Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
The Principles, Statistical and Computational Tools for Reproducible Data Science course has been meticulously created keeping in mind the wide usage and huge importance of reproducible research across various disciplines. The Principles, Statistical and Computational Tools for Reproducible Data Science certification thoroughly covers all the fundamentals of reproducible science in a very lucid manner and adds on to various useful computational tools, thus appealing to a very wider audience in various fields like data science, bioinformatics, biostatistics, computational biology etc.
The 8-week self-paced Principles, Statistical and Computational Tools for Reproducible Data Science classes are the perfect blend of video lectures, case-based studies and computational tools and can act as an asset to the knowledge of reproducible science of the candidates. Various intricate nuances such as data provenance have also been thoroughly explained in the course material. The best practices prevalent in pattern designing are also shared with the candidates. The Principles, Statistical, and Computational Tools for Reproducible Data Science online course encourages the candidates for peer-to-peer engagement for a great network and also acts as a place for intellectual and intense discussions on the topic.
There are two pathways in which enrolling in this course of edX is possible. One is the verified track, and the other is the audit mode. During the audit mode, the candidates shall have limited time access to the course, but access becomes unlimited during the verified track.
The Highlights
- Free access
- Certification by edX
- Usage of computational tools
- Case-based studies
- Intermediate level course
- In association with Harvard University
- 3-8 hours of weekly effort required
- Extensive course content extending up to 8 weeks
Programme Offerings
- video lectures
- Handouts
- Projects
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| INR 12432 | yes | Harvard University, Cambridge |
The fee structure for the Principles, Statistical and Computational Tools for Reproducible Data Science by edX course is:
- The fees for registration are free.
- edX certification for the same course is priced at ₹ 12,432 for all the appearing students.
Principles, Statistical and Computational Tools for Reproducible Data Science fees Structure
Fee category | Amount in INR |
Registration | Free |
Certification fee | ₹ 12,432 |
Eligibility Criteria
Certification Qualifying Details
edX certification shall be provided to the candidates applying for the programme and are opting the ‘verified track’ and are able to maintain at least 50% score in the assessment test conducted after the completion of the course.
What you will learn
With the Principles, Statistical and Computational Tools for Reproducible Data Science programme-
- Candidates will get to learn the fundamentals of computational biology.
- Course takers will get to develop their own environment for carrying out reproducible research.
- Candidates will be able to thoroughly understand the fundamentals of reproducible research in the course.
- Candidates will understand various statistical methods required for reproducible data analysis.
- The course benefits the takers, as they will develop new methods for reproducible research and reporting.
- Candidates will be able to identify key elements for data provenance in the Principles, Statistical and Computational Tools for Reproducible Data Science syllabus.
- Learners will understand how to write their own reproducible paper.
- Candidates will get first-hand experience on various computational tools and version control systems.
- Course takers will understand the funnel of reproducible dynamic report generation methods.
- Candidates will explore various workflows for reproducible research in the Principles, Statistical and Computational Tools for Reproducible Data Science certification.
Who it is for
The course is ideal for:
- Any individual interested in computational biology.
- Data scientists who want to learn about reproducible science.
Admission Details
The overall admission procedure for the course is pretty simple. Only the candidates who are opting the ‘verified track’ are required to keep their billing details and payment method near and ready for usage.
Candidates may follow these steps for getting registered:
Step 1: Candidates are firstly required to visit the overview section of the course: https://www.edx.org/learn/data-science/harvard-university-principles-statistical-and-computational-tools-for-reproducible-data-science
Step 2: Candidates are then required to login/register themselves on the edX website.
Step 3: After logging in, candidates are required to click on ‘Enroll’.
Step 4: Candidates then need to choose from the ‘audit access’ & ‘verified track’ option available in the Principles, Statistical and Computational Tools for Reproducible Data Science training.
Step 5: The course shall be available to the candidates right after the payment.
The Syllabus
- Definitions and Concepts
- Factors affecting reproducibility
- Project Design
- Journal Requirements
- Repositories
- Privacy and Security
- R and Rstudio
- Python, Git, and GitHub
- Creating a repository
- Data sources
- Dynamic report generation
- Workflows
- Prediction Models
- Coefficient of determination
- Brier score
- Area Under the Curve (AUC)
- Concordance in survival analysis
- Cross-validation
- Bootstrap
- Simulations
- Clustering
Instructors
Harvard University, Cambridge Frequently Asked Questions (FAQ's)
A laptop that can smoothly run computational tools taught in the course, shall act as an inflection point in the learning curve.
The online Principles, Statistical and Computational Tools for Reproducible Data Science certification has been curated by experienced faculties of Harvard T.H. Chan school of Public Health.
This course is taught by four of the highly experienced faculties of Harvard T.H. School of Public Health.
Yes, this is a self-paced course, so that candidates can study at their convenience during the Principles, Statistical and Computational Tools for Reproducible Data Science training.
An intense study of 3-8 hours per week is highly recommended for the course.
The computational tools that are required for reproducible data analysis and version control systems (Git/GitHub, Emacs/Spyder/RStudio).
Reproducible dynamic report generation tools (R Markdown/Jupyter/Pandoc/R Notebook) shall be talked about during the Principles, Statistical and Computational Tools for Reproducible Data Science certification course.
Unfortunately, candidates from Iran, Cuba, and the Crimea region of Ukraine will not be able to take this course.
‘audit access’ is provided only for a decisive period of time. So, the candidates are required to keep an eye on the course schedule/date section.
The extensive Principles, Statistical and Computational Tools for Reproducible Data Science certification benefit materials swiftly extends for up to 8 weeks.