About this Course

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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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Advanced Level

Approx. 40 hours to complete

English

Skills you will gain

Bioinformatics
Data Clustering Algorithms
Big Data
R Programming

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Advanced Level

Approx. 40 hours to complete

English

Instructor

Instructor rating

4.38/5 (52 Ratings)

Isabelle Bichindaritz

Syllabus - What you will learn from this course

Content Rating90%(2,161 ratings)

Week1

Week 1

4 hours to complete

Genes and Data

4 hours to complete

11 videos (Total 59 min), 2 readings, 6 quizzes

11 videos

Introduction to the Course1mIntroduction to Module1mDNA and Genes9mRNA and Proteins6mTranscription Process4mTranscription Animation1mTranslation Process5mTranslation Animation2mData, Variables, and Big Datasets6mWorking with cBioPortal - Genetic Data Analysis9mWorking with cBioPortal - Gene Networks9m

2 readings

Module 1 cBioPortal Data Analytics10mModule 1 Resources10m

6 practice exercises

DNA, RNA, Genes, and Proteins30mTranscription and Translation Processes30mData, Variables, and Big Datasets30mWorking with cBioPortal30mModule 1 Quiz30mModule 1 cBioPortal Data Analytics30m

Week2

Week 2

8 hours to complete

Preparing Datasets for Analysis

8 hours to complete

13 videos (Total 75 min), 4 readings, 8 quizzes

13 videos

Introduction to Module4mDatasets and Files10mData Sources11mImportance of Data Preprocessing4mData Preprocessing Tasks2mReplacing Missing Values3mData Normalization9mData Discretization5mFeature Selection3mData Sampling2mPrinciples of R6mR Language1mJupyter Notebooks 1017m

4 readings

Jupyter Notebooks Essentials10mNotebook Module 2 Tutorial10mModule 2 R Data Preprocessing10mModule 2 Resources10m

8 practice exercises

Datasets and Files30mData Preprocessing Tasks30mReplacing Missing Values30mNormalization and Discretization30mData Reduction30mWorking with R30mModule 2 Quiz30mModule 2 R Data Preprocessing30m

Week3

Week 3

7 hours to complete

Finding Differentially Expressed Genes

7 hours to complete

9 videos (Total 53 min), 4 readings, 6 quizzes

9 videos

Introduction to Module2mOverview of Feature Selection Methods13mFilter Methods4mWrapper Methods4mEvaluation Schemes7mSelecting Differentially Expressed Genes3mHeatmaps6mR Scripts for Feature Selection3mJupyter Notebooks 1017m

4 readings

Notebook Module 3 Tutorial10mJupyter Notebooks Essentials10mModule 3 R Finding Differentially Expressed Genes10mModule 3 Resources10m

6 practice exercises

Feature Selection Methods30mEvaluation Schemes30mDifferentially Expressed Genes30mHeatmaps30mModule 3 Quiz30mModule 3 R Finding Differentially Expressed Genes30m

Week4

Week 4

8 hours to complete

Predicting Diseases from Genes

8 hours to complete

12 videos (Total 85 min), 4 readings, 10 quizzes

12 videos

Introduction to Module9sOverview of Classification and Prediction Methods8mClassification Methods Based on Analogy12mClassification Methods Based on Rules13mClassification Methods Based on Neural Networks7mClassification Methods Based on Statistics3mClassification Methods Based on Probabilities7mPrediction Methods4mEvaluation Schemes13mPrediction Workflow4mR Scripts for Prediction1mJupyter Notebooks 1017m

4 readings

Jupyter Notebooks Essentials10mNotebook Module 4 Tutorial10mModule 4 R Predicting Diseases from Genes10mModule 4 Resources10m

10 practice exercises

Overview30mClassification with Analogy30mClassification based on Rules30mClassification with Neural Networks30mClassification based on Statistics30mClassification based on Probabilities30mPrediction Models30mEvaluation Schemes30mModule 4 Quiz30mModule 4 R Predicting Diseases from Genes30m

Week5

Week 5

7 hours to complete

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.

7 hours to complete

9 videos (Total 81 min), 4 readings, 8 quizzes

9 videos

Introduction to Module1mOverview of Gene Alterations16mGenetic Mutations9mFinding Genetic Mutations7mMethylation8mCopy Number Alterations10mGenomic Alterations and Gene Expressions16mR Scripts for Gene Alterations1mJupyter Notebooks 1017m

4 readings

Notebook Module 5 Tutorial10mJupyter Notebooks Essentials10mModule 5 R Gene Alterations10mModule 5 Resources10m

8 practice exercises

Gene Alterations30mGene Mutations30mMethylation30mCopy Number Alterations30mGenomic Alterations and Gene Expressions30mModule 5 Quiz (Temporary)30mModule 5 Quiz30mModule 5 R Gene Alterations30m

Week6

Week 6

6 hours to complete

Clustering and Pathway Analysis

6 hours to complete

12 videos (Total 73 min), 5 readings, 5 quizzes

12 videos

Introduction to Module2mOverview of Clustering Methods7mSimilarity Assessment8mClustering with KMeans7mDensity Based Clustering4mHierarchical Clustering6mPathway Analysis12mPathway Discovery7mPathway Visualization6mR Scripts for Clustering and Pathway Analysis1mJupyter Notebooks 1017mConcluding Remarks1m

5 readings

Jupyter Notebooks Essentials10mNotebook Module 6 Tutorial10mModule 6 R Clustering and Pathways10mModule 6 Resources10mAcknowledgements10m

5 practice exercises

Clustering30mClustering Methods30mPathways30mModule 6 Quiz30mModule 6 R Clustering and Pathways30m

Reviews

4.2

88 reviews

5 stars
54.47%
4 stars
29.18%
3 stars
8.56%
2 stars
2.33%
1 star
5.44%

TOP REVIEWS FROM BIG DATA, GENES, AND MEDICINE

by HHMar 6, 2017

The course is amazing, I want to purchase the course but it is not being working...I don't know what is the problem.

by BJFeb 16, 2023

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

by KMMay 23, 2020

The course was good to know about bioinformatics and how this can be used. Thank You

by SBJun 22, 2020

The course is more of an intermediate course than an advanced one, but a great starting point from where you can find your own interests and develop further on that.

View all reviews

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Big Data, Genes, and Medicine