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Duration 14 hours
Course Outline
Introduction to Databricks and Its Applications in Finance
- Exploring the Databricks ecosystem
- Overview of key financial data analysis workflows
- Real-world examples: risk modeling, financial reporting, and audit logging
Getting Started with Databricks Notebooks
- Creating and navigating through notebooks
- Utilizing Python and SQL within Databricks
- Collaborating through comments and tracking version history
Data Ingestion and Data Cleansing
- Importing financial data from CSV files, databases, and APIs
- Leveraging Spark DataFrames for data cleaning and preparation
- Managing missing values and identifying outliers
Transforming and Aggregating Financial Data
- Computing KPIs and financial ratios
- Filtering, grouping, and pivoting datasets for analysis
- Manipulating and resampling time series data
Visualizing Financial Insights
- Building dashboards using Databricks' visual tools
- Tailoring charts for specific finance reporting needs
- Exporting visuals for use in presentations or regulatory reviews
Optimizing Queries and Leveraging Delta Lake
- Understanding the architecture of Delta Lake
- Implementing ACID transactions for data reliability
- Enhancing performance through data partitioning
Collaboration, Scheduling, and Sharing
- Managing access controls and permissions for finance teams
- Scheduling jobs for automated reporting processes
- Securely exporting data and analysis results
Summary and Recommended Next Steps
Requirements
- A solid grasp of fundamental data analysis concepts
- Proficiency in either Python or SQL
- Knowledge of various financial data types 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
