Big Data, Genes, and Medicine

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Big Data, Genes, and Medicine

Instructor: Isabelle Bichindaritz

37,135 already enrolled

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6 modules

Gain insight into a topic and learn the fundamentals.

287 reviews

Advanced level

Designed for those already in the industry

Flexible schedule

4 weeks at 10 hours a week

Learn at your own pace

91%

Most learners liked this course

6 modules

Gain insight into a topic and learn the fundamentals.

287 reviews

Advanced level

Designed for those already in the industry

Flexible schedule

4 weeks at 10 hours a week

Learn at your own pace

91%

Most learners liked this course

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

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Assessments

43 assignments

Taught in English

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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

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
Module 1 Quiz 30 minutes
Module 1 cBioPortal Data Analytics 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

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
Module 2 Quiz 20 minutes
Module 2 R Data Preprocessing 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

Feature Selection Methods 30 minutes
Evaluation Schemes 30 minutes
Differentially Expressed Genes 30 minutes
Heatmaps 30 minutes
Module 3 Quiz 30 minutes
Module 3 R Finding Differentially Expressed Genes 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

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
Module 4 Quiz 30 minutes
Module 4 R Predicting Diseases from Genes 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

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
Module 5 Quiz 20 minutes
Module 5 R Gene Alterations 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

Clustering 30 minutes
Clustering Methods 30 minutes
Pathways 30 minutes
Module 6 Quiz 30 minutes
Module 6 R Clustering and 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

Instructor ratings

(66 ratings)

Isabelle Bichindaritz

The State University of New York

1 Course 37,135 learners

Offered by

The State University of New York

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Reviewed on Feb 16, 2023

I learned a lot, all thanks to SUNY and coursera for gradually making my dream a reality.

Reviewed on Jul 2, 2022

it was a good and useful course. it just needs an update because of some changes in the cbioportal.

Reviewed on Aug 13, 2022

Great place to start for those wanting to obtain a bioinformatic skill base. The skills I learned during this course have had a positive impact upon my current lab project. Well worth your time!

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