Big Data, Genes, and Medicine
- Offered byCoursera
Big Data, Genes, and Medicine at Coursera Overview
Duration | 40 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
Big Data, Genes, and Medicine at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Advanced Level
- Approx. 40 hours to complete
- English Subtitles: French, Portuguese (European), Russian, English, Spanish
Big Data, Genes, and Medicine at Coursera Course details
- This course distills for you expert knowledge and skills mastered by professionals in Health Big Data Science and Bioinformatics. You will learn exciting facts about the human body biology and chemistry, genetics, and medicine that will be intertwined with the science of Big Data and skills to harness the avalanche of data openly available at your fingertips and which we are just starting to make sense of. We?ll investigate the different steps required to master Big Data analytics on real datasets, including Next Generation Sequencing data, in a healthcare and biological context, from preparing data for analysis to completing the analysis, interpreting the results, visualizing them, and sharing the results.
- Needless to say, when you master these high-demand skills, you will be well positioned to apply for or move to positions in biomedical data analytics and bioinformatics. No matter what your skill levels are in biomedical or technical areas, you will gain highly valuable new or sharpened skills that will make you stand-out as a professional and want to dive even deeper in biomedical Big Data. It is my hope that this course will spark your interest in the vast possibilities offered by publicly available Big Data to better understand, prevent, and treat diseases.
Big Data, Genes, and Medicine at Coursera Curriculum
Genes and Data
Introduction to the Course
Introduction to Module
DNA and Genes
RNA and Proteins
Transcription Process
Transcription Animation
Translation Process
Translation Animation
Data, Variables, and Big Datasets
Working with cBioPortal - Genetic Data Analysis
Working with cBioPortal - Gene Networks
Module 1 cBioPortal Data Analytics
Module 1 Resources
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
Preparing Datasets for Analysis
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
Jupyter Notebooks Essentials
Notebook Module 2 Tutorial
Module 2 R Data Preprocessing
Module 2 Resources
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
Finding Differentially Expressed Genes
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
Notebook Module 3 Tutorial
Jupyter Notebooks Essentials
Module 3 R Finding Differentially Expressed Genes
Module 3 Resources
Feature Selection Methods
Evaluation Schemes
Differentially Expressed Genes
Heatmaps
Module 3 Quiz
Module 3 R Finding Differentially Expressed Genes
Predicting Diseases from Genes
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
Jupyter Notebooks Essentials
Notebook Module 4 Tutorial
Module 4 R Predicting Diseases from Genes
Module 4 Resources
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
Determining Gene Alterations
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
Notebook Module 5 Tutorial
Jupyter Notebooks Essentials
Module 5 R Gene Alterations
Module 5 Resources
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
Clustering and Pathway Analysis
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
Jupyter Notebooks Essentials
Notebook Module 6 Tutorial
Module 6 R Clustering and Pathways
Module 6 Resources
Acknowledgements
Clustering
Clustering Methods
Pathways
Module 6 Quiz
Module 6 R Clustering and Pathways
Big Data, Genes, and Medicine at Coursera Admission Process
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