About 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.
Who is this class for: This course is primarily aimed at health care professionals or assistants, and those with a BS/MA/MS in science or technology or equivalent professional experience. Minimum technical skills are a good understanding of using an Excel spreadsheet. Additional prerequisite knowledge in basic statistics would be preferred, however additional resources will be made available to learners to acquire this knowledge. I think that anyone interested in getting insights into how to harness Big Data to better understand, prevent, and treat diseases can take this course because the material can be applied at different levels of expertise.
Taught by: Isabelle Bichindaritz, Associate Professor
Computer Science
| Level | Advanced |
| Commitment | 6 weeks of study, 3-5 hours per week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
- Video: Introduction to the Course
- Video: Introduction to Module
- Video: DNA and Genes
- Video: RNA and Proteins
- Practice Quiz: DNA, RNA, Genes, and Proteins
- Video: Transcription Process
- Video: Transcription Animation
- Video: Translation Process
- Video: Translation Animation
- Practice Quiz: Transcription and Translation Processes
- Video: Data, Variables, and Big Datasets
- Practice Quiz: Data, Variables, and Big Datasets
- Video: Working with cBioPortal - Genetic Data Analysis
- Video: Working with cBioPortal - Gene Networks
- Practice Quiz: Working with cBioPortal
- Discussion Prompt: Module 1 Discussion
- Reading: Module 1 cBioPortal Data Analytics
- Reading: Module 1 Resources
- Video: Introduction to Module
- Video: Datasets and Files
- Video: Data Sources
- Practice Quiz: Datasets and Files
- Video: Importance of Data Preprocessing
- Video: Data Preprocessing Tasks
- Practice Quiz: Data Preprocessing Tasks
- Video: Replacing Missing Values
- Practice Quiz: Replacing Missing Values
- Video: Data Normalization
- Video: Data Discretization
- Practice Quiz: Normalization and Discretization
- Video: Feature Selection
- Video: Data Sampling
- Practice Quiz: Data Reduction
- Video: Principles of R
- Video: R Language
- Practice Quiz: Working with R
- Notebook: Module 2 Notebook
- Video: Jupyter Notebooks 101
- Reading: Jupyter Notebooks Essentials
- Reading: Notebook Module 2 Tutorial
- Discussion Prompt: Module 2 Discussion
- Reading: Module 2 R Data Preprocessing
- Notebook: Module 2 Notebook
- Reading: Module 2 Resources
- Video: Introduction to Module
- Video: Overview of Feature Selection Methods
- Video: Filter Methods
- Video: Wrapper Methods
- Practice Quiz: Feature Selection Methods
- Video: Evaluation Schemes
- Practice Quiz: Evaluation Schemes
- Video: Selecting Differentially Expressed Genes
- Practice Quiz: Differentially Expressed Genes
- Video: Heatmaps
- Practice Quiz: Heatmaps
- Video: R Scripts for Feature Selection
- Notebook: Module 3 Notebook
- Reading: Notebook Module 3 Tutorial
- Reading: Jupyter Notebooks Essentials
- Video: Jupyter Notebooks 101
- Discussion Prompt: Module 3 Discussion
- Reading: Module 3 R Finding Differentially Expressed Genes
- Notebook: Module 3 Notebook
- Reading: Module 3 Resources
- Video: Introduction to Module
- Video: Overview of Classification and Prediction Methods
- Practice Quiz: Overview
- Video: Classification Methods Based on Analogy
- Practice Quiz: Classification with Analogy
- Video: Classification Methods Based on Rules
- Practice Quiz: Classification based on Rules
- Video: Classification Methods Based on Neural Networks
- Practice Quiz: Classification with Neural Networks
- Video: Classification Methods Based on Statistics
- Practice Quiz: Classification based on Statistics
- Video: Classification Methods Based on Probabilities
- Practice Quiz: Classification based on Probabilities
- Video: Prediction Methods
- Practice Quiz: Prediction Models
- Video: Evaluation Schemes
- Practice Quiz: Evaluation Schemes
- Video: Prediction Workflow
- Video: R Scripts for Prediction
- Notebook: Module 4 Notebook
- Reading: Jupyter Notebooks Essentials
- Video: Jupyter Notebooks 101
- Reading: Notebook Module 4 Tutorial
- Discussion Prompt: Module 4 Discussion
- Reading: Module 4 R Predicting Diseases from Genes
- Reading: Module 4 Resources
- Video: Introduction to Module
- Video: Overview of Gene Alterations
- Practice Quiz: Gene Alterations
- Video: Genetic Mutations
- Video: Finding Genetic Mutations
- Practice Quiz: Gene Mutations
- Video: Methylation
- Practice Quiz: Methylation
- Video: Copy Number Alterations
- Practice Quiz: Copy Number Alterations
- Video: Genomic Alterations and Gene Expressions
- Practice Quiz: Genomic Alterations and Gene Expressions
- Video: R Scripts for Gene Alterations
- Notebook: Module 5 Notebook
- Video: Jupyter Notebooks 101
- Reading: Notebook Module 5 Tutorial
- Reading: Jupyter Notebooks Essentials
- Discussion Prompt: Module 5 Discussion
- Practice Quiz: Module 5 Quiz (Temporary)
- Reading: Module 5 R Gene Alterations
- Reading: Module 5 Resources
- Video: Introduction to Module
- Video: Overview of Clustering Methods
- Video: Similarity Assessment
- Practice Quiz: Clustering
- Video: Clustering with KMeans
- Video: Density Based Clustering
- Video: Hierarchical Clustering
- Practice Quiz: Clustering Methods
- Video: Pathway Analysis
- Video: Pathway Discovery
- Video: Pathway Visualization
- Practice Quiz: Pathways
- Video: R Scripts for Clustering and Pathway Analysis
- Notebook: Module 6 Notebook
- Video: Jupyter Notebooks 101
- Reading: Jupyter Notebooks Essentials
- Reading: Notebook Module 6 Tutorial
- Discussion Prompt: Module 6 Discussion
- Reading: Module 6 R Clustering and Pathways
- Reading: Module 6 Resources
- Video: Concluding Remarks
- Reading: Acknowledgements
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| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
This is an excellent course to learn about fundamental to intermediate level of "Big-data" in biomedical sciences and effective analysis. Very well designed and perfectly delivered by Prof. Bichindaritz. I would strongly recommend other genuine bioinformatics aspirants to enrol for this very course.
Thank you. :-)
Excellent introduction to how to run genetic data analytic applications on a big data infraestructure.
The theorical concepts related to data analytics were explained with highly professional quality; easy to understand for those with a data science backgroud. Also there were efective, efficient and acertive in the on the process of knowledge transference.
An outstanding course that exceed my expectations on regarding to data analytics theory and methods.
A good start. Congratulations.
The course has some minors mistakes that does not affect the essence of it.
Great course, it was exactly what I was missing to help me progress in my work analyzing genetic data. By studying the content, I gained in a short amount of time the new skills I needed to analyze genetic data. The course is very rich, and I enjoyed it very much. It is very visible that the professor is an expert in this field !
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Very good course for everyone new in the field of data science. Very good short lectures guiding you step by step to relevant skills and counclusions.