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.
Big Data, Genes, and Medicine
Offered By
Big Data, Genes, and Medicine
The State University of New York
About this Course
4.2
100 ratings
Skills you will gain
BioinformaticsData Clustering AlgorithmsBig DataR Programming
Syllabus - What you will learn from this course
Week
1Genes 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 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
2Preparing 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
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
3Finding 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
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
4Predicting 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
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
Instructor
Isabelle Bichindaritz
Associate ProfessorComputer Science
About 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.
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