Course Outline

Introduction to Databricks and Financial Use Cases

  • Understanding the Databricks ecosystem
  • Overview of financial data analysis workflows
  • Use case examples: risk modeling, financial reporting, audit logs

Getting Started with Databricks Notebooks

  • Creating and navigating notebooks
  • Using Python and SQL in Databricks
  • Collaborating with comments and version history

Data Ingestion and Cleaning

  • Importing financial data from CSV, databases, and APIs
  • Using Spark DataFrames for cleaning and preparation
  • Handling missing values and outliers

Transforming and Aggregating Financial Data

  • Calculating KPIs and financial ratios
  • Filtering, grouping, and pivoting datasets
  • Time series manipulation and resampling

Visualizing Financial Insights

  • Creating dashboards with Databricks visual tools
  • Customizing charts for finance reporting
  • Exporting visuals for presentations or regulatory review

Optimizing Queries and Using Delta Lake

  • Introduction to Delta Lake architecture
  • ACID transactions and data reliability
  • Improving performance with data partitioning

Collaboration, Scheduling, and Sharing

  • Managing access and permissions for finance teams
  • Scheduling jobs for automated reporting
  • Exporting data and results securely

Summary and Next Steps

Requirements

  • An understanding of data analysis concepts
  • Experience with Python or SQL
  • Familiarity with financial data types and reporting

Audience

  • Financial analysts and business intelligence professionals
  • Data analysts working in the finance sector
  • Data engineers supporting financial teams
 14 Hours

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