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.
Created by:Â Â The State University of New York
Taught by:Â Â 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 |
Syllabus
WEEK 1
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, 2 readings, 4 practice quizzes
- 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
Graded: Module 1 Quiz
Graded: Module 1 cBioPortal Data Analytics
WEEK 2
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, 4 readings, 6 practice quizzes
- 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
Graded: Module 2 Quiz
Graded: Module 2 R Data Preprocessing
WEEK 3
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, 4 readings, 4 practice quizzes
- 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
Graded: Module 3 Quiz
Graded: Module 3 R Finding Differentially Expressed Genes
WEEK 4
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, 4 readings, 8 practice quizzes
- 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
Graded: Module 4 Quiz
Graded: Module 4 R Predicting Diseases from Genes
WEEK 5
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, 4 readings, 6 practice quizzes
- 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
Graded: Module 5 Quiz
Graded: Module 5 R Gene Alterations
WEEK 6
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, 5 readings, 3 practice quizzes
- 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
Graded: Module 6 Quiz
Graded: Module 6 R Clustering and Pathways
FAQs
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Ratings and Reviews
prof.Bichindaritz is very kind, the course useful for beginners interested in bioinformatics. And many useful resources are provided. Hope for more weeks introducing life science data analysis!
I am very grateful with Dr. Isabelle Bichindaritz and her team that made it possible this course. This course was excellent for me and now I want to learn more about Bioinformatics.
Excellent course for an introduction to Big data and bioscience.
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I do like how in depth this was so that you can get a real taste of how some of this analysis is performed as well as the fundamentals behind it. Balancing all of the biology, informatics, statistics to an audience where you don't know everyone's backgrounds is very challenging but I think this course does a fairly good job of it. I do wish some of the answers were updated, as when you go through some of the analysis using the public tools they may have changed because certain answers I got were very slightly off (1-3) and I had thought it was my fault at first until I checked the forums, so that was annoying at worst. I liked all of the cbio portal lessons and the quizzes where we had to go into the code and change some numbers. The work in the jupyter notebooks felt overwhelming sometimes so I wish I all the codes were slightly more thoroughly explained, but otherwise a satisfying course.