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Duration 14 hours
Course Outline
Foundations of Databricks and Applications in Finance
- Exploring the Databricks ecosystem
- Workflow overview for financial data analysis
- Illustrative use cases: risk modeling, financial reporting, and audit logging
Initiating Work with Databricks Notebooks
- Creating and navigating notebook interfaces
- Utilizing Python and SQL within Databricks
- Collaboration features including comments and version control
Data Ingestion and Refinement
- Importing financial data from CSV files, databases, and APIs
- Employing Spark DataFrames for data cleaning and preparation
- Strategies for addressing missing values and outliers
Transformation and Aggregation of Financial Data
- Computing key performance indicators (KPIs) and financial ratios
- Techniques for filtering, grouping, and pivoting datasets
- Manipulating and resampling time-series data
Visualizing Financial Insights
- Building dashboards using Databricks visual tools
- Tailoring charts for finance-specific reporting needs
- Exporting visuals for presentation or regulatory compliance reviews
Query Optimization and Delta Lake Integration
- Overview of Delta Lake architecture
- Ensuring data reliability through ACID transactions
- Enhancing performance via data partitioning strategies
Collaboration, Automation, and Distribution
- Managing access controls and permissions for finance teams
- Scheduling automated jobs for routine reporting
- Securely exporting data and analytical results
Summary and Future Directions
Requirements
- Foundational knowledge of data analysis principles
- Practical experience with Python or SQL
- Understanding of financial data structures and reporting standards
Target Audience
- Financial analysts and business intelligence specialists
- Data analysts operating within the finance industry
- Data engineers providing support to financial teams
