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Duration 14 hours
Course Outline
Introduction to Databricks and Its Role in Finance
- Exploring the Databricks ecosystem
- Reviewing financial data analysis workflows
- 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
- Enhancing collaboration via comments and version history
Data Ingestion and Cleansing
- Importing financial data from sources such as CSV, databases, and APIs
- Applying Spark DataFrames for data cleaning and preparation
- Managing missing values and outliers
Transforming and Aggregating Financial Data
- Computing KPIs and financial ratios
- Filtering, grouping, and pivoting datasets for deeper analysis
- Manipulating and resampling time series data
Visualizing Financial Insights
- Building dashboards using Databricks visual tools
- Tailoring charts for effective financial reporting
- Exporting visuals for presentations or regulatory compliance reviews
Optimizing Queries and Leveraging Delta Lake
- Understanding Delta Lake architecture
- Ensuring data reliability through ACID transactions
- Enhancing performance via data partitioning
Collaboration, Scheduling, and Sharing
- Administering access and permissions for finance teams
- Scheduling jobs for automated reporting processes
- Securely exporting data and analytical results
Summary and Next Steps
Requirements
- A foundational grasp of data analysis principles
- Practical experience with Python or SQL
- Knowledge of 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
