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Duration 14 hours
Course Outline
Foundations of Databricks and Applications in Finance
- Exploring the Databricks ecosystem
- Reviewing workflows for financial data analysis
- Case studies: risk modeling, financial reporting, and audit logs
Initial Steps with Databricks Notebooks
- Generating and navigating through notebooks
- Utilizing Python and SQL within Databricks
- Collaborating via comments and version history
Data Intake and Purification
- Bringing in financial data from CSV files, databases, and APIs
- Applying Spark DataFrames for cleaning and preparation
- Addressing missing values and outliers
Modifying and Summarizing Financial Data
- Computing KPIs and financial ratios
- Filtering, grouping, and pivoting datasets
- Manipulating and resampling time series data
Visualizing Financial Insights
- Building dashboards using Databricks' visual tools
- Tailoring charts for finance reporting
- Exporting visuals for presentations or regulatory review
Query Optimization and Delta Lake Integration
- Introduction to Delta Lake architecture
- ACID transactions and data reliability
- Enhancing performance through data partitioning
Teamwork, Automation, and Distribution
- Oversight of access and permissions for finance teams
- Scheduling tasks for automated reporting
- Securely exporting data and results
Overview and Subsequent Steps
Requirements
- A grasp of fundamental data analysis concepts
- Proficiency with Python or SQL
- Knowledge of financial data types and reporting standards
Intended Audience
- Financial analysts and business intelligence professionals
- Data analysts employed in the finance sector
- Data engineers providing support to financial teams
