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Course Outline
Introduction to Data Analysis Tools
- Overview of Python, R, Power Query, and Power BI
- Applications of data analysis across various industries
- Setting up the tools and environment
Data Cleaning and Preparation
- Utilizing Python libraries (Pandas) for data cleansing
- Transforming and cleaning data with Power Query
- Addressing missing data and inconsistencies
Statistical Analysis with R
- Fundamental statistical functions and data manipulation in R
- Exploratory data analysis
- Constructing and interpreting statistical models
Data Integration and Transformation
- Merging data from multiple sources using Power Query
- Incorporating Python and R workflows into Power BI
- Maintaining data consistency and quality
Visualizing Data with Power BI
- Building dynamic dashboards and visualizations
- Leveraging Power BI to uncover trends and insights
- Distributing and publishing reports
Applications and Industry Case Studies
- Real-world case studies in data analysis
- Creating workflows for typical industry scenarios
- Practical project to reinforce learning
Summary and Next Steps
Requirements
- Fundamental knowledge of statistics
- Experience with spreadsheets and data entry
- 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.
