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.

Learner Career Outcomes

50%

started a new career after completing these courses

43%

got a tangible career benefit from this course

20%

got a pay increase or promotion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Advanced Level

Approx. 24 hours to complete

Suggested: 6 weeks of study, 3-5 hours per week...

English

Subtitles: English

Skills you will gain

BioinformaticsData Clustering AlgorithmsBig DataR Programming

Learner Career Outcomes

50%

started a new career after completing these courses

43%

got a tangible career benefit from this course

20%

got a pay increase or promotion

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Advanced Level

Approx. 24 hours to complete

Suggested: 6 weeks of study, 3-5 hours per week...

English

Subtitles: English

Syllabus - What you will learn from this course

Week

1

2 hours to complete

Genes and Data

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.

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 Proteins4m

Transcription and Translation Processes6m

Data, Variables, and Big Datasets4m

Working with cBioPortal6m

Module 1 Quiz20m

Module 1 cBioPortal Data Analytics8m

Week

2

5 hours to complete

Preparing Datasets for Analysis

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.

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 Files4m

Data Preprocessing Tasks4m

Replacing Missing Values2m

Normalization and Discretization4m

Data Reduction4m

Working with R4m

Module 2 Quiz20m

Module 2 R Data Preprocessing10m

Week

3

4 hours to complete

Finding Differentially Expressed Genes

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.

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 Methods4m

Evaluation Schemes2m

Differentially Expressed Genes4m

Heatmaps4m

Module 3 Quiz16m

Module 3 R Finding Differentially Expressed Genes10m

Week

4

4 hours to complete

Predicting Diseases from Genes

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.

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

Overview4m

Classification with Analogy2m

Classification based on Rules2m

Classification with Neural Networks2m

Classification based on Statistics2m

Classification based on Probabilities2m

Prediction Models2m

Evaluation Schemes2m

Module 4 Quiz20m

Module 4 R Predicting Diseases from Genes10m

Week

5

4 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.

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 Alterations4m

Gene Mutations6m

Methylation2m

Copy Number Alterations4m

Genomic Alterations and Gene Expressions4m

Module 5 Quiz (Temporary)20m

Module 5 Quiz20m

Module 5 R Gene Alterations10m

Week

6

4 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.

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

Clustering6m

Clustering Methods6m

Pathways4m

Module 6 Quiz20m

Module 6 R Clustering and Pathways10m

Instructor

Isabelle Bichindaritz

Associate Professor

Computer Science

Frequently Asked Questions

When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I purchase the Certificate?
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?

More questions? Visit the Learner Help Center.

Big Data, Genes, and Medicine