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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 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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