Data Analysis with Databricks for Finance Training Course

Overview of Databricks and its Financial Applications

• Exploring the Databricks ecosystem.
• Reviewing standard workflows for financial data analysis.
• Examining use case examples such as risk modeling, financial reporting, and audit logs.

Initial Steps with Databricks Notebooks

• Setting up and navigating notebook interfaces.
• Implementing Python and SQL within Databricks.
• Enhancing collaboration through comments and version history tracking.

Data Ingestion and Cleansing

• Integrating financial data from CSV files, databases, and APIs.
• Utilizing Spark DataFrames for data cleansing and preparation.
• Addressing missing values and identifying outliers.

Transformation and Aggregation of Financial Data

• Computing KPIs and key financial ratios.
• Applying filtering, grouping, and pivoting techniques to datasets.
• Managing time series data through manipulation and resampling.

Visualizing Financial Insights

• Designing dashboards using Databricks’ built-in visual tools.
• Tailoring charts for specific financial reporting needs.
• Exporting visuals for use in presentations or regulatory reviews.

Query Optimization and Delta Lake Integration

• Fundamentals of Delta Lake architecture.
• Ensuring data reliability through ACID transactions.
• Boosting performance via data partitioning strategies.

Collaboration, Scheduling, and Data Sharing

• Configuring access controls and permissions for finance teams.
• Setting up job schedules for automated reporting.
• Securely exporting data and analytical results.

Conclusion and Recommendations for Next Steps