In Data Science for Health Research, learn to organize and visualize health data using statistical analysis in programs like R. Explore how to translate data, interpret statistical models, and predict outcomes to help make data-informed decisions within the public health field.
Overview
Syllabus
Course 1: Arranging and Visualizing Data in R
- Offered by University of Michigan. This course provides a first look at the R statistical environment. Beginning with step-by-step ... Enroll for free.
Course 2: Linear Regression Modeling for Health Data
- Offered by University of Michigan. This course provides learners with a first look at the world of statistical modeling. It begins with a ... Enroll for free.
Course 3: Logistic Regression and Prediction for Health Data
- Offered by University of Michigan. This course introduces learners to the analysis of binary/dichotomous outcomes. Learners will become ... Enroll for free.
- Offered by University of Michigan. This course provides a first look at the R statistical environment. Beginning with step-by-step ... Enroll for free.
Course 2: Linear Regression Modeling for Health Data
- Offered by University of Michigan. This course provides learners with a first look at the world of statistical modeling. It begins with a ... Enroll for free.
Course 3: Logistic Regression and Prediction for Health Data
- Offered by University of Michigan. This course introduces learners to the analysis of binary/dichotomous outcomes. Learners will become ... Enroll for free.
Courses
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This course provides a first look at the R statistical environment. Beginning with step-by-step instructions on downloading and installing the software, learners will first practice navigating R and its companion, RStudio. Then, they will read data into the R environment and prepare it for summary and analysis. A wide variety of concepts will be covered, including sorting rows of data, grouping by variables, summarizing over variables, pivoting, and creating new variables. Then, learners will visualize their data, creating publication-ready plots with relatively little effort. Finally, learners will understand how to set up a project workflow for their own analyses. All concepts taught in this course will be covered with multiple modalities: slide-based lectures, guided coding practice with the instructor, and independent but structured practice.
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This course introduces learners to the analysis of binary/dichotomous outcomes. Learners will become familiar with fundamental tests for two-group comparisons and statistical inference plus prediction more broadly using logistic regression. They will understand the connection between prevalence, risk ratios, and odds ratios. By the end of this course, learners will be able to understand how binary outcomes arise, how to use R to compare proportions between two groups, how to fit logistic regressions in R, how to make predictions using logistic regression, and how to assess the quality of these predictions. All concepts taught in this course will be covered with multiple modalities: slide-based lectures, guided coding practice with the instructor, and independent but structured exercises.
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This course provides learners with a first look at the world of statistical modeling. It begins with a high-level overview of different philosophies on the question of 'what is a statistical model' and introduces learners to the core ideas of traditional statistical inference and reasoning. Learners will get their first look at the ever-popular t-test and delve further into linear regression. They will also learn how to fit and interpret regression models for a continuous outcome with multiple predictors. All concepts taught in this course will be covered with multiple modalities: slide-based lectures, guided coding practice with the instructor, and independent but structured exercises.
Taught by
Bhramar Mukherjee and Philip S. Boonstra
