Course Overview

Health Data Science and Analytics is a comprehensive professional training program designed to equip healthcare professionals, public health practitioners, researchers, epidemiologists, health informatics specialists, and data analysts with advanced skills in managing, analyzing, and interpreting health data to improve healthcare delivery and public health outcomes. As healthcare systems increasingly adopt Health Data Science, Healthcare Analytics, Health Informatics, Public Health Analytics, Big Data in Healthcare, Predictive Analytics, Artificial Intelligence in Healthcare, Clinical Data Analysis, Population Health Management, and Evidence-Based Healthcare, there is a growing demand for professionals who can transform complex health datasets into actionable insights. This course provides participants with practical expertise in health data management, statistical analysis, predictive modeling, and data-driven healthcare decision-making.

The training explores the complete health analytics lifecycle, including health data collection, integration, quality management, statistical analysis, predictive modeling, visualization, and reporting. Participants will learn how to analyze clinical, epidemiological, demographic, administrative, and health systems data to identify trends, evaluate interventions, optimize resource allocation, and improve patient outcomes. The course combines theoretical foundations with practical applications using real-world healthcare and public health datasets.

Participants will gain hands-on experience in electronic health records (EHR) analysis, disease surveillance, health information systems analytics, predictive healthcare modeling, population health assessment, healthcare quality improvement, and dashboard development. The course emphasizes the integration of data science methodologies, machine learning techniques, and health informatics principles to support clinical decision-making and health policy development. Through practical exercises and case studies, participants will develop confidence in applying advanced analytics to address healthcare challenges and improve service delivery.

The training further addresses emerging trends in digital health, including artificial intelligence in healthcare, precision medicine, telemedicine analytics, health data interoperability, wearable health technologies, real-time disease surveillance, cloud-based health systems, and health data governance. Participants will develop competencies required to design and implement health analytics solutions that enhance healthcare efficiency, patient care, disease prevention, and population health management.

Course Objectives

1. Understand the principles and applications of health data science and analytics.

2. Collect, manage, and analyze healthcare and public health datasets.

3. Apply statistical and epidemiological methods to health data.

4. Conduct health systems performance and service delivery analysis.

5. Utilize predictive analytics and machine learning in healthcare.

6. Analyze disease surveillance and population health data.

7. Develop dashboards and visualizations for health decision-making.

8. Ensure health data quality, security, and governance.

9. Support evidence-based healthcare planning and policy formulation.

10. Apply emerging digital health technologies and advanced analytics tools.

Organizational Benefits

1. Improved healthcare planning and decision-making.

2. Enhanced patient care quality and service delivery.

3. Better disease surveillance and outbreak response capabilities.

4. Increased efficiency in health resource allocation.

5. Improved monitoring and evaluation of health programs.

6. Enhanced healthcare performance measurement and reporting.

7. Strengthened public health research and policy development.

8. Improved health information system utilization.

9. Better compliance with health data governance and regulatory requirements.

10. Increased organizational capacity for digital health transformation.

Target Participants

· Public health professionals and epidemiologists

· Healthcare administrators and managers

· Health information officers and informatics specialists

· Medical researchers and biostatisticians

· Monitoring and Evaluation (M&E) specialists

· Clinical and hospital data managers

· Government health policy and planning officers

· NGO and development sector health professionals

· Data analysts and data scientists working in health

· Academic faculty and postgraduate students

· Health program managers and coordinators

· Anyone involved in healthcare analytics and health information systems

Course Outline

Module 1: Introduction to Health Data Science and Analytics

1. Fundamentals of health data science and healthcare analytics

2. Health information systems and data ecosystems

3. Types and sources of health data

4. Data-driven healthcare decision-making

5. Applications of analytics in healthcare and public health

6. Emerging trends in digital health

Case Study:
Developing a health analytics strategy to improve healthcare service delivery and performance.

Module 2: Health Data Collection and Management

1. Health data collection methodologies

2. Electronic Health Records (EHR) systems

3. Health Management Information Systems (HMIS)

4. Data quality assurance and validation

5. Data integration and interoperability standards

6. Health database management

Case Study:
Designing a centralized health data management system for healthcare facilities.

Module 3: Data Preparation and Exploratory Health Data Analysis

1. Data cleaning and preprocessing techniques

2. Handling missing and inconsistent health records

3. Exploratory data analysis methods

4. Descriptive health statistics

5. Trend and pattern identification

6. Health data visualization fundamentals

Case Study:
Analyzing patient admission and treatment records to identify healthcare utilization trends.

Module 4: Biostatistics and Epidemiological Analysis

1. Statistical foundations for health analytics

2. Measures of disease frequency and association

3. Hypothesis testing in health research

4. Regression analysis in healthcare

5. Epidemiological study designs

6. Interpretation of statistical findings

Case Study:
Investigating risk factors associated with chronic disease prevalence.

Module 5: Disease Surveillance and Public Health Analytics

1. Disease surveillance systems and indicators

2. Outbreak detection and monitoring

3. Public health data analytics techniques

4. Population health assessment methods

5. Geographic health analysis

6. Early warning systems and public health intelligence

Case Study:
Using surveillance data to monitor and respond to infectious disease outbreaks.

Module 6: Predictive Analytics and Machine Learning in Healthcare

1. Introduction to predictive healthcare analytics

2. Machine learning algorithms for health data

3. Risk prediction and patient stratification

4. Clinical decision support systems

5. Predictive disease modeling

6. Model evaluation and validation

Case Study:
Developing predictive models to identify high-risk patients for early intervention.

Module 7: Healthcare Quality and Performance Analytics

1. Healthcare quality measurement frameworks

2. Clinical performance indicators

3. Service delivery analytics

4. Hospital efficiency and utilization analysis

5. Patient outcomes measurement

6. Performance improvement strategies

Case Study:
Assessing healthcare facility performance using quality and efficiency indicators.

Module 8: Health Economics and Resource Allocation Analytics

1. Health economics fundamentals

2. Cost-effectiveness and cost-benefit analysis

3. Resource allocation and optimization

4. Healthcare financing analytics

5. Budget performance monitoring

6. Economic evaluation of health interventions

Case Study:
Evaluating the cost-effectiveness of a preventive healthcare program.

Module 9: Health Data Visualization and Dashboard Development

1. Principles of health data visualization

2. Dashboard design for healthcare monitoring

3. KPI development and performance tracking

4. Interactive reporting systems

5. Data storytelling for healthcare audiences

6. Executive decision-support dashboards

Case Study:
Developing a health system performance dashboard for policymakers and healthcare managers.

Module 10: Health Data Governance, Ethics, and Security

1. Health data governance frameworks

2. Data privacy and confidentiality principles

3. Health information security measures

4. Ethical considerations in health analytics

5. Regulatory compliance and standards

6. Data sharing and interoperability policies

Case Study:
Implementing secure health data governance practices within a healthcare organization.

Module 11: Digital Health Technologies and Advanced Analytics

1. Artificial intelligence in healthcare

2. Telemedicine and digital health analytics

3. Wearable devices and remote monitoring systems

4. Precision medicine and genomics analytics

5. Cloud-based health information systems

6. Real-time health monitoring technologies

Case Study:
Analyzing remote patient monitoring data to improve chronic disease management.

Module 12: Future Trends and Integrated Health Analytics Systems

1. Integrated health intelligence systems

2. Big data analytics in healthcare

3. Population health management strategies

4. Smart healthcare ecosystems

5. Emerging innovations in health data science

6. Strategic planning for digital health transformation

Case Study:
Designing an integrated health analytics ecosystem that combines health information systems, disease surveillance, predictive modeling, machine learning, population health analytics, healthcare performance monitoring, digital health technologies, data governance frameworks, and executive dashboards to improve patient outcomes, healthcare efficiency, public health preparedness, and evidence-based policy development.

Essential Information

Our courses are customizable to suit the specific needs of participants.
Participants are required to have proficiency in the English language.
Our training sessions feature comprehensive guidance through presentations, practical exercises, web-based tutorials, and collaborative group activities. Our facilitators boast extensive expertise, each with over a decade of experience.
Upon fulfilling the training requirements, participants will receive a prestigious Global King Project Management certificate.
Training sessions are conducted at various Global King Project Management Centers, including locations in Nairobi, Mombasa, Kigali, Dubai, Lagos, and others.
Organizations sending more than two participants from the same entity are eligible for a generous 20% discount.
The duration of our courses is adaptable, and the curriculum can be adjusted to accommodate any number of days.
To ensure seamless preparation, payment is expected before the commencement of training, facilitated through the Global King Project Management account.
For inquiries, reach out to us via email at training@globalkingprojectmanagement.org or by phone at +254 114 830 889.
Additional amenities such as tablets and laptops are available upon request for an extra fee. The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a certificate of successful completion. Participants are responsible for arranging and covering their travel expenses, including airport transfers, visa applications, dinners, health insurance, and any other personal expenses.

Health Data Science and Analytics Training Course