Course Summary
Learn how genetics and genomics are transforming medicine and healthcare in this data-driven course.Key Learning Points
- Explore the latest advancements in genomic research and how they are being used to treat diseases.
- Learn about the ethical considerations surrounding genetic testing and personalized medicine.
- Gain practical skills in data analysis and visualization using real-world genomic datasets.
Related Topics for further study
Learning Outcomes
- Understand the role of genetics and genomics in modern medicine
- Develop practical skills in data analysis and visualization
- Evaluate the ethical considerations surrounding genetic testing and personalized medicine
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of biology and genetics
- Familiarity with programming languages such as Python
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
- Quizzes and assignments
Similar Courses
- Bioinformatics: Introduction and Methods
- Introduction to Personalized Medicine
Related Education Paths
Notable People in This Field
- Director of the Scripps Translational Science Institute
- Director of the National Institutes of Health
Related Books
Description
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.
Outline
- 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
Summary of User Reviews
Discover the fascinating link between data science, genetics, and medicine with the Data, Genes and Medicine course on Coursera. Users have praised the course for its engaging content and practical applications in the field of medicine.Key Aspect Users Liked About This Course
Engaging content and practical applicationsPros from User Reviews
- Great introduction to data science in medicine
- Well-structured and easy to follow
- Real-world examples and case studies
- Lots of opportunities for hands-on learning
- Expert instructors with a wealth of knowledge
Cons from User Reviews
- Some technical concepts may be difficult for beginners
- Limited interaction with instructors
- Lack of depth in certain topics
- Not enough emphasis on statistical analysis
- Some assignments and quizzes were too easy
Isabelle Bichindaritz
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