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Duration 14 hours
Course Outline
Introduction to Databricks and Finance Applications
- Exploring the Databricks ecosystem
- Reviewing financial data analysis workflows
- Case studies: risk modeling, financial reporting, and audit logs
Initial Setup with Databricks Notebooks
- Creating and navigating through notebooks
- Utilizing Python and SQL within Databricks
- Collaborating via comments and version history
Data Ingestion and Preparation
- Importing financial data from CSVs, databases, and APIs
- Applying Spark DataFrames for data cleansing and preparation
- Addressing missing values and outliers
Transformation and Aggregation of 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 financial reporting needs
- Exporting visuals for presentations or regulatory compliance
Query Optimization and Delta Lake Implementation
- Overview of Delta Lake architecture
- Understanding ACID transactions and data integrity
- Enhancing performance through data partitioning
Collaboration, Scheduling, and Distribution
- Administering access and permissions for finance teams
- Scheduling automated jobs for reporting
- Securing the export of data and results
Conclusion and Recommendations
Requirements
- A solid grasp 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 financial sector
- Data engineers providing support to finance teams
Testimonials (1)
All the topics covered, although many were very quick, give us an idea of what we will need to delve into further. Additionally, I liked that we got to do some hands-on practice, although I still believe the course deserves more.
Sandra Mariela Lopez Bernal - Kueski
Course - Databricks
Machine Translated
