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Course Outline
Introduction
Stata and Large-Scale Data Management
- Overview of the Stata statistical software package
- Command structure and syntax for Stata operations
R Programming Language
- Overview of the R programming environment
- Syntax conventions and structural framework in R
Establishment of the Analytical Environment
- Installation and configuration procedures for Stata
- Deployment of required R libraries and frameworks for government
Integration of R and Stata
- Data import and export protocols between R and Stata formats
Database Management and Data Handling in Stata
- Procedures for loading and clearing data sessions
- Compression techniques for dataset files
- Methods for importing and exporting database records
- Review, description, and summary of raw data elements
- Application of tabulations and structured tables
- Deployment of variables for data manipulation tasks
Descriptive and Predictive Analytical Methods
- Execution of distributional analysis
- Implementation of Monte Carlo simulation techniques
- Analysis of count-based data sets
- Application of survival analysis models
Hypothesis Testing Procedures
- Statistical testing and comparison of mean values
Data Visualization in Stata
- Utilization of plots, charts, and graphical outputs
- Integration of statistical metrics within visualizations
- Formatting and composition of composite graphs
Regression Modeling in R
- Application of bivariate correlation and regression techniques
- Implementation of Ordinary Least Squares (OLS), logistic, and probit models
- Analysis of interaction effects within regression frameworks
Summary and Concluding Remarks
Requirements
- Proficiency in data analysis methodologies
Target Audience
- Data Analysts providing support for government operations
35 Hours
Testimonials (1)
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