Designed Data Visualizations and Interactive Dashboards to create presentations on energy cost and analysis
Developed in Microsoft Power BI and incorporates the use of DAX to create calculated columns, measures, and virtual tables, and M to create queries and shape data
Interesting Findings:
Manhattan has an Average Facility Amount, however has the Greatest Energy Usage and is the Least Energy Efficient in its Cost/Facility-Count ratio
Brooklyn has the Most Facility Amount, however is the Second-Least Energy Efficeint
Bronx is the Most Energy Efficient
Queens is Average across the board in terms of Energy Usage, Cost, and Efficiency
Staten Island has the Least Facility Amount trailing second-least by ~40%, and is the Second-Most Energy Efficient
Research and dissect the visualizations, DAX code, Calculated columns and tables from various professional PBIX files
Understanding the differences between traditional OLTP and columnar databases
Client-side loading external data sources to form a client-side data warehouse that (ETL) (extract transform and load using PowerQuery (M)) evolving the creation of various staging queries to shape data efficiently into a BISM (Business Modeling Sematic Model) data model.
Applying parallels from Relational database SQL skills to functional programming in DAX (Data Analysis Expressions) for PowerBI or PowerPivot.
Data Analysis expressions (DAX) to create calculated columns, measures and virtual tables using skills learned within the prerequisites
Create Pivot tables, Charts and Key Point Indicators (KPI)
PowerBI Desktop and PowerPivot Excel 2019 or better to prototype BISM models
Use techniques to slice and dice the decision support data to provide the macro to micro perspectives for the business user
