Online Course – Certified Professional Internship in Public Health Epidemiology from Imperial College London

Learn to design and review epidemiological research. Develop skills that will enable you to critically evaluate studies in the field of epidemiological research.

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Professional Certificate

Beginners

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Clinical research design
  • bias
  • association
  • Causal inference
  • Filtering

What you will learn in the course

Courses for which the course is suitable

  • Epidemiologist
  • Public health researcher
  • Epidemiological data analyst
  • Health Policy Advisor
  • Clinical Research Manager
  • Disease prevention specialist
  • Medical causality researcher
  • Public health program developer
  • Course Guide in Epidemiology
  • Medical Research Analyst

Internship – a three-part course series

Thousands of new epidemiological studies are conducted every year, and their results can have a major impact on the way we live. Decisions about the food we eat, how much physical activity we do, where we live, and what treatment we seek if we feel unwell are all based on data from such studies. This specialization is designed to equip you with the skills to correctly interpret epidemiological research, consider its limitations, and design your own studies.

Specialization courses

  • First Course: Measuring Disease in Epidemiology

    Deals with the main tools used in epidemiology and how they can be relied upon in making decisions regarding public health policy, surveys, and disease prevention.

  • Second Course: Research Designs in Epidemiology

    Provides an overview of the most common research designs, their strengths and limitations.

  • Course Three: Validity and Bias in Epidemiology

    Builds on the fundamental principles learned in previous courses to discuss bias and confounding and how they may affect research results. It also provides the essential skills to prevent bias and confounding and think critically about causality.

Upon completion of this internship, you will develop the skills required to design and evaluate epidemiological research and be able to proceed to more advanced courses in epidemiology. Although this internship is part of the GMPH program, it can be taken independently.

Applied Learning Project

Through the tasks of this specialization, you will use the skills you have learned to calculate appropriate measures of frequency and association in a variety of study types. You will also practice identifying bias and confounding in epidemiological research and taking the necessary steps to control them. In doing so, you will understand which research findings are trustworthy and correctly interpret their applications in real life.

Details of the courses that make up the specialization

Measuring disease in epidemiology

  • Course 1
  • 10 hours
  • 4.7 (746 ratings)

Course Details

What you’ll learn
  • Calculate and interpret appropriate measures to describe disease frequency, associations, and attributable risk in different scenarios.
  • Calculate sensitivity, specificity, positive and negative predictive values, to interpret these values ​​in the context of screening.
Skills to Develop
  • Category: Prevalence
  • Category: Epidemiology
  • Category: Connections
  • Category: Morbidity

Research designs in epidemiology

  • Course 2
  • 8 hours
  • 4.8 (588 ratings)

Course Details

What you’ll learn
  • Compare and contrast different epidemiological research designs to describe their advantages and disadvantages.
Skills to Develop
  • Category: Randomized controlled trial
  • Category: Research designs
  • Category: Case-control study
  • Category: Cohort

Validity and biases in epidemiology

  • Course 3
  • 7 hours
  • 4.9 (236 ratings)

Course Details

What you’ll learn
  • Identify different types of biases that may occur in epidemiological studies, in order to seek strategies to reduce these biases.
Skills to Develop
  • Category: Selection bias
  • Category: Interaction (Statistics)
  • Category: Validity
  • Category: Intervening bias
  • Category: Information bias