R For Statistical Data Analysis Training Course: Modeling & Visuals

Introduction

Unlock the power of statistical analysis with our R for Statistical Data Analysis Training Course. This program is designed to equip you with the essential skills to utilize R for statistical modeling and data visualization, enabling you to derive meaningful insights and make data-driven decisions. In today's data-rich environment, mastering R is crucial for performing advanced statistical analysis and creating compelling visualizations. Our R statistical analysis training course offers hands-on experience and expert guidance, empowering you to leverage R's extensive statistical capabilities.

This R data visualization training delves into the core concepts of statistical analysis with R, covering topics such as hypothesis testing, regression modeling, and data visualization. You'll gain expertise in using industry-standard R packages to utilize R for statistical modeling and data visualization, meeting the demands of modern data analysis projects. Whether you're a data analyst, statistician, or researcher, this R for Statistical Data Analysis course will empower you to perform rigorous statistical analyses and create impactful data visualizations.

Target Audience:

• Data Analysts
• Statisticians
• Researchers
• Data Scientists
• Business Analysts
• Biostatisticians
• Anyone needing R statistical analysis skills

Course 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.

Duration

10 Days

Course content

Module 1: Introduction to R for Statistical Data Analysis

• Fundamentals of R for statistical data analysis.
• Overview of R's statistical and visualization capabilities.
• Setting up an R statistical analysis development environment.
• Introduction to R packages and best practices.
• Best practices for R statistical analysis.

Module 2: Data Manipulation and Cleaning with R

• Implementing data manipulation with R's data frames (dplyr).
• Utilizing data cleaning and transformation techniques.
• Designing and building data preparation pipelines.
• Optimizing R code for data manipulation.
• Best practices for data manipulation.

Module 3: Statistical Modeling Techniques in R

• Implementing statistical modeling techniques (regression, ANOVA).
• Utilizing linear and non-linear models for data analysis.
• Designing and building statistical models for inference.
• Optimizing models for prediction and interpretation.
• Best practices for statistical modeling.

Module 4: Data Visualization with ggplot2

• Implementing data visualization with ggplot2.
• Utilizing graphical parameters and customization.
• Designing and building effective data visualizations.
• Optimizing visualizations for data insights.
• Best practices for ggplot2.

Module 5: Statistical Reports and Presentations

• Designing and building statistical reports and presentations with R.
• Utilizing R Markdown for reproducible reports.
• Implementing interactive visualizations and dashboards.
• Optimizing reports for stakeholder communication.
• Best practices for reports.

Module 6: R Code Optimization

• Optimizing R code for performance and reproducibility.
• Utilizing vectorization and efficient algorithms.
• Implementing parallel processing and code profiling.
• Designing scalable R scripts.
• Best practices for R code optimization.

Module 7: Troubleshooting R Statistical Analysis Challenges

• Debugging common challenges in R statistical analysis.
• Analyzing code performance and errors.
• Utilizing troubleshooting techniques for problem resolution.
• Resolving common statistical analysis issues.
• Best practices for troubleshooting.

Module 8: Hypothesis Testing and Statistical Inference

• Implementing hypothesis testing and statistical inference in R.
• Utilizing t-tests, chi-square tests, and confidence intervals.
• Designing and building statistical tests for data analysis.
• Optimizing tests for statistical power and significance.
• Best practices for hypothesis testing.

Module 9: Integration with Real-World Data Sources

• Integrating R with real-world data sources and applications.
• Utilizing database connections and APIs.
• Designing and building data integration pipelines.
• Optimizing integration for data retrieval and processing.
• Best practices for integration.

Module 10: Handling Large Datasets and Missing Data

• Implementing techniques for handling large datasets and missing data.
• Utilizing data sampling and imputation methods.
• Designing and building data processing strategies.
• Optimizing data handling for large-scale analysis.
• Best practices for large datasets.

Module 11: Advanced R Packages for Specialized Analyses

• Exploring advanced R packages for specialized statistical analyses.
• Utilizing packages for time series analysis (forecast).
• Implementing spatial data analysis with spatial packages.
• Designing and building solutions with specialized packages.
• Optimizing package usage for specific applications.
• Best practices for advanced packages.

Module 12: Real-World Use Cases

• Implementing R for financial data analysis and risk modeling.
• Utilizing R for bioinformatics and genomics data processing.
• Implementing R for social science research and survey analysis.
• Utilizing R for marketing analytics and A/B testing.
• Best practices for real-world applications.

Module 13: R Ecosystem for Efficient Workflows

• Leveraging R's ecosystem for efficient statistical workflows.
• Utilizing project management and version control (Git).
• Implementing reproducible research practices.
• Designing and building efficient R pipelines.
• Best practices for efficient workflows.

Module 14: Statistical Modeling Performance Tuning

• Implementing performance tuning for statistical models in R.
• Utilizing model diagnostics and evaluation metrics.
• Designing and building optimized models.
• Optimizing model performance and computational efficiency.
• Best practices for performance tuning.

Module 15: Future Trends in R Statistical Analysis

• Emerging trends in R statistical analysis.
• Utilizing AI and machine learning with R.
• Implementing cloud-based R environments.
• Best practices for future R applications.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.