Informações sobre o curso: 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.
Para quem é direcionado este curso: 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.
Ministrado por: Isabelle Bichindaritz, Associate Professor
Computer Science
| Nível | Avançado |
| Compromisso | 6 weeks of study, 3-5 hours per week |
| Idioma | English |
| Como ser aprovado | Seja aprovado em todas as tarefas para concluir o curso. |
| Classificação do usuário |
- Vídeo: Introduction to Module
- Vídeo: DNA and Genes
- Vídeo: RNA and Proteins
- Тренировочный тест: DNA, RNA, Genes, and Proteins
- Vídeo: Transcription Process
- Vídeo: Transcription Animation
- Vídeo: Translation Process
- Vídeo: Translation Animation
- Тренировочный тест: Transcription and Translation Processes
- Vídeo: Data, Variables, and Big Datasets
- Тренировочный тест: Data, Variables, and Big Datasets
- Vídeo: Working with cBioPortal - Genetic Data Analysis
- Vídeo: Working with cBioPortal - Gene Networks
- Тренировочный тест: Working with cBioPortal
- Вопрос для обсуждения: Module 1 Discussion
- Материал для самостоятельного изучения: Module 1 cBioPortal Data Analytics
- Материал для самостоятельного изучения: Module 1 Resources
- Vídeo: Datasets and Files
- Vídeo: Data Sources
- Тренировочный тест: Datasets and Files
- Vídeo: Importance of Data Preprocessing
- Vídeo: Data Preprocessing Tasks
- Тренировочный тест: Data Preprocessing Tasks
- Vídeo: Replacing Missing Values
- Тренировочный тест: Replacing Missing Values
- Vídeo: Data Normalization
- Vídeo: Data Discretization
- Тренировочный тест: Normalization and Discretization
- Vídeo: Feature Selection
- Vídeo: Data Sampling
- Тренировочный тест: Data Reduction
- Vídeo: Principles of R
- Vídeo: R Language
- Тренировочный тест: Working with R
- Блокнот: Module 2 Notebook
- Vídeo: Jupyter Notebooks 101
- Материал для самостоятельного изучения: Jupyter Notebooks Essentials
- Материал для самостоятельного изучения: Notebook Module 2 Tutorial
- Вопрос для обсуждения: Module 2 Discussion
- Материал для самостоятельного изучения: Module 2 R Data Preprocessing
- Блокнот: Module 2 Notebook
- Материал для самостоятельного изучения: Module 2 Resources
- Vídeo: Overview of Feature Selection Methods
- Vídeo: Filter Methods
- Vídeo: Wrapper Methods
- Тренировочный тест: Feature Selection Methods
- Vídeo: Evaluation Schemes
- Тренировочный тест: Evaluation Schemes
- Vídeo: Selecting Differentially Expressed Genes
- Тренировочный тест: Differentially Expressed Genes
- Vídeo: Heatmaps
- Тренировочный тест: Heatmaps
- Vídeo: R Scripts for Feature Selection
- Блокнот: Module 3 Notebook
- Материал для самостоятельного изучения: Notebook Module 3 Tutorial
- Материал для самостоятельного изучения: Jupyter Notebooks Essentials
- Vídeo: Jupyter Notebooks 101
- Вопрос для обсуждения: Module 3 Discussion
- Материал для самостоятельного изучения: Module 3 R Finding Differentially Expressed Genes
- Блокнот: Module 3 Notebook
- Материал для самостоятельного изучения: Module 3 Resources
- Vídeo: Overview of Classification and Prediction Methods
- Тренировочный тест: Overview
- Vídeo: Classification Methods Based on Analogy
- Тренировочный тест: Classification with Analogy
- Vídeo: Classification Methods Based on Rules
- Тренировочный тест: Classification based on Rules
- Vídeo: Classification Methods Based on Neural Networks
- Тренировочный тест: Classification with Neural Networks
- Vídeo: Classification Methods Based on Statistics
- Тренировочный тест: Classification based on Statistics
- Vídeo: Classification Methods Based on Probabilities
- Тренировочный тест: Classification based on Probabilities
- Vídeo: Prediction Methods
- Тренировочный тест: Prediction Models
- Vídeo: Evaluation Schemes
- Тренировочный тест: Evaluation Schemes
- Vídeo: Prediction Workflow
- Vídeo: R Scripts for Prediction
- Блокнот: Module 4 Notebook
- Материал для самостоятельного изучения: Jupyter Notebooks Essentials
- Vídeo: Jupyter Notebooks 101
- Материал для самостоятельного изучения: Notebook Module 4 Tutorial
- Вопрос для обсуждения: Module 4 Discussion
- Материал для самостоятельного изучения: Module 4 R Predicting Diseases from Genes
- Материал для самостоятельного изучения: Module 4 Resources
- Vídeo: Overview of Gene Alterations
- Тренировочный тест: Gene Alterations
- Vídeo: Genetic Mutations
- Vídeo: Finding Genetic Mutations
- Тренировочный тест: Gene Mutations
- Vídeo: Methylation
- Тренировочный тест: Methylation
- Vídeo: Copy Number Alterations
- Тренировочный тест: Copy Number Alterations
- Vídeo: Genomic Alterations and Gene Expressions
- Тренировочный тест: Genomic Alterations and Gene Expressions
- Vídeo: R Scripts for Gene Alterations
- Блокнот: Module 5 Notebook
- Vídeo: Jupyter Notebooks 101
- Материал для самостоятельного изучения: Notebook Module 5 Tutorial
- Материал для самостоятельного изучения: Jupyter Notebooks Essentials
- Вопрос для обсуждения: Module 5 Discussion
- Тренировочный тест: Module 5 Quiz (Temporary)
- Материал для самостоятельного изучения: Module 5 R Gene Alterations
- Материал для самостоятельного изучения: Module 5 Resources
- Vídeo: Overview of Clustering Methods
- Vídeo: Similarity Assessment
- Тренировочный тест: Clustering
- Vídeo: Clustering with KMeans
- Vídeo: Density Based Clustering
- Vídeo: Hierarchical Clustering
- Тренировочный тест: Clustering Methods
- Vídeo: Pathway Analysis
- Vídeo: Pathway Discovery
- Vídeo: Pathway Visualization
- Тренировочный тест: Pathways
- Vídeo: R Scripts for Clustering and Pathway Analysis
- Блокнот: Module 6 Notebook
- Vídeo: Jupyter Notebooks 101
- Материал для самостоятельного изучения: Jupyter Notebooks Essentials
- Материал для самостоятельного изучения: Notebook Module 6 Tutorial
- Вопрос для обсуждения: Module 6 Discussion
- Материал для самостоятельного изучения: Module 6 R Clustering and Pathways
- Материал для самостоятельного изучения: Module 6 Resources
- Vídeo: Concluding Remarks
- Материал для самостоятельного изучения: Acknowledgements
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| Comprar curso | |
|---|---|
| Acesso aos materiais do curso | Disponível |
| Acesso a materiais valendo nota | Disponível |
| Receba uma nota final | Disponível |
| Obtenha um Certificado de Curso compartilhável | Disponível |
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.
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.
AE
I feel like I wanted to have practice with the material that was covered in the lecture