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Course Outline

Introduction to Stata

  • Overview of Stata’s features and real-world applications.
  • Comparative analysis of Stata versus SPSS and R.
  • Exploring Stata syntax, core commands, and standard workflows.

Environment Setup

  • Installation and configuration of Stata.
  • Review of RStudio and essential R libraries for integration purposes.

Data Management with Stata

  • Procedures for importing and exporting data.
  • Techniques for data cleaning and transformation.
  • Strategies for efficient handling of large datasets.

Statistical Analysis in Stata

  • Generating descriptive statistics and summary tables.
  • Analyzing probability distributions and conducting hypothesis tests.
  • Performing regression analysis, including linear, logistic, and multivariate models.

Graphing and Visualization in Stata

  • Creating professional charts, plots, and graphs.
  • Customizing visualizations for presentation and reporting.

Integrating Stata and R

  • Transferring data between Stata and R environments.
  • Executing Stata commands directly from R.
  • Automating statistical workflows across both platforms.

Advanced Concepts

  • Implementing macros and loops in Stata.
  • Applying Stata to predictive modeling tasks.
  • Advanced Stata programming using do-files and ado-files.

Case Studies and Practical Applications

  • Real-world use cases in research and data science.
  • Integrating Stata and R in academic and industrial projects.

Summary and Future Directions

Requirements

  • Practical experience with SPSS for statistical analysis.
  • Proficiency in R programming.

Target Audience

  • Computer science professionals.
  • Data scientists and researchers focusing on statistical models.
  • Analysts aiming to integrate Stata workflows with R.
 35 Hours

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