Biostatistics in Public Health

Offered By: Johns Hopkins University via Coursera

Course Description

Overview

This specialization is intended for public health and healthcare professionals, researchers, data analysts, social workers, and others who need a comprehensive concepts-centric biostatistics primer. Those who complete the specialization will be able to read and respond to the scientific literature, including the Methods and Results sections, in public health, medicine, biological science, and related fields. Successful learners will also be prepared to participate as part of a research team.

Syllabus

Course 1: Summary Statistics in Public Health
- Offered by Johns Hopkins University. Biostatistics is the application of statistical reasoning to the life sciences, and it is the key to ... Enroll for free.

Course 2: Hypothesis Testing in Public Health
- Offered by Johns Hopkins University. Biostatistics is an essential skill for every public health researcher because it provides a set of ... Enroll for free.

Course 3: Simple Regression Analysis in Public Health
- Offered by Johns Hopkins University. Biostatistics is the application of statistical reasoning to the life sciences, and it's the key to ... Enroll for free.

Course 4: Multiple Regression Analysis in Public Health
- Offered by Johns Hopkins University. Biostatistics is the application of statistical reasoning to the life sciences, and it's the key to ... Enroll for free.

Courses

1 review
19 hours 13 minutes
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Biostatistics is an essential skill for every public health researcher because it provides a set of precise methods for extracting meaningful conclusions from data. In this second course of the Biostatistics in Public Health Specialization, you'll learn to evaluate sample variability and apply statistical hypothesis testing methods. Along the way, you'll perform calculations and interpret real-world data from the published scientific literature. Topics include sample statistics, the central limit theorem, confidence intervals, hypothesis testing, and p values.
1 review
15 hours 1 minute
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Biostatistics is the application of statistical reasoning to the life sciences, and it is the key to unlocking the data gathered by researchers and the evidence presented in the scientific literature. In this course, we'll focus on the use of statistical measurement methods within the world of public health research. Along the way, you'll be introduced to a variety of methods and measures, and you'll practice interpreting data and performing calculations on real data from published studies. Topics include summary measures, visual displays, continuous data, sample size, the normal distribution, binary data, the element of time, and the Kaplan-Meir curve.
1 review
13 hours 56 minutes
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Biostatistics is the application of statistical reasoning to the life sciences, and it's the key to unlocking the data gathered by researchers and the evidence presented in the scientific public health literature. In this course, we'll focus on the use of simple regression methods to determine the relationship between an outcome of interest and a single predictor via a linear equation. Along the way, you'll be introduced to a variety of methods, and you'll practice interpreting data and performing calculations on real data from published studies. Topics include logistic regression, confidence intervals, p-values, Cox regression, confounding, adjustment, and effect modification.
1 review
14 hours
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Biostatistics is the application of statistical reasoning to the life sciences, and it's the key to unlocking the data gathered by researchers and the evidence presented in the scientific public health literature. In this course, you'll extend simple regression to the prediction of a single outcome of interest on the basis of multiple variables. Along the way, you'll be introduced to a variety of methods, and you'll practice interpreting data and performing calculations on real data from published studies. Topics include multiple logistic regression, the Spline approach, confidence intervals, p-values, multiple Cox regression, adjustment, and effect modification.

Taught by

John McGready, PhD, MS