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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 trails
Initiating Work with Databricks Notebooks
- Creating and navigating through notebooks
- Leveraging Python and SQL within Databricks
- Collaborating through comments and version control
Data Ingestion and Cleansing
- Importing financial data from CSVs, databases, and APIs
- Employing Spark DataFrames for data preparation and cleaning
- Addressing missing values and outliers
Transforming and Aggregating Financial Data
- Computing KPIs and financial ratios
- Applying filters, grouping, and pivoting to datasets
- Manipulating and resampling time series data
Visualizing Financial Insights
- Building dashboards using Databricks visualization tools
- Customizing charts for financial reporting purposes
- Exporting visuals for presentations or regulatory compliance
Query Optimization and Delta Lake
- Understanding Delta Lake architecture
- Ensuring data reliability through ACID transactions
- Enhancing performance via data partitioning
Collaboration, Automation, and Sharing
- Managing access controls and permissions for finance teams
- Scheduling automated reporting jobs
- Securely exporting data and analytical results
Summary and Future Directions
Requirements
- A foundational understanding of data analysis principles
- Proficiency in 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 sector
- Data engineers providing support to financial teams
