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Quick Facts

Medium Of InstructionsMode Of LearningMode Of Delivery
EnglishSelf StudyVideo 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 InformationsCertificate AvailabilityCertificate Providing Authority
USD 219yesHarvard University, Cambridge

 Inference and Modeling for High-throughput Experiments Course Fee Structure

Fees components

Amount

Certificate fees

$ 219


What you will learn

Analytical skillsStatistical skillsData science knowledge

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)

1: Is this course a beginner level course?

No, the course is taught at the intermediate level and would require learners to have basic knowledge of statistics and programming.

2: In how many weeks is the course covered?

The Statistical Inference and Modeling for High-throughput Experiments Course covers a total of 4 weeks time.

3: Will I be able to share the certificate I receive after the course?

Yes, students will receive a shareable certificate after completing the course.

4: How much time should a student spend as weekly study hours?

Each person who enrolls in the course is required to spend 2 to 4 hours per week studying.

5: Can I attend the Statistical Inference and Modeling for High-throughput Experiments Certification Course for free?

Yes, you have an option to choose the free version of the course wherein you will get limited access to online study materials.

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