R and Python are widely used programming languages in health research for data preparation, statistical analysis, visualization, and machine learning. Their broad analytical capabilities and flexible structure offer important advantages in both academic research and institutional data analytics.
Data4Care’s Health Data Analysis with R and Python training offers a hands-on learning experience so participants can analyze health data in a programming environment.
The training focuses on reading, cleaning, transforming, analyzing, and visualizing data through examples from health research.
Training Content
1. Introduction to R and Python Environments
- Using R and Python in health research
- RStudio and Python working environments
- Basic programming concepts
- Data structures
- Importing data files
2. Data Preparation and Cleaning
- Working with Excel and CSV files
- Reviewing missing data
- Data cleaning and transformation
- Creating variables
- Merging datasets
- Data quality checks
3. Statistical Analyses
- Descriptive statistics
- Group comparisons
- Correlation analysis
- Introduction to regression models
- Common statistical analyses in health research
4. Data Visualization
- Distribution and comparison plots
- Box plots and histograms
- Relationship plots
- Publication-ready figures
- Formatting visual outputs
5. Automating Analysis Workflows
- Reproducible analysis workflows
- Organizing analysis steps with functions
- Basics of working with larger datasets
- Exporting analysis outputs
- Code and analysis documentation
6. Introduction to Advanced Analyses
- Data preparation for machine learning
- Basic classification and prediction examples
- Combining statistical and programming-based approaches
Who Can Attend?
Health sciences researchers, academics, graduate students, clinical researchers, laboratory specialists, and professionals working with health data.
Our Training Approach
The training is practice-oriented. Participants perform data preparation, analysis, and visualization on sample health datasets.
The program can be organized into beginner and advanced modules by participant level. Separate R- or Python-focused options can also be offered.
After the Training
Participants should be able to organize health data in a programming environment, run core analyses, prepare suitable visuals, and understand the basics of reproducible analysis workflows.
Want to learn how to analyze health data with R or Python?
Contact us for details on the training scope and practice level.