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Duration 21 hours
Course Outline
Overview of Data Analysis Instruments
- Introduction to Python, R, Power Query, and Power BI
- Use cases of data analysis in various sectors
- Configuring tools and the working environment
Data Cleansing and Readiness
- Applying Python libraries (Pandas) for data refinement
- Cleansing and transforming data via Power Query
- Managing missing values and data inconsistencies
Statistical Examination with R
- Core statistical operations and data handling in R
- Exploratory analysis of data
- Constructing and deciphering statistical models
Data Merging and Conversion
- Aggregating data from multiple origins using Power Query
- Blending Python and R processes with Power BI
- Maintaining data uniformity and integrity
Data Visualization via Power BI
- Designing dynamic dashboards and visual representations
- Leveraging Power BI to detect trends and derive insights
- Distributing and releasing reports
Applications and Sector-Specific Examples
- Practical case studies in data analysis
- Creating workflows for typical industry situations
- A practical project to reinforce acquired skills
Conclusion and Future Directions
Requirements
- Fundamental grasp of statistical concepts
- Comfort with spreadsheet tools and data input
- No previous coding background needed
Intended Participants
- Business analysts
- Data specialists
- Project managers
- Administrative personnel
Testimonials (2)
How to use relationships in Power BIand the differences between columns and measurements
LEUNG Chun Hei - HAESL
Course - Advanced Power BI: Data Modeling, DAX, and Enhanced Analytics
Method of training.
