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Designed Data Visualizations and Interactive Dashboards to create presentations on energy cost and analysis
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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
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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
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Highlights:
- Syllabus
- 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
- Building and implementing relational databases
- Power Query Fundamentals
- Power Query Essentials
- Power Query Advanced
- Ultimate Beginners Guide to Power BI
- Ultimate Beginners Guide to Power DAX