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Duration 14 hours
Course Outline
Fundamentals of Databricks in Finance
- Exploring the Databricks ecosystem
- Review of financial data analysis processes
- Real-world examples: risk modeling, financial reporting, audit trails
Initiating Work with Databricks Notebooks
- Building and navigating through notebooks
- Applying Python and SQL within Databricks
- Teamwork through comments and version control
Data Collection and Purification
- Fetching financial data from CSV files, databases, and APIs
- Utilizing Spark DataFrames for data cleansing and formatting
- Addressing missing entries and anomalies
Modifying and Summarizing Financial Information
- Determining KPIs and financial metrics
- Refining, categorizing, and restructuring datasets
- Manipulating and resampling time-series data
Presenting Financial Insights Visually
- Building dashboards using Databricks visual capabilities
- Adapting charts for financial reporting purposes
- Sharing visuals for presentations or compliance checks
Enhancing Queries and Leveraging Delta Lake
- Overview of Delta Lake structure
- ACID transactions and data integrity
- Boosting efficiency through data partitioning
Teamwork, Automation, and Distribution
- Overseeing access and permissions for finance groups
- Scheduling tasks for automatic reporting
- Securly transferring data and outcomes
Conclusion and Future Steps
Requirements
- A foundational grasp of data analysis principles
- Proficiency in either Python or SQL
- Knowledge of financial data structures and reporting standards
Target Audience
- Financial analysts and business intelligence experts
- Data analysts specializing in the financial sector
- Data engineers providing support to finance departments
