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Bioconductor for Genomic Data Science
Kasper Daniel Hansen, PhD
Kasper Daniel Hansen, PhD
Learn to use tools from the Bioconductor project to perform analysis of genomic data. This is the fifth course in the Genomic Big Data Specialization from Johns Hopkins University.
Register for this course and see more details by visiting:
OpenCourser.com/course/k3bfoi/bioconductor
What's inside
Syllabus
Week One
The class will cover how to install and use Bioconductor software. We will discuss common data structures, including ExpressionSets, SummarizedExperiment and GRanges used across several types of analyses.
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Syllabus
Week One
The class will cover how to install and use Bioconductor software. We will discuss common data structures, including ExpressionSets, SummarizedExperiment and GRanges used across several types of analyses.
Read more
Week Two
In this week we will learn how to represent and compute on biological sequences, both at the whole-genome level and at the level of millions of short reads.
Week Three
In this week we will cover Basic Data Types, ExpressionSet, biomaRt, and R S4.
Week Four
In this week, we will cover Getting data in Bioconductor, Rsamtools, oligo, limma, and minfi
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Meets a need in the industry for skilled workers familiar with Bioconductor
Taught by an expert with advanced degrees - PhD in the relevant field
Taught by an experienced instructor - Kasper Daniel Hansen
Taught by experienced instructors from Johns Hopkins University
Quantitative orientation helps learners who prefer to learn through data
Taught by an instructor with extensive background and knowledge in Genomics
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Save Bioconductor for Genomic Data Science to your list so you can find it easily later:
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Reviews summary
Bioconductor for genomics data science
According to students, Bioconductor for Genomic Data Science has largely positive reviews. Learners say this course teaches the basics of Bioconductor, including its data types and packages, which are used for the analysis of genomic data. Students widely agree that this course offers engaging assignments and challenging quizzes and exams. There is some concern that this course is best suited for students who already have prior knowledge and experience with R. Learners also mention that this course is not always easy to follow, especially for beginners, and that some of the information is outdated. However, students appreciate the supplementary materials provided by the instructor, which they say are helpful for understanding the course material.
Valuable supplementary materials provided.
"The supplementary materials provided in the lecturer's GitHub are hugely helpful."
"There are ample materials, in the form of slides, videos and code, as well as a textbook whose chapters are in the instructors archives and also available in pdf form on the Internet."
Instructor's accent may be difficult to understand.
"The instructor have some accent, which made me even harder to get into it."
"he doesn't look like a teacher, she looks like a youtuber "explaining the 100 things you don't know about me""
Expect a demanding workload.
"Workload is quite heavy for this course (10-15 hours per week)"
"I recommend against taking this class unless they update it or get someone who you can ask stuff directly."
"could not download many resources due to outdate website or else, the course need update"
Concerns about outdated information and resources.
"The course is outdated."
"This course has a steep learning curve and requires fairly extensive reading outside the lectures"
"It seemed that the target audience is people who had already had comprehensive knowledge in genomics, proteomics and transcriptomics and are already good in R."
Recommended for learners with prior knowledge of R.
"This course is not suitable for beginners."
"I think this course is really useful for those who have been relatively proficient in R but are lack of experience in applying R for genomic analyses."
"Personally speaking, some previous experience in R is necessary for understanding of this course."
Career center
Learners who complete Bioconductor for Genomic Data Science will develop knowledge and skills
that may be useful to these careers:
Genomic Data Scientist
Genomic Data Scientist
This course will help build a foundation for a career as a Genomic Data Scientist. This role analyzes genomic data to identify patterns and trends, and to develop new methods for diagnosing and treating diseases.
Biostatistician
Biostatistician
A Biostatistician designs and analyzes studies to evaluate the effectiveness of new treatments and interventions in the field of medicine. This course will help build a foundation in the statistical methods used in biostatistics.
Bioinformatics Analyst
Bioinformatics Analyst
A Bioinformatics Analyst collects, analyzes, and interprets large datasets to identify patterns and trends in biological data. This course will help build a foundation in the computational and statistical methods used in bioinformatics.
Research Scientist
Research Scientist
Research Scientists conduct experiments and analyze data to develop new knowledge and theories in the field of genomics. This course will help build a foundation in the methods used to conduct research in genomics.
Computational Biologist
Computational Biologist
Computational Biology uses computer science, applied mathematics, statistics, and engineering to analyze and interpret biological data. This course will help build a foundation in the computational methods used in computational biology.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify patterns and trends. This course will help build a foundation in the statistical methods used in data analysis.
Statistician
Statistician
Statisticians develop and apply statistical methods to collect, analyze, interpret, and present data. This course will help build a foundation in the statistical methods used in statistics.
Computer Scientist
Computer Scientist
Computer Scientists design, develop, and implement computer systems and applications. This course will help build a foundation in the computational methods used in computer science.
Systems Analyst
Systems Analyst
Systems Analysts design, develop, and implement computer systems. This course will help build a foundation in the computational methods used in systems analysis.
Software Engineer
Software Engineer
Software Engineers design, develop, and implement software systems. This course will help build a foundation in the computational methods used in software engineering.
Database Administrator
Database Administrator
Database Administrators design, develop, and implement database systems. This course will help build a foundation in the computational methods used in database administration.
Network Administrator
Network Administrator
Network Administrators design, develop, and implement computer networks. This course will help build a foundation in the computational methods used in network administration.
Security Analyst
Security Analyst
Security Analysts design, develop, and implement security systems. This course will help build a foundation in the computational methods used in security analysis.
Web Developer
Web Developer
Web Developers design, develop, and implement websites. This course will help build a foundation in the computational methods used in web development.
Technical Writer
Technical Writer
Technical Writers create documentation for computer systems and applications. This course will help build a foundation in the writing skills used in technical writing.
For more career information including salaries, visit:
OpenCourser.com/course/k3bfoi/bioconductor
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Bioconductor for Genomic Data Science.
Covers the concepts of genomic data science by getting into the biological and statistical foundations, big data technologies, open source tools, and analysis methods for the interpretation of genomic data.
Dives deep into the tools commonly used in bioinformatics data science and data carpentry, providing a deep dive into tools.
As a more advanced but still practical resource, this book helps learners get the most out of the Bioconductor workflow.
Save
Provides a practical approach to analyzing data from next-generation sequencing techniques.
Introduces the fundamental principles of genomics, providing a clear and concise overview of the field.
Provides a broad overview of genomics, proteomics, and bioinformatics, introducing the fundamental concepts and techniques used in these fields.
Provides a comprehensive introduction to bioinformatics, covering a wide range of topics from sequence analysis to protein structure prediction.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/k3bfoi/bioconductor
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Kasper Daniel Hansen, PhD
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Good to know
Meets a need in the industry for skilled workers familiar with Bioconductor
Taught by an expert with advanced degrees - PhD in the relevant field
Taught by an experienced instructor - Kasper Daniel Hansen
Taught by experienced instructors from Johns Hopkins University
Quantitative orientation helps learners who prefer to learn through data
Taught by an instructor with extensive background and knowledge in Genomics
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