Harvard University - Statistical Inference and Modeling for High-throughput Experiments
- Offered byedX
Statistical Inference and Modeling for High-throughput Experiments at edX Overview
Duration | 15 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Schedule type | Self paced |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Statistical Inference and Modeling for High-throughput Experiments at edX Highlights
- Reset deadlines in accordance to your schedule.
- Instructors -
- Rafael Irizarry & Michael Love
- Effort - 2?4 hours per week
- FREE
- Add a Verified Certificate for ?11,116
Statistical Inference and Modeling for High-throughput Experiments at edX Course details
- This course is designed for those who want to learn about Statistical Inference and Modeling
- In this course participants learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis
- Introduce statistical modeling and how it is applied to high-throughput data
- In particular, we will discuss parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation
- Provide several examples of how these concepts are applied in next generation sequencing and microarray data
- Finally, we will discuss hierarchical models and empirical bayes along with some examples of how these are used in practice
Statistical Inference and Modeling for High-throughput Experiments at edX Curriculum
Understand a series of concepts, thought patterns, analysis paradigms, and computational and statistical tools, that together support data science and reproducible research
Fundamentals of reproducible science using case studies that illustrate various practices
Key elements for ensuring data provenance and reproducible experimental design
Statistical methods for reproducible data analysis
Computational tools for reproducible data analysis and version control (Git/GitHub, Emacs/RStudio/Spyder), reproducible data (Data repositories/Dataverse) and reproducible dynamic report generation (Rmarkdown/R Notebook/Jupyter/Pandoc), and workflows
How to develop new methods and tools for reproducible research and reporting
How to write your own reproducible paper
Statistical Inference and Modeling for High-throughput Experiments at edX Entry Requirements
- PH525.1x and PH525.2x or basic programming, intro to statistics, intro to linear algebra
- Not mentioned
Statistical Inference and Modeling for High-throughput Experiments at edX Admission Process
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