Module 1: Introduction to Disease Outbreak Data Analytics

• Overview of disease outbreak data
• Importance of data analytics in public health
• Basic principles of data management
• Introduction to R and Power BI
• Practical session: Setting up R and Power BI environments

Module 2: Data Collection and Management

• Methods of data collection in disease outbreaks
• Data cleaning and preprocessing
• Handling missing data
• Data storage and management practices
• Practical session: Data cleaning and preprocessing in R

Module 3: Data Visualization with R

• Principles of data visualization
• Creating visualizations with ggplot2
• Customizing plots and visualizations
• Visualizing temporal and spatial data
• Practical session: Creating visualizations using R

Module 4: Data Visualization with Power BI

• Introduction to Power BI interface
• Importing data into Power BI
• Creating interactive dashboards
• Customizing visuals and reports
• Practical session: Building interactive dashboards in Power BI

Module 5: Statistical Analysis in R

• Descriptive statistics and exploratory data analysis
• Hypothesis testing and confidence intervals
• Regression analysis
• Time series analysis
• Practical session: Conducting statistical analysis in R

Module 6: Geospatial Analysis in R

• Introduction to geospatial data
• Mapping disease outbreaks
• Spatial data visualization
• Analyzing spatial patterns
• Practical session: Geospatial analysis in R

Module 7: Advanced Data Visualization with Power BI

• Advanced visualization techniques
• Custom visualizations and scripting
• Integrating R scripts in Power BI
• Performance optimization for dashboards
• Practical session: Advanced visualizations in Power BI

Module 8: Predictive Modeling in R

• Introduction to predictive modeling
• Building and validating models
• Machine learning techniques
• Evaluating model performance
• Practical session: Developing predictive models in R

Module 9: Data Reporting and Communication

• Principles of effective data reporting
• Creating compelling reports
• Communicating data insights to stakeholders
• Customizing reports for different audiences
• Practical session: Creating comprehensive reports

Module 10: Real-World Case Studies

• Analyzing historical disease outbreak data
• Applying learned techniques to real-world scenarios
• Group discussions and collaborative analysis
• Presenting findings and recommendations
• Practical session: Case study analysis

Module 11: Data Ethics and Privacy

• Ethical considerations in data analysis
• Ensuring data privacy and security
• Compliance with data protection regulations
• Handling sensitive health data
• Practical session: Implementing data privacy measures

Module 12: Emerging Technologies in Data Analytics

• Overview of emerging technologies
• Big data and its impact on public health
• Artificial intelligence and machine learning
• Future trends in data analytics
• Practical session: Exploring emerging technologies

Module 13: Data Integration and Interoperability

• Integrating data from multiple sources
• Interoperability standards and protocols
• Data exchange and collaboration
• Managing data quality and consistency
• Practical session: Data integration techniques

Module 14: Advanced Statistical Methods

• Advanced hypothesis testing
• Multivariate analysis
• Survival analysis
• Bayesian statistics
• Practical session: Applying advanced statistical methods

Module 15: Data-Driven Decision Making

• Frameworks for data-driven decision making
• Decision support systems
• Risk assessment and management
• Scenario analysis
• Practical session: Decision-making simulations

Module 16: Crisis Management and Response

• Principles of crisis management
• Data-driven response strategies
• Communication during crises
• Evaluating response effectiveness
• Practical session: Crisis response simulation

Module 17: Public Health Surveillance

• Concepts of public health surveillance
• Surveillance system design and implementation
• Data collection and analysis for surveillance
• Reporting and disseminating surveillance data
• Practical session: Designing a surveillance system

Module 18: Evaluating Public Health Programs

• Evaluation frameworks and methodologies
• Measuring program effectiveness
• Data collection and analysis for evaluation
• Reporting evaluation findings
• Practical session: Program evaluation techniques

Trainer Experience

Our trainers are seasoned professionals with extensive experience in public health, data analytics, and the use of R and Power BI. They bring a wealth of practical knowledge and real-world insights to the training.

Quality statement

We are committed to providing high-quality training that meets the needs of our participants. Our courses are designed to be interactive, engaging, and relevant to real-world challenges.

Tailor-made courses

We offer tailor-made courses to suit the specific needs of organizations. Please contact us to discuss your requirements and we will design a course that meets your needs.

Payment

Payment is required a week before the training starts.

Accommodation and airport pick-up

We provide accommodation and airport pick-up services for participants. Please contact us for more details.