Big Data, Genes, and Medicine

Product type
Logo Coursera (CC)
Provider rating: starstarstarstar_halfstar_border 6.6 Coursera (CC) has an average rating of 6.6 (out of 5 reviews)

Need more information? Get more details on the site of the provider.

Description

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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, vi…

Read the complete description

Frequently asked questions

There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.

Didn't find what you were looking for? See also: Medicine, Travel, Tourism & Hospitality, Infection Control, Advanced Practice, and Acute Care.

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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.

Who is this class for: This course is primarily aimed at health care professionals or assistants, and those with a BS/MA/MS in science or technology or equivalent professional experience. Minimum technical skills are a good understanding of using an Excel spreadsheet. Additional prerequisite knowledge in basic statistics would be preferred, however additional resources will be made available to learners to acquire this knowledge. I think that anyone interested in getting insights into how to harness Big Data to better understand, prevent, and treat diseases can take this course because the material can be applied at different levels of expertise.

Created by:  The State University of New York
  • Taught by:  Isabelle Bichindaritz, Associate Professor

    Computer Science
Level Advanced Commitment 6 weeks of study, 3-5 hours per week Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.1 stars Average User Rating 4.1See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

Help from your peers

Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

Certificates

Earn official recognition for your work, and share your success with friends, colleagues, and employers.

The State University of New York The State University of New York, with 64 unique institutions, is the largest comprehensive system of higher education in the United States. Educating nearly 468,000 students in more than 7,500 degree and certificate programs both on campus and online, SUNY has nearly 3 million alumni around the globe.

Syllabus


WEEK 1


Genes and Data



After this module, you will be able to 1. Locate and download files for data analysis involving genes and medicine. 2. Open files and preprocess data using R language. 3. Write R scripts to replace missing values, normalize data, discretize data, and sample data.


11 videos, 2 readings, 4 practice quizzes expand


  1. Video: Introduction to the Course
  2. Video: Introduction to Module
  3. Video: DNA and Genes
  4. Video: RNA and Proteins
  5. Practice Quiz: DNA, RNA, Genes, and Proteins
  6. Video: Transcription Process
  7. Video: Transcription Animation
  8. Video: Translation Process
  9. Video: Translation Animation
  10. Practice Quiz: Transcription and Translation Processes
  11. Video: Data, Variables, and Big Datasets
  12. Practice Quiz: Data, Variables, and Big Datasets
  13. Video: Working with cBioPortal - Genetic Data Analysis
  14. Video: Working with cBioPortal - Gene Networks
  15. Practice Quiz: Working with cBioPortal
  16. Discussion Prompt: Module 1 Discussion
  17. Reading: Module 1 cBioPortal Data Analytics
  18. Reading: Module 1 Resources

Graded: Module 1 Quiz
Graded: Module 1 cBioPortal Data Analytics

WEEK 2


Preparing Datasets for Analysis



After this module, you will be able to: 1. Locate and download files for data analysis involving genes and medicine. 2. Open files and preprocess data using R language. 3. Write R scripts to replace missing values, normalize data, discretize data, and sample data.


13 videos, 4 readings, 6 practice quizzes expand


  1. Video: Introduction to Module
  2. Video: Datasets and Files
  3. Video: Data Sources
  4. Practice Quiz: Datasets and Files
  5. Video: Importance of Data Preprocessing
  6. Video: Data Preprocessing Tasks
  7. Practice Quiz: Data Preprocessing Tasks
  8. Video: Replacing Missing Values
  9. Practice Quiz: Replacing Missing Values
  10. Video: Data Normalization
  11. Video: Data Discretization
  12. Practice Quiz: Normalization and Discretization
  13. Video: Feature Selection
  14. Video: Data Sampling
  15. Practice Quiz: Data Reduction
  16. Video: Principles of R
  17. Video: R Language
  18. Practice Quiz: Working with R
  19. Notebook: Module 2 Notebook
  20. Video: Jupyter Notebooks 101
  21. Reading: Jupyter Notebooks Essentials
  22. Reading: Notebook Module 2 Tutorial
  23. Discussion Prompt: Module 2 Discussion
  24. Reading: Module 2 R Data Preprocessing
  25. Notebook: Module 2 Notebook
  26. Reading: Module 2 Resources

Graded: Module 2 Quiz
Graded: Module 2 R Data Preprocessing

WEEK 3


Finding Differentially Expressed Genes
After this module, you will be able to 1. Select features from highly dimensional datasets. 2. Evaluate the performance of feature selection methods. 3. Write R scripts to select features from datasets involving gene expressions.


9 videos, 4 readings, 4 practice quizzes expand


  1. Video: Introduction to Module
  2. Video: Overview of Feature Selection Methods
  3. Video: Filter Methods
  4. Video: Wrapper Methods
  5. Practice Quiz: Feature Selection Methods
  6. Video: Evaluation Schemes
  7. Practice Quiz: Evaluation Schemes
  8. Video: Selecting Differentially Expressed Genes
  9. Practice Quiz: Differentially Expressed Genes
  10. Video: Heatmaps
  11. Practice Quiz: Heatmaps
  12. Video: R Scripts for Feature Selection
  13. Notebook: Module 3 Notebook
  14. Reading: Notebook Module 3 Tutorial
  15. Reading: Jupyter Notebooks Essentials
  16. Video: Jupyter Notebooks 101
  17. Discussion Prompt: Module 3 Discussion
  18. Reading: Module 3 R Finding Differentially Expressed Genes
  19. Notebook: Module 3 Notebook
  20. Reading: Module 3 Resources

Graded: Module 3 Quiz
Graded: Module 3 R Finding Differentially Expressed Genes

WEEK 4


Predicting Diseases from Genes
After this module, you will be able to 1. Build classification and prediction models. 2. Evaluate the performance of classification and prediction methods. 3. Write R scripts to classify and predict diseases from gene expressions.


12 videos, 4 readings, 8 practice quizzes expand


  1. Video: Introduction to Module
  2. Video: Overview of Classification and Prediction Methods
  3. Practice Quiz: Overview
  4. Video: Classification Methods Based on Analogy
  5. Practice Quiz: Classification with Analogy
  6. Video: Classification Methods Based on Rules
  7. Practice Quiz: Classification based on Rules
  8. Video: Classification Methods Based on Neural Networks
  9. Practice Quiz: Classification with Neural Networks
  10. Video: Classification Methods Based on Statistics
  11. Practice Quiz: Classification based on Statistics
  12. Video: Classification Methods Based on Probabilities
  13. Practice Quiz: Classification based on Probabilities
  14. Video: Prediction Methods
  15. Practice Quiz: Prediction Models
  16. Video: Evaluation Schemes
  17. Practice Quiz: Evaluation Schemes
  18. Video: Prediction Workflow
  19. Video: R Scripts for Prediction
  20. Notebook: Module 4 Notebook
  21. Reading: Jupyter Notebooks Essentials
  22. Video: Jupyter Notebooks 101
  23. Reading: Notebook Module 4 Tutorial
  24. Discussion Prompt: Module 4 Discussion
  25. Reading: Module 4 R Predicting Diseases from Genes
  26. Reading: Module 4 Resources

Graded: Module 4 Quiz
Graded: Module 4 R Predicting Diseases from Genes

WEEK 5


Determining Gene Alterations



After this module, you will be able to 1. List different types of gene alterations. 2. Compare and contrast methods for detecting gene mutations. 3. Compare and contrast methods for detecting methylation. 4. Compare and contrast methods for detecting copy number variations. 5. Quantify genomic alterations. 6. Connect genomic alterations to differential expression of genes. 7. Write programs in R for determining gene alterations and their relationship with gene expression.


9 videos, 4 readings, 6 practice quizzes expand


  1. Video: Introduction to Module
  2. Video: Overview of Gene Alterations
  3. Practice Quiz: Gene Alterations
  4. Video: Genetic Mutations
  5. Video: Finding Genetic Mutations
  6. Practice Quiz: Gene Mutations
  7. Video: Methylation
  8. Practice Quiz: Methylation
  9. Video: Copy Number Alterations
  10. Practice Quiz: Copy Number Alterations
  11. Video: Genomic Alterations and Gene Expressions
  12. Practice Quiz: Genomic Alterations and Gene Expressions
  13. Video: R Scripts for Gene Alterations
  14. Notebook: Module 5 Notebook
  15. Video: Jupyter Notebooks 101
  16. Reading: Notebook Module 5 Tutorial
  17. Reading: Jupyter Notebooks Essentials
  18. Discussion Prompt: Module 5 Discussion
  19. Practice Quiz: Module 5 Quiz (Temporary)
  20. Reading: Module 5 R Gene Alterations
  21. Reading: Module 5 Resources

Graded: Module 5 Quiz
Graded: Module 5 R Gene Alterations

WEEK 6


Clustering and Pathway Analysis
After this module, you will be able to 1. Find clusters in biomedical data involving genes.2. Analyze and visualize biological pathways. 3. Write R scripts for clustering and for pathway analysis.


12 videos, 5 readings, 3 practice quizzes expand


  1. Video: Introduction to Module
  2. Video: Overview of Clustering Methods
  3. Video: Similarity Assessment
  4. Practice Quiz: Clustering
  5. Video: Clustering with KMeans
  6. Video: Density Based Clustering
  7. Video: Hierarchical Clustering
  8. Practice Quiz: Clustering Methods
  9. Video: Pathway Analysis
  10. Video: Pathway Discovery
  11. Video: Pathway Visualization
  12. Practice Quiz: Pathways
  13. Video: R Scripts for Clustering and Pathway Analysis
  14. Notebook: Module 6 Notebook
  15. Video: Jupyter Notebooks 101
  16. Reading: Jupyter Notebooks Essentials
  17. Reading: Notebook Module 6 Tutorial
  18. Discussion Prompt: Module 6 Discussion
  19. Reading: Module 6 R Clustering and Pathways
  20. Reading: Module 6 Resources
  21. Video: Concluding Remarks
  22. Reading: Acknowledgements

Graded: Module 6 Quiz
Graded: Module 6 R Clustering and Pathways
There are no reviews yet.

Share your review

Do you have experience with this course? Submit your review and help other people make the right choice. As a thank you for your effort we will donate £1.- to Stichting Edukans.

There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.