Big Data, Genes, and Medicine
Keep adding new skills with 10,000+ programs for $239 (usually $399). Save now.
Ask Coursera
287 reviews
287 reviews
Skills you'll gain
- Informatics
- Predictive Analytics
- Biomedical Technology
- Model Evaluation
- Data Preprocessing
- Bioinformatics
- Molecular Biology
- Big Data
- Health Informatics
- Feature Engineering
- Statistical Programming
- Medical Science and Research
- Data Transformation
- Statistical Analysis
- Data Cleansing
- Predictive Modeling
- Advanced Analytics
- Analytics
Tools you'll learn
Details to know
See how employees at top companies are mastering in-demand skills
There are 6 modules in 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.
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.
What's included
11 videos2 readings6 assignments1 discussion prompt
11 videos•Total 59 minutes
- Introduction to the Course•2 minutes
- Introduction to Module•2 minutes
- DNA and Genes•9 minutes
- RNA and Proteins•7 minutes
- Transcription Process•5 minutes
- Transcription Animation•2 minutes
- Translation Process•6 minutes
- Translation Animation•2 minutes
- Data, Variables, and Big Datasets•6 minutes
- Working with cBioPortal - Genetic Data Analysis•9 minutes
- Working with cBioPortal - Gene Networks•9 minutes
2 readings•Total 20 minutes
- Module 1 cBioPortal Data Analytics•10 minutes
- Module 1 Resources•10 minutes
6 assignments•Total 180 minutes
- Module 1 Quiz•30 minutes
- Module 1 cBioPortal Data Analytics•30 minutes
- DNA, RNA, Genes, and Proteins•30 minutes
- Transcription and Translation Processes•30 minutes
- Data, Variables, and Big Datasets•30 minutes
- Working with cBioPortal•30 minutes
1 discussion prompt•Total 10 minutes
- Module 1 Discussion•10 minutes
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.
What's included
13 videos4 readings8 assignments1 discussion prompt2 ungraded labs
13 videos•Total 75 minutes
- Introduction to Module•5 minutes
- Datasets and Files•11 minutes
- Data Sources•12 minutes
- Importance of Data Preprocessing•5 minutes
- Data Preprocessing Tasks•3 minutes
- Replacing Missing Values•4 minutes
- Data Normalization•10 minutes
- Data Discretization•5 minutes
- Feature Selection•3 minutes
- Data Sampling•3 minutes
- Principles of R•7 minutes
- R Language•2 minutes
- Jupyter Notebooks 101•7 minutes
4 readings•Total 40 minutes
- Jupyter Notebooks Essentials•10 minutes
- Notebook Module 2 Tutorial•10 minutes
- Module 2 R Data Preprocessing•10 minutes
- Module 2 Resources•10 minutes
8 assignments•Total 230 minutes
- Module 2 Quiz•20 minutes
- Module 2 R Data Preprocessing•30 minutes
- Datasets and Files•30 minutes
- Data Preprocessing Tasks•30 minutes
- Replacing Missing Values•30 minutes
- Normalization and Discretization•30 minutes
- Data Reduction•30 minutes
- Working with R•30 minutes
1 discussion prompt•Total 10 minutes
- Module 2 Discussion•10 minutes
2 ungraded labs•Total 120 minutes
- Module 2 Notebook•60 minutes
- Module 2 Notebook•60 minutes
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.
What's included
9 videos4 readings6 assignments1 discussion prompt2 ungraded labs
9 videos•Total 53 minutes
- Introduction to Module•2 minutes
- Overview of Feature Selection Methods•13 minutes
- Filter Methods•5 minutes
- Wrapper Methods•5 minutes
- Evaluation Schemes•8 minutes
- Selecting Differentially Expressed Genes•3 minutes
- Heatmaps•6 minutes
- R Scripts for Feature Selection•3 minutes
- Jupyter Notebooks 101•7 minutes
4 readings•Total 40 minutes
- Notebook Module 3 Tutorial•10 minutes
- Jupyter Notebooks Essentials•10 minutes
- Module 3 R Finding Differentially Expressed Genes•10 minutes
- Module 3 Resources•10 minutes
6 assignments•Total 180 minutes
- Module 3 Quiz•30 minutes
- Module 3 R Finding Differentially Expressed Genes•30 minutes
- Feature Selection Methods•30 minutes
- Evaluation Schemes•30 minutes
- Differentially Expressed Genes•30 minutes
- Heatmaps•30 minutes
1 discussion prompt•Total 10 minutes
- Module 3 Discussion•10 minutes
2 ungraded labs•Total 120 minutes
- Module 3 Notebook•60 minutes
- Module 3 Notebook•60 minutes
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.
What's included
12 videos4 readings10 assignments1 discussion prompt1 ungraded lab
12 videos•Total 85 minutes
- Introduction to Module•0 minutes
- Overview of Classification and Prediction Methods•9 minutes
- Classification Methods Based on Analogy•12 minutes
- Classification Methods Based on Rules•13 minutes
- Classification Methods Based on Neural Networks•7 minutes
- Classification Methods Based on Statistics•4 minutes
- Classification Methods Based on Probabilities•8 minutes
- Prediction Methods•4 minutes
- Evaluation Schemes•14 minutes
- Prediction Workflow•4 minutes
- R Scripts for Prediction•2 minutes
- Jupyter Notebooks 101•7 minutes
4 readings•Total 40 minutes
- Jupyter Notebooks Essentials•10 minutes
- Notebook Module 4 Tutorial•10 minutes
- Module 4 R Predicting Diseases from Genes•10 minutes
- Module 4 Resources•10 minutes
10 assignments•Total 300 minutes
- Module 4 Quiz•30 minutes
- Module 4 R Predicting Diseases from Genes•30 minutes
- Overview•30 minutes
- Classification with Analogy•30 minutes
- Classification based on Rules•30 minutes
- Classification with Neural Networks•30 minutes
- Classification based on Statistics•30 minutes
- Classification based on Probabilities•30 minutes
- Prediction Models•30 minutes
- Evaluation Schemes•30 minutes
1 discussion prompt•Total 10 minutes
- Module 4 Discussion•10 minutes
1 ungraded lab•Total 60 minutes
- Module 4 Notebook•60 minutes
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.
What's included
9 videos4 readings8 assignments1 discussion prompt1 ungraded lab
9 videos•Total 81 minutes
- Introduction to Module•2 minutes
- Overview of Gene Alterations•16 minutes
- Genetic Mutations•10 minutes
- Finding Genetic Mutations•8 minutes
- Methylation•9 minutes
- Copy Number Alterations•11 minutes
- Genomic Alterations and Gene Expressions•17 minutes
- R Scripts for Gene Alterations•2 minutes
- Jupyter Notebooks 101•7 minutes
4 readings•Total 40 minutes
- Notebook Module 5 Tutorial•10 minutes
- Jupyter Notebooks Essentials•10 minutes
- Module 5 R Gene Alterations•10 minutes
- Module 5 Resources•10 minutes
8 assignments•Total 230 minutes
- Module 5 Quiz•20 minutes
- Module 5 R Gene Alterations•30 minutes
- Gene Alterations•30 minutes
- Gene Mutations•30 minutes
- Methylation•30 minutes
- Copy Number Alterations•30 minutes
- Genomic Alterations and Gene Expressions•30 minutes
- Module 5 Quiz (Temporary)•30 minutes
1 discussion prompt•Total 10 minutes
- Module 5 Discussion•10 minutes
1 ungraded lab•Total 60 minutes
- Module 5 Notebook•60 minutes
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.
What's included
12 videos5 readings5 assignments1 discussion prompt1 ungraded lab
12 videos•Total 73 minutes
- Introduction to Module•2 minutes
- Overview of Clustering Methods•8 minutes
- Similarity Assessment•9 minutes
- Clustering with KMeans•8 minutes
- Density Based Clustering•4 minutes
- Hierarchical Clustering•7 minutes
- Pathway Analysis•13 minutes
- Pathway Discovery•7 minutes
- Pathway Visualization•6 minutes
- R Scripts for Clustering and Pathway Analysis•2 minutes
- Jupyter Notebooks 101•7 minutes
- Concluding Remarks•1 minute
5 readings•Total 50 minutes
- Jupyter Notebooks Essentials•10 minutes
- Notebook Module 6 Tutorial•10 minutes
- Module 6 R Clustering and Pathways•10 minutes
- Module 6 Resources•10 minutes
- Acknowledgements•10 minutes
5 assignments•Total 150 minutes
- Module 6 Quiz•30 minutes
- Module 6 R Clustering and Pathways•30 minutes
- Clustering•30 minutes
- Clustering Methods•30 minutes
- Pathways•30 minutes
1 discussion prompt•Total 10 minutes
- Module 6 Discussion•10 minutes
1 ungraded lab•Total 60 minutes
- Module 6 Notebook•60 minutes
Instructor
Offered by
Explore more from Health Informatics
- Status: PreviewB
Birla Institute of Technology & Science, Pilani
Course
- Status: PreviewU
University of Geneva
Course
- Status: Free TrialJ
Johns Hopkins University
Course
- Status: Free TrialU
University of California San Diego
Course
Why people choose Coursera for their career
Learner reviews
- 5 stars
55.70%
- 4 stars
27.68%
- 3 stars
8.30%
- 2 stars
2.76%
- 1 star
5.53%
Showing 3 of 287
Reviewed on May 23, 2020
A very informative course . I learn so many things from this course and this course has very good coverage in data and its analysis. thank you so much for providing this course
Reviewed on Aug 31, 2020
A very interesting basis for a start in big data with some particularly enjoyable practical exercises.
Reviewed on Feb 16, 2023
I learned a lot, all thanks to SUNY and coursera for gradually making my dream a reality.
Frequently asked questions
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
More questions
Financial aid available,