The State University of New York

Big Data, Genes, and Medicine

39,787 already enrolled

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Gain insight into a topic and learn the fundamentals.
4.3

(283 reviews)

Advanced level
Designed for those already in the industry
Flexible schedule
Approx. 40 hours
Learn at your own pace
90%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.3

(283 reviews)

Advanced level
Designed for those already in the industry
Flexible schedule
Approx. 40 hours
Learn at your own pace
90%
Most learners liked this course

Details to know

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Assessments

43 assignments

Taught in English

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There are 6 modules in this course

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

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

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

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

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

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

Instructor

Instructor ratings
4.3 (64 ratings)
Isabelle Bichindaritz
The State University of New York
1 Course35,479 learners

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Recommended if you're interested in Health Informatics

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Learner reviews

4.3

283 reviews

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