14 Topics
10 Days
71 Sessions
Programme Overview
Training Description
Who Should Attend
Session Objectives
- Understand the fundamentals of R for statistical data analysis.
- Master data manipulation and cleaning with R's data frames.
- Utilize statistical modeling techniques (regression, ANOVA) in R.
- Implement data visualization with ggplot2 and other R packages.
- Design and build statistical reports and presentations with R.
- Optimize R code for performance and reproducibility.
- Troubleshoot and address common challenges in R statistical analysis.
- Implement hypothesis testing and statistical inference in R.
- Integrate R with real-world data sources and applications.
- Understand how to handle large datasets and missing data in R.
- Explore advanced R packages for specialized statistical analyses.
- Apply real world use cases for R in statistical data analysis.
- Leverage R's ecosystem for efficient statistical workflows.
About the Course
This practical training equips participants with the skills to use R for exploratory data analysis, statistical inference, and evidence-based decision-making. It combines hands-on R programming with core statistical techniques, enabling participants to move from raw datasets to meaningful insights and defensible conclusions.
Participants will learn how to clean and prepare data, summarize and visualize distributions, identify patterns and relationships, test statistical assumptions, formulate hypotheses, conduct statistical tests, estimate population parameters, and interpret results. The course emphasizes practical application using real-world datasets and reproducible analytical workflows.
Curriculum & Topics
14 Topics | 71 Sessions
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Workshop 1.1:
Fundamentals of R for statistical data analysis.
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Workshop 1.2:
Overview of R's statistical and visualization capabilities.
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Workshop 1.3:
Setting up an R statistical analysis development environment.
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Workshop 1.4:
Introduction to R packages and best practices.
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Workshop 1.5:
Best practices for R statistical analysis.
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Workshop 2.1:
Implementing data manipulation with R's data frames (dplyr).
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Workshop 2.2:
Utilizing data cleaning and transformation techniques.
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Workshop 2.3:
Designing and building data preparation pipelines.
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Workshop 2.4:
Optimizing R code for data manipulation.
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Workshop 2.5:
Best practices for data manipulation.
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Workshop 2.6:
Best practices for statistical modeling.
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Workshop 3.1:
Implementing statistical modeling techniques (regression, ANOVA).
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Workshop 3.2:
Utilizing linear and non-linear models for data analysis.
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Workshop 3.3:
Designing and building statistical models for inference.
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Workshop 3.4:
Optimizing models for prediction and interpretation.
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Workshop 4.1:
Implementing data visualization with ggplot2.
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Workshop 4.2:
Utilizing graphical parameters and customization.
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Workshop 4.3:
Designing and building effective data visualizations.
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Workshop 4.4:
Optimizing visualizations for data insights.
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Workshop 4.5:
Best practices for ggplot2.
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Workshop 5.1:
Designing and building statistical reports and presentations with R.
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Workshop 5.2:
Utilizing R Markdown for reproducible reports.
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Workshop 5.3:
Implementing interactive visualizations and dashboards.
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Workshop 5.4:
Optimizing reports for stakeholder communication.
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Workshop 5.5:
Best practices for reports.
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Workshop 6.1:
Optimizing R code for performance and reproducibility.
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Workshop 6.2:
Utilizing vectorization and efficient algorithms.
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Workshop 6.3:
Implementing parallel processing and code profiling.
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Workshop 6.4:
Designing scalable R scripts.
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Workshop 6.5:
Best practices for R code optimization.
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Workshop 7.1:
Debugging common challenges in R statistical analysis.
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Workshop 7.2:
Analyzing code performance and errors.
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Workshop 7.3:
Utilizing troubleshooting techniques for problem resolution.
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Workshop 7.4:
Resolving common statistical analysis issues.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing hypothesis testing and statistical inference in R.
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Workshop 8.2:
Utilizing t-tests, chi-square tests, and confidence intervals.
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Workshop 8.3:
Designing and building statistical tests for data analysis.
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Workshop 8.4:
Optimizing tests for statistical power and significance.
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Workshop 8.5:
Best practices for hypothesis testing.
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Workshop 9.1:
Integrating R with real-world data sources and applications.
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Workshop 9.2:
Utilizing database connections and APIs.
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Workshop 9.3:
Designing and building data integration pipelines.
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Workshop 9.4:
Optimizing integration for data retrieval and processing.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Implementing techniques for handling large datasets and missing data.
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Workshop 10.2:
Utilizing data sampling and imputation methods.
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Workshop 10.3:
Designing and building data processing strategies.
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Workshop 10.4:
Optimizing data handling for large-scale analysis.
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Workshop 10.5:
Best practices for large datasets.
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Workshop 11.1:
Exploring advanced R packages for specialized statistical analyses.
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Workshop 11.2:
Utilizing packages for time series analysis (forecast).
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Workshop 11.3:
Implementing spatial data analysis with spatial packages.
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Workshop 11.4:
Designing and building solutions with specialized packages.
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Workshop 11.5:
Optimizing package usage for specific applications.
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Workshop 11.6:
Best practices for advanced packages.
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Workshop 12.1:
Implementing R for financial data analysis and risk modeling.
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Workshop 12.2:
Utilizing R for bioinformatics and genomics data processing.
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Workshop 12.3:
Implementing R for social science research and survey analysis.
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Workshop 12.4:
Utilizing R for marketing analytics and A/B testing.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Leveraging R's ecosystem for efficient statistical workflows.
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Workshop 13.2:
Utilizing project management and version control (Git).
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Workshop 13.3:
Implementing reproducible research practices.
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Workshop 13.4:
Designing and building efficient R pipelines.
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Workshop 13.5:
Best practices for efficient workflows.
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Workshop 14.1:
Implementing performance tuning for statistical models in R.
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Workshop 14.2:
Utilizing model diagnostics and evaluation metrics.
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Workshop 14.3:
Designing and building optimized models.
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Workshop 14.4:
Optimizing model performance and computational efficiency.
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Workshop 14.5:
Best practices for performance tuning.
