Biostatistics Certificate Program (http://www.statistics.com/biostatistics)
This Program is for you if
- You are a biomedical researcher and need to strengthen your basic statistics skills
- You work with clinical trials and need to know the statistical procedures that govern them
-You are a biotech researcher seeking more expertise in statistics
Hint: Check out our job boards page (http://www.statistics.com/jobs/job-boards/), conduct some job searches, find some jobs you are interested in, and see what skills are needed before you start your program of study. Statistics.com can help you acquire most of the analytics skills you need.
Program Content
The Biostatistics Certificate Program consists of fourteen, 4-week courses offered completely online at Statistics.com. There is a group of required topics, and a selection of electives. The workload for the entire program is the equivalent of 21 credits in the U.S. academic system. At the completion of the program you will have learned how to:
Design and analysis principles for randomized controlled trials
Design principles for observational (epidemiological) studies, and the potential problems with these studies
Concepts of inference relevant for biostatistics (ROC curves, odds rations, relative risk)
How to fit linear and logistic regression models, interpret output, and conduct diagnostics
How to model and evaluate survival (time-to-event) data
Methods to determine sample size for a study, and calculate power
Bayesian methods for analyzing data (electives)
Adaptive methods for designing clinical trials (elective)
Required Courses (7)
-Biostatistics 1 for Medical Science and Public Health
-Biostatistics 2 for Medical Science and Public Health
-Categorical Data Analysis
-Designing Valid Statistical Studies
-Regression Analysis
-Sample Size and Power Determination
-Survival Analysis
Elective Courses (choose 7)
-Adaptive Designs for Clinical Trials
-Advanced Logistic Regression
-Analysis and Sensitivity Analysis for Missing Data
-Bayesian Regression Modeling via MCMC Techniques
-Bayesian Statistics in R
-Bootstrap Methods
-Clinical Trials - Phamacokinetics and Bioequivalence
-Cluster Analysis
-Ecological and Environmental Sampling
-Epidemiologic Statistics
-Introduction to Bayesian Computing and Techniques
-Introduction to Bayesian Hierarchical and Multi-level Models
-Introduction to Bayesian Statistics
-Introduction to Resampling Methods
-Introduction to Statistical Issues in Clinical Trials
-Introduction to Statistical Modeling
-Logistic Regression
-Matrix Algebra Review-
-Maximum Likelihood Estimation
-Meta Analysis
-Meta Analysis 2
-Missing Data
-Modeling Count Data
-Multivariate Statistics
-Safety Monitoring Committees in Clinical Trials
-Sample Size and Power-Analysis for Cluster-Randomized and Multi-Site Studies
-Spatial Analysis Techniques in R
-Spatial Statistics with Geographic Information Systems
-Statistical Analysis of Microarray Data with R
-Survival Analysis
Details: http://www.statistics.com/biostatistics
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