks Notebooks
- Creating and navigating through notebooks
- Utilizing Python and SQL within Databricks
- Collaborating using comments and version history
Data Ingestion and Cleansing
- Importing financial data from CSV files, databases, and APIs
- Employing Spark DataFrames for data cleaning and preparation
- Managing missing values and outliers
Transforming and Aggregating Financial Data
- Computing KPIs and financial ratios
- Applying filters, grouping, and pivoting datasets
- Manipulating and resampling time series data
Visualizing Financial Insights
- Building dashboards using Databricks visualization tools
- Tailoring charts for financial reporting purposes
- Exporting visuals for presentations or regulatory audits
Query Optimization and Delta Lake
- Overview of Delta Lake architecture
- ACID transactions and data integrity
- Enhancing performance through data partitioning
Collaboration, Scheduling, and Sharing
- Administering access rights and permissions for finance teams
- Scheduling automated jobs for reporting
- Securely exporting data and results
Summary and Future Directions
Requirements
- A solid grasp of data analysis principles
- Proficiency in Python or SQL
- A working knowledge of financial data structures and reporting standards
Target Audience
- Financial analysts and business intelligence specialists
- Data analysts operating within the finance sector
- Data engineers providing support to financial teams
