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Duration 14 hours
Course Outline
Introduction to Databricks and Financial Applications
- Exploring the Databricks ecosystem
- Review of financial data analysis workflows
- Real-world examples: risk modeling, financial reporting, and audit logs
Initiating Work with Databricks Notebooks
- Creating and navigating through notebooks
- Utilizing Python and SQL within Databricks
- Collaborating via comments and version history
Data Ingestion and Cleaning Processes
- Importing financial data from CSVs, databases, and APIs
- Applying Spark DataFrames for data cleansing and preparation
- Addressing missing values and outliers
Transformation and Aggregation of Financial Data
- Computing KPIs and financial ratios
- Filtering, grouping, and pivoting datasets
- Manipulating time series data and resampling
Visualizing Financial Insights
- Constructing dashboards using Databricks visualization tools
- Tailoring charts for financial reporting needs
- Exporting visuals for presentations or regulatory compliance reviews
Query Optimization and Delta Lake Integration
- Fundamentals of Delta Lake architecture
- Ensuring data reliability through ACID transactions
- Enhancing performance via data partitioning
Collaboration, Automation, and Data Sharing
- Overseeing access and permissions for finance teams
- Scheduling jobs for automated reporting cycles
- Safely exporting data and analysis results
Recap and Future Directions
Requirements
- A foundational grasp of data analysis principles
- Proficiency in Python or SQL
- Knowledge of financial data structures and reporting standards
Target Audience
- Financial analysts and business intelligence specialists
- Data analysts operating within the financial sector
- Data engineers providing support to financial teams
