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 Duration 21 hours

Course Outline

Overview of Data Analysis Methodologies

  • Introduction to Python, R, Power Query, and Power BI
  • Strategic applications of data analytics across public and private sectors
  • Configuration of technical environments and toolsets

Data Sanitization and Structuring

  • Utilizing Python frameworks (Pandas) for data refinement
  • Executing data cleaning and transformation processes via Power Query
  • Managing null values and data inconsistencies

Statistical Assessment using R

  • Core statistical functions and data manipulation capabilities in R
  • Conducting exploratory data assessments
  • Formulating and interpreting statistical models

Data Consolidation and Processing

  • Merging disparate datasets using Power Query
  • Synthesizing Python and R analytical workflows within Power BI
  • Maintaining data integrity and quality standards

Visual Representation of Data via Power BI

  • Designing interactive dashboards and visual outputs
  • Leveraging Power BI to detect trends and derive strategic insights
  • Distribution and publication of analytical reports

Practical Applications and Sector-Specific Examples

  • Examination of real-world analytical case studies
  • Construction of workflows for typical operational scenarios
  • Practical exercise to reinforce acquired knowledge

Concluding Review and Future Directions

Requirements

  • Foundational knowledge of statistical principles
  • Working proficiency with spreadsheet software and data entry procedures
  • No previous programming expertise is necessary

Intended Audience

  • Business analysts
  • Data specialists
  • Project managers
  • Administrative personnel

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