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Duration 14 hours
Course Outline
Introduction to Databricks and its Applications in Finance
- Exploring the Databricks ecosystem
- Examining typical financial data analysis workflows
- Practical use cases: risk modeling, financial reporting, and audit logs
Initiating Work with Databricks Notebooks
- Creating and navigating the notebook interface
- Integrating Python and SQL within Databricks
- Utilizing comments and version history for effective collaboration
Ingesting and Cleansing Data
- Importing financial data from sources such as CSV files, databases, and APIs
- Leveraging Spark DataFrames for data cleaning and preparation
- Addressing missing values and identifying outliers
Transforming and Aggregating Financial Data
- Computing KPIs and key financial ratios
- Applying filters, grouping, and pivoting to datasets
- Managing time series data through manipulation and resampling
Visualizing Financial Insights
- Building dashboards using Databricks’ native visualization tools
- Customizing charts for specific financial reporting needs
- Exporting visual assets for presentations or regulatory reviews
Optimizing Queries and Leveraging Delta Lake
- Understanding the architecture of Delta Lake
- Ensuring data reliability through ACID transactions
- Enhancing performance via strategic data partitioning
Collaboration, Scheduling, and Data Sharing
- Administering access controls and permissions for finance teams
- Configuring job schedules for automated reporting
- Securely exporting data and analytical results
Summary and Future Recommendations
Requirements
- A foundational understanding of data analysis principles
- Proficiency with Python or SQL
- Knowledge of financial data structures and reporting standards
Target Audience
- Financial analysts and business intelligence specialists
- Data analysts operating within the finance industry
- Data engineers providing support to financial teams
