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Duration 14 hours
Course Outline
Foundations of Databricks and Financial Applications
- Exploring the Databricks ecosystem
- Understanding workflows for financial data analysis
- Case studies: risk modeling, financial reporting, and audit logs
Initiating Work with Databricks Notebooks
- Building and managing notebooks
- Applying Python and SQL within Databricks
- Collaborating through comments and version control
Data Acquisition and Purification
- Importing financial data from CSVs, databases, and APIs
- Leveraging Spark DataFrames for data cleansing and preparation
- Managing missing values and statistical outliers
Transformation and Aggregation of Financial Data
- Computing KPIs and financial ratios
- Filtering, grouping, and pivoting data sets
- Manipulating and resampling time series data
Visualizing Financial Insights
- Constructing dashboards using Databricks visualization tools
- Tailoring charts for financial reporting
- Exporting visuals for presentations or regulatory compliance reviews
Query Optimization and Delta Lake Utilization
- Overview of Delta Lake architecture
- Ensuring data reliability through ACID transactions
- Enhancing performance via data partitioning
Collaboration, Automation, and Distribution
- Overseeing access controls and permissions for finance teams
- Scheduling automated jobs for routine reporting
- Securing the export of data and results
Conclusion and Path Forward
Requirements
- A foundational grasp of data analytics principles
- Practical experience with Python or SQL
- Knowledge of financial data structures and reporting standards
Target Participants
- Financial analysts and business intelligence specialists
- Data analysts operating within the finance industry
- Data engineers assisting financial teams
