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Course Outline
Overview of Data Analysis Tools
- Introduction to Python, R, Power Query, and Power BI
- The role of data analysis in various sectors
- Configuring the tools and development environment
Data Cleansing and Readiness
- Utilizing Python libraries (specifically Pandas) for data purification
- Data refinement and conversion via Power Query
- Addressing gaps and data inconsistencies
Statistical Examination using R
- Core statistical functions and data handling in R
- Exploratory data analysis techniques
- Construction and interpretation of statistical models
Data Consolidation and Conversion
- Merging data from multiple sources using Power Query
- Combining Python and R processes within Power BI
- Maintaining data integrity and quality standards
Data Visualization via Power BI
- Building interactive dashboards and visual elements
- Leveraging Power BI to uncover trends and key insights
- Distribution and publication of reports
Practical Applications and Sector-Specific Case Studies
- Examination of real-world data analysis scenarios
- Creating workflows for typical industry challenges
- A practical project to reinforce learned concepts
Conclusions and Future Directions
Requirements
- Fundamental knowledge of statistics
- Comfort with spreadsheets and data entry processes
- No previous programming background is necessary
Target Audience
- Business analysts
- Data specialists
- Project managers
- Administrative personnel
21 Hours
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.
