Statistical Inference and Modeling for High-throughput Experiments
Learn about the common techniques used to perform statistical inference through the Statistical Inference and Modeling for High-throughput Experiments Course.
Intermediate
Online
4 WeeksQuick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
The Statistical Inference and Modeling for High-throughput Experiments Training Course is an open online course conducted in collaboration with the edX platform and Harvard University. The course deals with different techniques of statistical inference and their applications with regard to the handling of high-throughput data.
The Statistical Inference and Modeling for High-throughput Experiments Certification Course is taught over 4 weeks with the help of online study materials. The course is instructed by Rafael Irizarry and Michael Love, renowned professors at Harvard University. The online course is assessed through graded assignments and exams.
The Statistical Inference and Modeling for High-throughput Experiments Online Course is an intermediate-level course taught in a self-paced manner. The students who enrol in the course are required to have basic knowledge of programming, statistics and linear algebra. The students will receive professional certification after successful course completion if they join the course in the verified mode. For free, they can audit it for a limited duration.
The Highlights
- Shareable certificate upon completion
- Self-paced course
- 4 weeks duration
- 2 to 4 hours weekly study
- English medium
- Online course materials
- Graded assignments and exams
- Knowledge of data analysis
Programme Offerings
- Shareable Certificate upon completion
- Self-paced Course
- 4 Weeks Duration
- 2 to 4 Hours Weekly Study
- English medium
- Online Course Materials
- Graded Assignments and Exams
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| USD 219 | yes | Harvard University, Cambridge |
Inference and Modeling for High-throughput Experiments Course Fee Structure
Fees components | Amount |
Certificate fees | $ 219 |
What you will learn
After completing the Statistical Inference and Modeling for High-throughput Experiments Training Course, you will gain knowledge about the following topics:
- Organizing high throughput data
- Multiple comparison problem
- Family Wise Error Rates
- False Discovery Rate
- Self-paced course
- Error Rate Control procedures
- Bonferroni Correction
- q-values
- Statistical Modeling
- Hierarchical Models and the basics of Bayesian Statistics
- Exploratory Data Analysis for High throughput data
Admission Details
Follow the steps to enroll in the Statistical Inference and Modeling for High-throughput Experiments Online Course:
Step 1: Click on the URL given below -
https://www.edx.org/learn/statistics/harvard-university-statistical-inference-and-modeling-for-high-throughput-experiments
Step 2: Click on the enroll option in the course description.
Step 3: When the tab opens, create a new account by listing your name, email id and password.
Step 4: Once your account is created you can choose the course name and enroll.
The Syllabus
Instructors
Harvard University, Cambridge Frequently Asked Questions (FAQ's)
No, the course is taught at the intermediate level and would require learners to have basic knowledge of statistics and programming.
The Statistical Inference and Modeling for High-throughput Experiments Course covers a total of 4 weeks time.
Yes, students will receive a shareable certificate after completing the course.
Each person who enrolls in the course is required to spend 2 to 4 hours per week studying.
Yes, you have an option to choose the free version of the course wherein you will get limited access to online study materials.