Data Analysis with Python, R, Power Query, and Power BI

• Basic understanding of statistics
• Familiarity with spreadsheets and data entry
• No prior programming experience required

Audience

• Business analysts
• Data professionals
• Project managers
• Administrative staff

Python, R, Power Query, and Power BI are versatile tools for managing, analyzing, and visualizing data.

This instructor-led, live training (online or onsite) is aimed at beginner-level professionals who wish to clean and analyze data, make statistical projections, and create insightful visualizations using these tools.

By the end of this training, participants will be able to:

• Understand the basics of Python, R, Power Query, and Power BI for data analysis.
• Clean and organize datasets using Python and Power Query.
• Perform statistical analysis and projections with R.
• Create professional dashboards and reports with Power BI.
• Integrate and analyze data from multiple sources effectively.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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