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
 21 Hours

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