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
Big Data, Genes, and Medicine Certification Programme brings carefully picked nuances of Bioinformatics and Health Big Data Science for enhancing the conceptual basics of candidates while providing them expertise and professionals skills in the concerning domain. This course will become an exciting experience for science enthusiasts who are fascinated by the mysteries of human body biology, its genetics and chemistry. These concepts will be related to medicine and Big Data to instil curiosity and consequent knowledge of Big Data in the learners.
Instructors will deal with important concepts related to Big Data analytics which will help them to gain a strong foothold over real datasets, not to forget Next Generation Sequencing data; all in a biological and healthcare context. The course covers a wide array of topics from preparing data for analysis, interpreting the results, visualising them, completing the analysis and ultimately sharing those results.
The curriculum is offered by the State University of New York, which has released this course as a part of its online training programme. Reputed as the largest comprehensive system of education in the US, it has proved itself to have a significant impact on the professional orientation of the learners.
The Highlights
- Advanced level of training
- Online and distance learning format
- Subtitled video lectures in different languages
- 40 hours of course duration
- Course offering by State University of New York (SUNY)
Programme Offerings
- Online Format
- practice assignments
- additional readings
- quizzes
- video classrooms.
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| INR 2435 | yes | Coursera |
The fees for the course Big Data, Genes, and Medicine is -
| Head | Amount in INR |
| Certificate fees | Rs. 2,435/- |
Eligibility Criteria
Certification Qualifying Details
Candidates pursuing the Big Data, Genes, and Medicine Certification Programme can purchase the certification after paying the fee for the same. It would be an online Certification and they shall receive the certificate on “My Accomplishments” tab only after completing the course in total and post staff and peer grading of the assignments submitted by them.
What you will learn
Big Data, Genes, and Medicine Certification shed light on many engaging concepts within the ambit of Health Informatics. The course is crafted to focus on the domain from an educational and professional point of view. After completion of the course, learners will gain information on-
- How to locate and download files for data analysis that involves genes and medicine.
- Evaluate the performance of feature selection methods.
- Quantify genomic alterations.
- Writing R scripts to classify and predict diseases from gene expressions.
- Analyse and visualise biological pathways.
Who it is for
Big Data, Genes, and Medicine Certification Programme have been designed for various professional roles and designations in the health and Big Data science industry. Pursuing this course is particularly beneficial for-
- Bioinformaticians
- Biomedical data analysts
- Big Data scientists
- Science enthusiasts
Admission Details
All applicants have to follow a very direct and simple process for registering for Big Data, Genes, and Medicine Certification Programme course on Coursera. They are advised to follow this step-by-step guide for a hassle-free enrollment-
Step 1: Visit the course page. https://www.coursera.org/learn/data-genes-medicine
Step 2: Candidates will be able to see an “Enroll for Free” tab on the top of the webpage, clicking on which would display a dialogue box. Candidates have the option of choosing between the free version and paid version of the course.
Step 3: In order for further process to effectuate, candidates should be signed up on Coursera.
Step 4: If the candidate chooses the paid version, he will have to pay the required course fee there and then to get access to all the course material. On selecting the free version, candidates will get access to all the course material except quizzes. They will have to tap on “Start Learning” to begin learning.
The Syllabus
Videos
- Introduction to Module
- Introduction to the Course
- Transcription Process
- Transcription Animation
- RNA and Proteins
- Translation Process
- DNA and Genes
- Data, Variables, and Big Datasets
- Translation Animation
- Working with cBioPortal - Genetic Data Analysis
Readings
- Module 1 cBioPortal Data Analytics
- Module 1 Resources
Assignments
- DNA, RNA, Genes, and Proteins
- Transcription and Translation Processes
- Data, Variables, and Big Datasets
- Working with cBioPortal
- Module 1 Quiz
- Module 1 cBioPortal Data Analytics
Discussion Prompt
- Module 1 Discussion
Videos
- Introduction to Module
- Datasets and Files
- Data Sources
- Importance of Data Preprocessing
- Data Preprocessing Tasks
- Replacing Missing Values
- Data Normalization
- Data Discretization
- Feature Selection
- Data Sampling
- Principles of R
- R Language
- Jupyter Notebooks 101
Readings
- Jupyter Notebooks Essentials
- Notebook Module 2 Tutorial
- Module 2 R Data Preprocessing
- Module 2 Resources
Assignments
- Datasets and Files
- Data Preprocessing Tasks
- Replacing Missing Values
- Normalization and Discretization
- Data Reduction
- Working with R
- Module 2 Quiz
- Module 2 R Data Preprocessing
Discussion Prompt
- Module 2 Discussion
Ungraded Labs
- Module 2 Notebook
- Module 2 Notebook
Videos
- Introduction to Module
- Overview of Feature Selection Methods
- Filter Methods
- Wrapper Methods
- Evaluation Schemes
- Selecting Differentially Expressed Genes
- Heatmaps
- R Scripts for Feature Selection
- Jupyter Notebooks 101
Readings
- Notebook Module 3 Tutorial
- Jupyter Notebooks Essentials
- Module 3 R Finding Differentially Expressed Genes
- Module 3 Resources
Assignments
- Feature Selection Methods
- Evaluation Schemes
- Differentially Expressed Genes
- Heatmaps
- Module 3 Quiz
- Module 3 R Finding Differentially Expressed Genes
Discussion Prompt
- Module 3 Discussion
Ungraded Labs
- Module 3 Notebook
- Module 3 Notebook
Videos
- Introduction to Module
- Overview of Classification and Prediction Methods
- Classification Methods Based on Analogy
- Classification Methods Based on Rules
- Classification Methods Based on Neural Networks
- Classification Methods Based on Statistics
- Classification Methods Based on Probabilities
- Prediction Methods
- Evaluation Schemes
- Prediction Workflow
- R Scripts for Prediction
- Jupyter Notebooks 101
Readings
- Jupyter Notebooks Essentials
- Notebook Module 4 Tutorial
- Module 4 R Predicting Diseases from Genes
- Module 4 Resources
Assignments
- Overview
- Classification with Analogy
- Classification based on Rules
- Classification with Neural Networks
- Classification based on Statistics
- Classification based on Probabilities
- Prediction Models
- Evaluation Schemes
- Module 4 Quiz
- Module 4 R Predicting Diseases from Genes
Discussion Prompt
- Module 4 Discussion
Ungraded Lab
- Module 4 Notebook
Videos
- Introduction to Module
- Overview of Gene Alterations
- Genetic Mutations
- Finding Genetic Mutations
- Methylation
- Copy Number Alterations
- Genomic Alterations and Gene Expressions
- R Scripts for Gene Alterations
- Jupyter Notebooks 101
Readings
- Notebook Module 5 Tutorial
- Jupyter Notebooks Essentials
- Module 5 R Gene Alterations
- Module 5 Resources
Assignments
- Gene Alterations
- Gene Mutations
- Methylation
- Copy Number Alterations
- Genomic Alterations and Gene Expressions
- Module 5 Quiz (Temporary)
- Module 5 Quiz
- Module 5 R Gene Alterations
Discussion Prompt
- Module 5 Discussion
Ungraded Lab
- Module 5 Notebook
Videos
- Introduction to Module
- Overview of Clustering Methods
- Similarity Assessment
- Clustering with KMeans
- Density Based Clustering
- Hierarchical Clustering
- Pathway Analysis
- Pathway Discovery
- Pathway Visualization
- R Scripts for Clustering and Pathway Analysis
- Jupyter Notebooks 101
- Concluding Remarks
Readings
- Jupyter Notebooks Essentials
- Notebook Module 6 Tutorial
- Module 6 R Clustering and Pathways
- Module 6 Resources
- Acknowledgements
Assignments
- Clustering
- Clustering Methods
- Pathways
- Module 6 Quiz
- Module 6 R Clustering and Pathways
Discussion Prompt
- Module 6 Discussion
Ungraded Lab
- Module 6 Notebook
Instructors
SUNY Frequently Asked Questions (FAQ's)
Coursera has a mobile application supported on both iOS and Android. For Android, candidates will have to ensure their OS version is Android 4.4 (Kit Kat) or higher.
To recover work, candidates can-
- Find their current notebook version by checking the top of the notebook window for the title
- Click the Coursera logo in their notebook view
- Find and click the name of their previous file
The following way is used for grading-
- Firstly, the peer checks the assignment and attributes a grade to it.
- Thereafter, the median score calculated for every assignment totals up to the final grade.
- Lastly, all the final grades are summed up to calculate the total grade for a particular assignment.
The learner’s age should be 13 years or older to take up this course on Coursera unless they belong to any country from the European Economic Area (EEA) where the limit differs.
Most features on Coursera are compatible with screen readers so visually impaired learners can make use of the same while pursuing the course.