Big Data, Genes, and Medicine

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Big Data, Genes, and Medicine

Instructor: Isabelle Bichindaritz

37,577 already enrolled

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6 modules

Gain insight into a topic and learn the fundamentals.

Advanced level

Designed for those already in the industry

4 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

6 modules

Gain insight into a topic and learn the fundamentals.

Advanced level

Designed for those already in the industry

4 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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 videosTotal 59 minutes

Introduction to the Course2 minutes
Introduction to Module2 minutes
DNA and Genes9 minutes
RNA and Proteins7 minutes
Transcription Process5 minutes
Transcription Animation2 minutes
Translation Process6 minutes
Translation Animation2 minutes
Data, Variables, and Big Datasets6 minutes
Working with cBioPortal - Genetic Data Analysis9 minutes
Working with cBioPortal - Gene Networks9 minutes

2 readingsTotal 20 minutes

Module 1 cBioPortal Data Analytics10 minutes
Module 1 Resources10 minutes

6 assignmentsTotal 180 minutes

DNA, RNA, Genes, and Proteins30 minutes
Transcription and Translation Processes30 minutes
Data, Variables, and Big Datasets30 minutes
Working with cBioPortal30 minutes
Module 1 Quiz30 minutes
Module 1 cBioPortal Data Analytics30 minutes

1 discussion promptTotal 10 minutes

Module 1 Discussion10 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 videosTotal 75 minutes

Introduction to Module5 minutes
Datasets and Files11 minutes
Data Sources12 minutes
Importance of Data Preprocessing5 minutes
Data Preprocessing Tasks3 minutes
Replacing Missing Values4 minutes
Data Normalization10 minutes
Data Discretization5 minutes
Feature Selection3 minutes
Data Sampling3 minutes
Principles of R7 minutes
R Language2 minutes
Jupyter Notebooks 1017 minutes

4 readingsTotal 40 minutes

Jupyter Notebooks Essentials10 minutes
Notebook Module 2 Tutorial10 minutes
Module 2 R Data Preprocessing10 minutes
Module 2 Resources10 minutes

8 assignmentsTotal 230 minutes

Datasets and Files30 minutes
Data Preprocessing Tasks30 minutes
Replacing Missing Values30 minutes
Normalization and Discretization30 minutes
Data Reduction30 minutes
Working with R30 minutes
Module 2 Quiz20 minutes
Module 2 R Data Preprocessing30 minutes

1 discussion promptTotal 10 minutes

Module 2 Discussion10 minutes

2 ungraded labsTotal 120 minutes

Module 2 Notebook60 minutes
Module 2 Notebook60 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 videosTotal 53 minutes

Introduction to Module2 minutes
Overview of Feature Selection Methods13 minutes
Filter Methods5 minutes
Wrapper Methods5 minutes
Evaluation Schemes8 minutes
Selecting Differentially Expressed Genes3 minutes
Heatmaps6 minutes
R Scripts for Feature Selection3 minutes
Jupyter Notebooks 1017 minutes

4 readingsTotal 40 minutes

Notebook Module 3 Tutorial10 minutes
Jupyter Notebooks Essentials10 minutes
Module 3 R Finding Differentially Expressed Genes10 minutes
Module 3 Resources10 minutes

6 assignmentsTotal 180 minutes

Feature Selection Methods30 minutes
Evaluation Schemes30 minutes
Differentially Expressed Genes30 minutes
Heatmaps30 minutes
Module 3 Quiz30 minutes
Module 3 R Finding Differentially Expressed Genes30 minutes

1 discussion promptTotal 10 minutes

Module 3 Discussion10 minutes

2 ungraded labsTotal 120 minutes

Module 3 Notebook60 minutes
Module 3 Notebook60 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 videosTotal 85 minutes

Introduction to Module0 minutes
Overview of Classification and Prediction Methods9 minutes
Classification Methods Based on Analogy12 minutes
Classification Methods Based on Rules13 minutes
Classification Methods Based on Neural Networks7 minutes
Classification Methods Based on Statistics4 minutes
Classification Methods Based on Probabilities8 minutes
Prediction Methods4 minutes
Evaluation Schemes14 minutes
Prediction Workflow4 minutes
R Scripts for Prediction2 minutes
Jupyter Notebooks 1017 minutes

4 readingsTotal 40 minutes

Jupyter Notebooks Essentials10 minutes
Notebook Module 4 Tutorial10 minutes
Module 4 R Predicting Diseases from Genes10 minutes
Module 4 Resources10 minutes

10 assignmentsTotal 300 minutes

Overview30 minutes
Classification with Analogy30 minutes
Classification based on Rules30 minutes
Classification with Neural Networks30 minutes
Classification based on Statistics30 minutes
Classification based on Probabilities30 minutes
Prediction Models30 minutes
Evaluation Schemes30 minutes
Module 4 Quiz30 minutes
Module 4 R Predicting Diseases from Genes30 minutes

1 discussion promptTotal 10 minutes

Module 4 Discussion10 minutes

1 ungraded labTotal 60 minutes

Module 4 Notebook60 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 videosTotal 81 minutes

Introduction to Module2 minutes
Overview of Gene Alterations16 minutes
Genetic Mutations10 minutes
Finding Genetic Mutations8 minutes
Methylation9 minutes
Copy Number Alterations11 minutes
Genomic Alterations and Gene Expressions17 minutes
R Scripts for Gene Alterations2 minutes
Jupyter Notebooks 1017 minutes

4 readingsTotal 40 minutes

Notebook Module 5 Tutorial10 minutes
Jupyter Notebooks Essentials10 minutes
Module 5 R Gene Alterations10 minutes
Module 5 Resources10 minutes

8 assignmentsTotal 230 minutes

Gene Alterations30 minutes
Gene Mutations30 minutes
Methylation30 minutes
Copy Number Alterations30 minutes
Genomic Alterations and Gene Expressions30 minutes
Module 5 Quiz (Temporary)30 minutes
Module 5 Quiz20 minutes
Module 5 R Gene Alterations30 minutes

1 discussion promptTotal 10 minutes

Module 5 Discussion10 minutes

1 ungraded labTotal 60 minutes

Module 5 Notebook60 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 videosTotal 73 minutes

Introduction to Module2 minutes
Overview of Clustering Methods8 minutes
Similarity Assessment9 minutes
Clustering with KMeans8 minutes
Density Based Clustering4 minutes
Hierarchical Clustering7 minutes
Pathway Analysis13 minutes
Pathway Discovery7 minutes
Pathway Visualization6 minutes
R Scripts for Clustering and Pathway Analysis2 minutes
Jupyter Notebooks 1017 minutes
Concluding Remarks1 minute

5 readingsTotal 50 minutes

Jupyter Notebooks Essentials10 minutes
Notebook Module 6 Tutorial10 minutes
Module 6 R Clustering and Pathways10 minutes
Module 6 Resources10 minutes
Acknowledgements10 minutes

5 assignmentsTotal 150 minutes

Clustering30 minutes
Clustering Methods30 minutes
Pathways30 minutes
Module 6 Quiz30 minutes
Module 6 R Clustering and Pathways30 minutes

1 discussion promptTotal 10 minutes

Module 6 Discussion10 minutes

1 ungraded labTotal 60 minutes

Module 6 Notebook60 minutes

Instructor

Isabelle Bichindaritz

The State University of New York

1 Course37,577 learners

Offered by

The State University of New York

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