Big Data, Genes, and Medicine

4.2
stars
221 ratings

Isabelle Bichindaritz

25,300 already enrolled

Offered By

Big Data, Genes, and Medicine

The State University of New York

About this Course

25,833 recent views

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.

Learner Career Outcomes

25%

started a new career after completing these courses

33%

got a tangible career benefit from this course

17%

got a pay increase or promotion

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.

Advanced Level

Approx. 40 hours to complete

English

Subtitles: French, Portuguese (European), Russian, English, Spanish

Skills you will gain

BioinformaticsData Clustering AlgorithmsBig DataR Programming

Learner Career Outcomes

25%

started a new career after completing these courses

33%

got a tangible career benefit from this course

17%

got a pay increase or promotion

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.

Advanced Level

Approx. 40 hours to complete

English

Subtitles: French, Portuguese (European), Russian, English, Spanish

Instructor

Instructor rating

4.51/5 (30 Ratings)

Isabelle Bichindaritz

Offered by

The State University of New York

The State University of New York, with 64 unique institutions, is the largest comprehensive system of higher education in the United States. Educating nearly 468,000 students in more than 7,500 degree and certificate programs both on campus and online, SUNY has nearly 3 million alumni around the globe.

Syllabus - What you will learn from this course

Content Rating90%(1,837 ratings)

Week

Week 1

4 hours to complete

Genes and Data

After this module, you will be able to

4 hours to complete

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

11 videos

Introduction to the Course1m

Introduction to Module1m

DNA and Genes9m

RNA and Proteins6m

Transcription Process4m

Transcription Animation1m

Translation Process5m

Translation Animation2m

Data, Variables, and Big Datasets6m

Working with cBioPortal - Genetic Data Analysis9m

Working with cBioPortal - Gene Networks9m

2 readings

Module 1 cBioPortal Data Analytics10m

Module 1 Resources10m

6 practice exercises

DNA, RNA, Genes, and Proteins30m

Transcription and Translation Processes30m

Data, Variables, and Big Datasets30m

Working with cBioPortal30m

Module 1 Quiz30m

Module 1 cBioPortal Data Analytics30m

Week

Week 2

8 hours to complete

Preparing Datasets for Analysis

After this module, you will be able to:

8 hours to complete

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

13 videos

Introduction to Module4m

Datasets and Files10m

Data Sources11m

Importance of Data Preprocessing4m

Data Preprocessing Tasks2m

Replacing Missing Values3m

Data Normalization9m

Data Discretization5m

Feature Selection3m

Data Sampling2m

Principles of R6m

R Language1m

Jupyter Notebooks 1017m

4 readings

Jupyter Notebooks Essentials10m

Notebook Module 2 Tutorial10m

Module 2 R Data Preprocessing10m

Module 2 Resources10m

8 practice exercises

Datasets and Files30m

Data Preprocessing Tasks30m

Replacing Missing Values30m

Normalization and Discretization30m

Data Reduction30m

Working with R30m

Module 2 Quiz30m

Module 2 R Data Preprocessing30m

Week

Week 3

7 hours to complete

Finding Differentially Expressed Genes

After this module, you will be able to

7 hours to complete

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

9 videos

Introduction to Module2m

Overview of Feature Selection Methods13m

Filter Methods4m

Wrapper Methods4m

Evaluation Schemes7m

Selecting Differentially Expressed Genes3m

Heatmaps6m

R Scripts for Feature Selection3m

Jupyter Notebooks 1017m

4 readings

Notebook Module 3 Tutorial10m

Jupyter Notebooks Essentials10m

Module 3 R Finding Differentially Expressed Genes10m

Module 3 Resources10m

6 practice exercises

Feature Selection Methods30m

Evaluation Schemes30m

Differentially Expressed Genes30m

Heatmaps30m

Module 3 Quiz30m

Module 3 R Finding Differentially Expressed Genes30m

Week

Week 4

8 hours to complete

Predicting Diseases from Genes

After this module, you will be able to

8 hours to complete

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

12 videos

Introduction to Module9s

Overview of Classification and Prediction Methods8m

Classification Methods Based on Analogy12m

Classification Methods Based on Rules13m

Classification Methods Based on Neural Networks7m

Classification Methods Based on Statistics3m

Classification Methods Based on Probabilities7m

Prediction Methods4m

Evaluation Schemes13m

Prediction Workflow4m

R Scripts for Prediction1m

Jupyter Notebooks 1017m

4 readings

Jupyter Notebooks Essentials10m

Notebook Module 4 Tutorial10m

Module 4 R Predicting Diseases from Genes10m

Module 4 Resources10m

10 practice exercises

Overview30m

Classification with Analogy30m

Classification based on Rules30m

Classification with Neural Networks30m

Classification based on Statistics30m

Classification based on Probabilities30m

Prediction Models30m

Evaluation Schemes30m

Module 4 Quiz30m

Module 4 R Predicting Diseases from Genes30m

Week

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 Module1m

Overview of Gene Alterations16m

Genetic Mutations9m

Finding Genetic Mutations7m

Methylation8m

Copy Number Alterations10m

Genomic Alterations and Gene Expressions16m

R Scripts for Gene Alterations1m

Jupyter Notebooks 1017m

4 readings

Notebook Module 5 Tutorial10m

Jupyter Notebooks Essentials10m

Module 5 R Gene Alterations10m

Module 5 Resources10m

8 practice exercises

Gene Alterations30m

Gene Mutations30m

Methylation30m

Copy Number Alterations30m

Genomic Alterations and Gene Expressions30m

Module 5 Quiz (Temporary)30m

Module 5 Quiz30m

Module 5 R Gene Alterations30m

Week

Week 6

6 hours to complete

Clustering and Pathway Analysis

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.

6 hours to complete

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

12 videos

Introduction to Module2m

Overview of Clustering Methods7m

Similarity Assessment8m

Clustering with KMeans7m

Density Based Clustering4m

Hierarchical Clustering6m

Pathway Analysis12m

Pathway Discovery7m

Pathway Visualization6m

R Scripts for Clustering and Pathway Analysis1m

Jupyter Notebooks 1017m

Concluding Remarks1m

5 readings

Jupyter Notebooks Essentials10m

Notebook Module 6 Tutorial10m

Module 6 R Clustering and Pathways10m

Module 6 Resources10m

Acknowledgements10m

5 practice exercises

Clustering30m

Clustering Methods30m

Pathways30m

Module 6 Quiz30m

Module 6 R Clustering and Pathways30m

Reviews

4.2

72 reviews

5 stars
53.60%
4 stars
30.18%
3 stars
8.55%
2 stars
2.70%
1 star
4.95%

TOP REVIEWS FROM BIG DATA, GENES, AND MEDICINE

by AWJun 11, 2020

Very good for statistics information in regards to genes, medicine, and big data. I wish they would have spent more time on the R code and how to create it, but otherwise was a really good course.

by BBJul 1, 2020

Very practical and easy to follow. The quizzes definitely help you retain the knowledge from the lectures. The hands-on with Jupyter Notebook was my favourite part of this course! Thank you!

by MAMay 9, 2019

Really enjoying the way she is teaching. Very easy to learn and practice. I strongly believe when I will complete the course I will gain a remarkable skill on Big Data, Genes, and Medicine.

by MPMay 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

View all reviews

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More questions? Visit the Learner Help Center.

Big Data, Genes, and Medicine

Big Data, Genes, and Medicine

About this Course

Learner Career Outcomes

25%

33%

17%

Skills you will gain

Learner Career Outcomes

25%

33%

17%

Instructor

Isabelle Bichindaritz

Offered by

The State University of New York

Syllabus - What you will learn from this course

Week 1

Genes and Data

Week 2

Preparing Datasets for Analysis

Week 3

Finding Differentially Expressed Genes

Week 4

Predicting Diseases from Genes

Week 5

Determining Gene Alterations

Week 6

Clustering and Pathway Analysis

Reviews

TOP REVIEWS FROM BIG DATA, GENES, AND MEDICINE

Frequently Asked Questions

Start or advance your career

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Big Data, Genes, and Medicine

Big Data, Genes, and Medicine

About this Course

Learner Career Outcomes

25%

33%

17%

Skills you will gain

Learner Career Outcomes

25%

33%

17%

Instructor

Isabelle Bichindaritz

Offered by

The State University of New York

Syllabus - What you will learn from this course

Week 1

Genes and Data

Week 2

Preparing Datasets for Analysis

Week 3

Finding Differentially Expressed Genes

Week 4

Predicting Diseases from Genes

Week 5

Determining Gene Alterations

Week 6

Clustering and Pathway Analysis

Reviews

TOP REVIEWS FROM BIG DATA, GENES, AND MEDICINE

Frequently Asked Questions

Coursera Footer

Start or advance your career

Browse popular topics

Upskill your team

Earn a degree or certificate online

Coursera

Community

More