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.

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.

Advanced Level

Approx. 40 hours to complete

English

Instructor

Instructor rating

4.38/5 (50 Ratings)

Isabelle Bichindaritz

Syllabus - What you will learn from this course

Content Rating90%(2,117 ratings)

Week

1

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 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

2

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 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

3

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 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

4

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 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

5

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

6

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 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

86 reviews

5 stars
54.33%
4 stars
29.13%
3 stars
8.66%
2 stars
2.36%
1 star
5.51%

TOP REVIEWS FROM 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

by AMNov 28, 2018

Should have focused more on the coding part. Rest of the course was informative and well structured.

by CWMay 19, 2019

Highly recommended! A great yet in-depth course on data science and bioinformatics.

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.

View all reviews

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Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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Big Data, Genes, and Medicine