Course Outline
Introduction to Data Analysis Tools
- Overview of Python, R, Power Query, and Power BI
- Applications of data analysis across industries
- Setting up the tools and environment
Data Cleaning and Preparation
- Using Python libraries (Pandas) for data cleaning
- Data cleaning and transformation with Power Query
- Handling missing data and inconsistencies
Statistical Analysis with R
- Basic statistical functions and data manipulation in R
- Exploratory data analysis
- Building and interpreting statistical models
Data Integration and Transformation
- Combining data from multiple sources with Power Query
- Integrating Python and R workflows with Power BI
- Ensuring data consistency and quality
Visualizing Data with Power BI
- Creating dynamic dashboards and visualizations
- Using Power BI to identify trends and insights
- Sharing and publishing reports
Applications and Industry Case Studies
- Real-world case studies in data analysis
- Developing workflows for common industry scenarios
- Hands-on project to consolidate learning
Summary and Next Steps
Requirements
- Basic understanding of statistics
- Familiarity with spreadsheets and data entry
- No prior programming experience required
Audience
- Business analysts
- Data professionals
- Project managers
- Administrative staff
Testimonials (3)
The basic structure of Power BI, its main functions, as well as some advanced functions
Alessandro Binaghi - Great Place to Work(R) Institute Italia Srl
Course - PL-300: Microsoft Power BI Data Analyst
I was pleasantly surprised by the trainer's attitude toward the psychological aspects of the reporting activity, not just the technical ones. I believe the most valuable part of this course is that it showed me what excellence looks like in the field I have chosen, and that motivates me greatly. As for technical elements, it helped me the most to understand DAX concepts and how to incorporate them into my work.
Fufa Claudia Alina Stefania - Zentiva
Course - Power BI - Advanced
Machine Translated
Method of training.