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Duration 14 hours
Course Outline
Introduction to Databricks and Applications in Finance
- Exploring the Databricks ecosystem
- An overview of workflows for financial data analysis
- Practical examples: risk modeling, financial reporting, and audit logs
Starting with Databricks Notebooks
- Creating and navigating through notebooks
- Integrating Python and SQL within Databricks
- Collaborating via comments and tracking version history
Data Ingestion and Data Cleaning
- Importing financial data from CSV files, databases, and APIs
- Utilizing Spark DataFrames for data cleaning and preparation
- Addressing missing values and identifying outliers
Transforming and Aggregating Financial Data
- Computing KPIs and key financial ratios
- Filtering, grouping, and pivoting datasets for analysis
- Manipulating time series data and resampling intervals
Visualizing Financial Insights
- Building dashboards using Databricks visual tools
- Customizing charts to meet finance reporting requirements
- Exporting visual assets for presentations or regulatory compliance
Optimizing Queries and Leveraging Delta Lake
- Understanding the architecture of Delta Lake
- Ensuring data reliability through ACID transactions
- Enhancing performance via data partitioning strategies
Collaboration, Job Scheduling, and Data Sharing
- Managing access controls and permissions for finance teams
- Configuring scheduled jobs for automated reporting
- Securely exporting data and analysis results
Wrap-up and Path Forward
Requirements
- A solid grasp of fundamental data analysis principles
- Proficiency in Python or SQL
- Knowledge of various 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
