Enterprises

R - introductory course

Level: beginners

Workload: 2x8h (2 days)

Introduction to R – overview of the RStudio interface and key functionalities
Data formats and variable types
Indexing techniques for efficient data handling
Data manipulation and preparation – fundamentals of dplyr
Automating repetitive tasks with loops
Writing and utilizing functions in R
Fundamentals of data visualization
Streamlining workflows with dplyr and ggplot2 – pipeline processing
Integrating AI (ChatGPT) in coding – practical applications, benefits, and limitation

Data visualisation in R - basic

Data visualisation - basics

Level: beginners

Workload: 2x8h (2 days)

Importing data from various formats (TXT, CSV, XLSX)
Data preparation for analysis using tidyr, dplyr, stringr, and forcats
Fundamentals of effective data visualization
Principles of research presentation – tailoring the format for clarity and impact
Introduction to ggplot2 – the Grammar of Graphics
Practical application of ggplot2 – hands-on project with expert guidance
Leveraging AI (ChatGPT) in coding – use cases, benefits, and potential challenges

Data visualisation in R - advanced

Data visualisation - advanced

Level: advanced

Workload: 2x8h (2 days)

Principles of effective research result visualization
Advanced data presentation techniques using dplyr and ggplot2
Visualization methods tailored to variable scales:
- Nominal scale
- Ordinal scale
- Interval and ratio scale
- Combining variables from different scales
Correlation analysis and data relationships
Heatmaps for pattern recognition and trend analysis
Introduction to mapping techniques
Leveraging AI (ChatGPT) in coding – applications, benefits, and challenges

Statistics with R

Level: beginner

Workload: 2x8h (2 days)

Data preparation techniques
Fundamentals of descriptive statistics
Data manipulation and pipeline processing with dplyr
Key topics in statistics and statistical inference using R:
- Group characteristics analysis
- Univariate data analysis
- Correlation and regression analysis
- Time series analysis
Effective result visualization with ggplot2
Utilizing AI (ChatGPT) in coding – applications, benefits, and potential challenges

Statistical modeling with R

Level: intermediatte (2 days) or advanced (3 days)

(at least basic knowledge of statistics and statistical inference tools required)

Workload: 2x8h (2 days) or 3x8h (3 days)

Fundamentals of statistical modeling
Data preparation for modeling
Model selection strategies – choosing the right approach and validation methods
Decision tree framework for optimal model selection
Regression analysis:
- Simple and multiple regression
- Logistic regression
Introduction to machine learning:
- Random Forest
- Artificial Neural Networks
Model performance evaluation:
- For continuous variables
- For discrete variables
Leveraging AI (ChatGPT) in coding – applications, benefits, and challenges

R on-demand course

R on-demand

Workload: depending on the course scope

Customize Your Course to Fit Your Needs

Design a course tailored to your specific requirements by selecting from our standard offerings or incorporating advanced topics beyond the core curriculum. Examples include modeling extreme phenomena using theoretical distributions or automating reporting processes.

The possibilities are virtually limitless.

Where devil can’t go, R rises to the challenge.

For complex cases, we welcome the opportunity to demonstrate our expertise.

Data analysis

Data analysis at the request of the Ordering Party

Workload: depending on the scope of the order

Example Scope of Analysis:

Data mining – trend identification and anomaly detection
Statistical analysis and forecasting
Statistical modeling and model performance evaluation
Customized charts and analytical reports tailored to business needs
Interactive data visualization dashboards using R Shiny