About this Course
4.2
101 ratings
28 reviews
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Advanced Level

Advanced Level

Hours to complete

Approx. 22 hours to complete

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

English

Subtitles: English

Skills you will gain

BioinformaticsData Clustering AlgorithmsBig DataR Programming
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Advanced Level

Advanced Level

Hours to complete

Approx. 22 hours to complete

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

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
Hours to complete
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. ...
Reading
11 videos (Total 59 min), 2 readings, 6 quizzes
Video11 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
Reading2 readings
Module 1 cBioPortal Data Analytics10m
Module 1 Resources10m
Quiz6 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
Hours to complete
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. ...
Reading
13 videos (Total 75 min), 4 readings, 8 quizzes
Video13 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
Reading4 readings
Jupyter Notebooks Essentials10m
Notebook Module 2 Tutorial10m
Module 2 R Data Preprocessing10m
Module 2 Resources10m
Quiz8 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
Hours to complete
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. ...
Reading
9 videos (Total 53 min), 4 readings, 6 quizzes
Video9 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
Reading4 readings
Notebook Module 3 Tutorial10m
Jupyter Notebooks Essentials10m
Module 3 R Finding Differentially Expressed Genes10m
Module 3 Resources10m
Quiz6 practice exercises
Feature Selection Methods4m
Evaluation Schemes2m
Differentially Expressed Genes4m
Heatmaps4m
Module 3 Quiz16m
Module 3 R Finding Differentially Expressed Genes10m
Week
4
Hours to complete
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....
Reading
12 videos (Total 85 min), 4 readings, 10 quizzes
Video12 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
Reading4 readings
Jupyter Notebooks Essentials10m
Notebook Module 4 Tutorial10m
Module 4 R Predicting Diseases from Genes10m
Module 4 Resources10m
Quiz10 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

Avatar

Isabelle Bichindaritz

Associate Professor
Computer Science

About The State University of New York

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Frequently Asked Questions

  • 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.

  • 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.

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