Clinical trial analysis
Analyse a public trial dataset with a logistic regression model.
All pathsDiscover Coursera PlusAfter step 3 After step 4 After step 5
Biology & health · 30Intermediate
Biostatistician
Statisticians in clinical research or genomics.
Biology basics, statistics in Python, linear models and GLMs, applied Bayesian analysis and genomic statistics.
324hours of coursework
33weeks at 10 h
5steps
19courses
4universities
Target skills
Statistical ModelingStatistical AnalysisStatisticsRegression AnalysisData AnalysisModel EvaluationPredictive ModelingStatistical Hypothesis TestingStatistical InferencePython ProgrammingData ScienceMolecular Biology
Skill labels as listed on Coursera.
Target jobs
- Biostatistician
- Clinical trial data manager
- Genomics statistician
Progression
- 1University of Colorado BoulderBeginner
Show the 4 courses
Course Level Length Rating (reviews) Biology Everywhere Foundations Beginner 5 h 4.5 (249) The Little Stuff: Energy, Cells, and Genetics Beginner 6 h 4.7 (127) The Big Stuff: Evolution and Ecology Beginner 4 h 4.6 (48) Specialty Topics: Biology Across Disciplines Beginner 7 h 4.4 (43) - 2University of MichiganBeginnerPython, Jupyter, Matplotlib, NumPy, pandas, Seaborn, Statsmodels
Stated prerequisites: High school algebra; basic Python recommended
Show the 3 courses
Course Level Length Rating (reviews) Understanding and Visualizing Data with Python Beginner 20 h 4.7 (2,741) Inferential Statistical Analysis with Python Intermediate 22 h 4.6 (938) Fitting Statistical Models to Data with Python Intermediate 15 h 4.4 (718) - 3University of Colorado BoulderIntermediateR, Jupyter Notebooksacademic credit
Stated prerequisites: Calculus, basic linear algebra and probability theory
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Course Level Length Rating (reviews) Modern Regression Analysis in R Intermediate 45 h 4.3 (40) ANOVA and Experimental Design Intermediate 40 h 4.0 (23) Generalized Linear Models and Nonparametric Regression Intermediate 42 h 4.2 (26) - 4University of PittsburghIntermediatePython, PyMC3
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Course Level Length Rating (reviews) Bayesian Inference Fundamentals Intermediate 20 h — Bayesian Regression and Model Selection Intermediate 20 h — Advanced Bayesian Methods and Applications Intermediate 22 h — - 5Johns Hopkins UniversityIntermediatePython, R, Bioconductor, Unix
Stated prerequisites: Some statistics, computer science and biology background (helpful, not required)
Show the 6 courses
Course Level Length Rating (reviews) Introduction to Genomic Technologies — 6 h 4.6 (4,901) Python for Genomic Data Science — 8 h 4.3 (1,830) Algorithms for DNA Sequencing Beginner 12 h 4.7 (938) Command Line Tools for Genomic Data Science — 12 h 4.0 (578) Bioconductor for Genomic Data Science — 9 h 3.8 (388) Statistics for Genomic Data Science — 9 h 4.2 (383)
With Coursera Plus
This path takes 324 hours, about 8 months at 10 hours a week. A Coursera Plus subscription gives access to most of its courses during that time.
Projects to build
Bayesian meta-analysis
Combine several published studies with a hierarchical Bayesian model.
Genomic statistics
Test thousands of genes while controlling the false discovery rate.
Free textbooks
OpenIntro StatisticsD. Diez, M. Çetinkaya-Rundel, C. BarrFree PDF
Bayesian Data Analysis, 3rd ed.A. Gelman et al.Free PDF, non-commercial use
Computational Genomics with RAltuna AkalinFree online, CC BY-NC-SA 4.0
Biology 2eOpenStaxFree, CC BY 4.0