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Fundamental Skills in Bioinformatics
Vincenzo Lagani,
David Gomez-Cabrero,
Robert Lehman,
Zafer Ali,
and
Jesper Tegner
Vincenzo Lagani,
David Gomez-Cabrero,
Robert Lehman,
Zafer Ali,
and
Jesper Tegner
The course provides a broad and mainly practical overview of fundamental skills for bioinformatics (and, in general, data analysis). The aim is to support the simultaneous development of quantitative and programming skills for biological and biomedical students with little or no background in programming or quantitative analysis.
Read more
The course provides a broad and mainly practical overview of fundamental skills for bioinformatics (and, in general, data analysis). The aim is to support the simultaneous development of quantitative and programming skills for biological and biomedical students with little or no background in programming or quantitative analysis.
Read more
The course provides a broad and mainly practical overview of fundamental skills for bioinformatics (and, in general, data analysis). The aim is to support the simultaneous development of quantitative and programming skills for biological and biomedical students with little or no background in programming or quantitative analysis.
Through the course, the student will develop the necessary practical skills to conduct basic data analysis. Most importantly, participants will learn long-term skills in programming (and data analysis) and the guidelines for improving their knowledge on it. The course will include Programming in R, programming in Python, Unix server, and reviewing basic concepts of statistics.
Register for this course and see more details by visiting:
OpenCourser.com/course/klacyq/fundamental
What's inside
Syllabus
Module 1: Introduction to Programming (using R)
The first module will explore the basics of programming through R and this will include: working in R and RStudio, understanding data types, loops and ifs. Additionally, the module will provide an introduction to RMarkDown as a tool for sharing code that we will use in the coding lectures.
Read more
Syllabus
Module 1: Introduction to Programming (using R)
The first module will explore the basics of programming through R and this will include: working in R and RStudio, understanding data types, loops and ifs. Additionally, the module will provide an introduction to RMarkDown as a tool for sharing code that we will use in the coding lectures.
Read more
Module 2: Introduction to Programming II (using R)
The second module will focus on two aims. Firstly, to master the use of logical values and vectors and its applications in quality control. Secondly, to practice the programming skills while learning how to perform basic statistical analysis. This will include: explorative data analysis, correlation, linear models, T-test, and ANOVA. Finally, we will explore the available resources for R programming.
Module 3: Programming in Python
The third module will provide the basics of the Python programming language. First, the module will compare Python and R language and learn the programming syntax of Python. Second, the module will work with two key Python modules: pandas and numpy.
Module 4: Bioinformatics case study - RNA-seq bulk and single-cell data analysis
The final module will focus on applying knowledge and understanding of programming in the analysis of real RNA-seq data. R will be used for analysing of bulk RNA-seq and Python for single- cell RNA-seq. The results of both analyses will then be integrated. Finally, the module will provide insights in how to gain deeper knowledge and skills in R.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Designed for biology and biomedical learners who want to enhance their quantitative and programming proficiency
Suitable for beginners with minimal prior knowledge in programming or data analysis
Empowers learners to develop long-term skills in programming and data analysis, establishing a strong foundation for future growth
Imparts practical skills in data analysis through a series of hands-on exercises
Covers essential programming languages, including R and Python, while emphasizing their practical applications in bioinformatics
Provides guidance for learners to continuously expand their knowledge and skills in programming and data analysis
Save this course
Save Fundamental Skills in Bioinformatics to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Fundamental Skills in Bioinformatics with these
activities:
Review R programming basics
Show steps
This activity will help you refresh your understanding of R programming basics, which will be essential for success in this course.
Browse courses on
R Programming
Show steps
-
Review online tutorials on R programming
-
Complete practice exercises on R programming
-
Create a small R program to analyze a simple dataset
Review programming basics
Show steps
Review the fundamentals of programming language, particularly R and Python, for easier understanding of the concepts discussed in the course.
Browse courses on
Programming
Show steps
-
Go over essential programming concepts such as variables, data types, and control flow.
-
Practice solving simple coding problems to reinforce your understanding.
-
Review basic statistical concepts such as mean, median, and standard deviation.
Create a personalized resource guide
Show steps
Organize and expand your learning materials by compiling a resource guide with relevant articles, tutorials, and tools.
Show steps
-
Gather and curate online resources, such as articles, tutorials, and videos.
-
Organize the resources into categories or topics.
-
Add your own notes and annotations to enhance your understanding.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Read 'Bioinformatics Data Skills' by Vince Buffalo
Show steps
Gain a deeper understanding of bioinformatics data analysis and visualization techniques by reading this recommended book.
View
Bioinformatics Data Skills: Reproducible and...
on Amazon
Show steps
-
Read the book and take notes on key concepts and methods.
-
Work through the exercises and examples provided in the book.
-
Apply the techniques you learn to your own bioinformatics projects.
Solve Python programming challenges
Show steps
This activity will help you develop your Python programming skills by solving challenging problems.
Browse courses on
Python Programming
Show steps
-
Find online Python programming challenges
-
Attempt to solve the challenges on your own
-
Review solutions to the challenges and identify areas for improvement
Join a bioinformatics study group
Show steps
Engage with fellow students and discuss course concepts, share resources, and help each other with assignments.
Show steps
-
Find or create a study group with other students taking the course.
-
Meet regularly to discuss the course material and work on assignments together.
Solve programming exercises
Show steps
Strengthen your programming skills by solving coding exercises and challenges related to the topics covered in the course.
Show steps
-
Find online coding platforms or resources that provide practice problems.
-
Start with easier exercises and gradually increase the difficulty level.
-
Debug your code and try to find efficient solutions.
Write a blog post on a bioinformatics topic
Show steps
This activity will help you solidify your understanding of a bioinformatics topic by writing a blog post about it.
Browse courses on
Bioinformatics
Show steps
-
Choose a bioinformatics topic that you are interested in
-
Research the topic and gather information
-
Write a blog post that explains the topic in a clear and concise way
Explore advanced programming techniques
Show steps
Enhance your knowledge by exploring advanced programming techniques and concepts that can complement the course material.
Show steps
-
Identify advanced programming topics that you want to learn.
-
Find online tutorials, courses, or books that cover those topics.
-
Work through the tutorials and apply the new techniques in your own projects.
Contribute to open-source bioinformatics projects
Show steps
Enhance your practical skills and contribute to the bioinformatics community by participating in open-source projects.
Show steps
-
Identify open-source bioinformatics projects that align with your interests.
-
Join the project and start contributing code, documentation, or other resources.
Develop a bioinformatics project
Show steps
Apply the skills learned in the course to create a bioinformatics project that demonstrates your understanding and proficiency.
Show steps
-
Identify a biological question or problem that you want to address.
-
Gather and prepare the necessary data.
-
Design and implement a bioinformatics workflow to analyze the data.
-
Interpret the results and draw conclusions.
Review R programming basics
Show steps
This activity will help you refresh your understanding of R programming basics, which will be essential for success in this course.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Review online tutorials on R programming
- Complete practice exercises on R programming
- Create a small R program to analyze a simple dataset
Review programming basics
Show steps
Review the fundamentals of programming language, particularly R and Python, for easier understanding of the concepts discussed in the course.
Browse courses on
Programming
Show steps
Show steps
Show steps
- Go over essential programming concepts such as variables, data types, and control flow.
- Practice solving simple coding problems to reinforce your understanding.
- Review basic statistical concepts such as mean, median, and standard deviation.
Create a personalized resource guide
Show steps
Organize and expand your learning materials by compiling a resource guide with relevant articles, tutorials, and tools.
Show steps
Show steps
Show steps
- Gather and curate online resources, such as articles, tutorials, and videos.
- Organize the resources into categories or topics.
- Add your own notes and annotations to enhance your understanding.
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Read 'Bioinformatics Data Skills' by Vince Buffalo
Show steps
Gain a deeper understanding of bioinformatics data analysis and visualization techniques by reading this recommended book.
View
Bioinformatics Data Skills: Reproducible and...
on Amazon
Show steps
Show steps
Show steps
- Read the book and take notes on key concepts and methods.
- Work through the exercises and examples provided in the book.
- Apply the techniques you learn to your own bioinformatics projects.
Solve Python programming challenges
Show steps
This activity will help you develop your Python programming skills by solving challenging problems.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Find online Python programming challenges
- Attempt to solve the challenges on your own
- Review solutions to the challenges and identify areas for improvement
Join a bioinformatics study group
Show steps
Engage with fellow students and discuss course concepts, share resources, and help each other with assignments.
Show steps
Show steps
Show steps
- Find or create a study group with other students taking the course.
- Meet regularly to discuss the course material and work on assignments together.
Solve programming exercises
Show steps
Strengthen your programming skills by solving coding exercises and challenges related to the topics covered in the course.
Show steps
Show steps
Show steps
- Find online coding platforms or resources that provide practice problems.
- Start with easier exercises and gradually increase the difficulty level.
- Debug your code and try to find efficient solutions.
Write a blog post on a bioinformatics topic
Show steps
This activity will help you solidify your understanding of a bioinformatics topic by writing a blog post about it.
Browse courses on
Bioinformatics
Show steps
Show steps
Show steps
- Choose a bioinformatics topic that you are interested in
- Research the topic and gather information
- Write a blog post that explains the topic in a clear and concise way
Explore advanced programming techniques
Show steps
Enhance your knowledge by exploring advanced programming techniques and concepts that can complement the course material.
Show steps
Show steps
Show steps
- Identify advanced programming topics that you want to learn.
- Find online tutorials, courses, or books that cover those topics.
- Work through the tutorials and apply the new techniques in your own projects.
Contribute to open-source bioinformatics projects
Show steps
Enhance your practical skills and contribute to the bioinformatics community by participating in open-source projects.
Show steps
Show steps
Show steps
- Identify open-source bioinformatics projects that align with your interests.
- Join the project and start contributing code, documentation, or other resources.
Develop a bioinformatics project
Show steps
Apply the skills learned in the course to create a bioinformatics project that demonstrates your understanding and proficiency.
Show steps
Show steps
Show steps
- Identify a biological question or problem that you want to address.
- Gather and prepare the necessary data.
- Design and implement a bioinformatics workflow to analyze the data.
- Interpret the results and draw conclusions.
Career center
Learners who complete Fundamental Skills in Bioinformatics will develop knowledge and skills
that may be useful to these careers:
Bioinformatician
Bioinformatician
Programming in R and Python is essential for a Bioinformatician, whose primary responsibilities include analyzing and interpreting large datasets related to biology and genetics. The skills acquired in this course can provide a solid foundation for working with the necessary tools and technologies used by Bioinformaticians. Furthermore, the focus on data analysis will enable learners to understand and extract meaningful insights from complex biological data.
Data Analyst
Data Analyst
Data Analysts play a crucial role in analyzing, interpreting, and presenting data to derive meaningful insights. This course provides a comprehensive overview of data analysis techniques, covering programming in R and Python, statistical concepts, and data visualization. These skills are essential for developing a foundation in data analysis, which is highly valued in the industry.
Data Scientist
Data Scientist
Data Scientists bridge the gap between data and business, leveraging their expertise in data analysis and programming to solve complex problems. This course offers a solid introduction to data analysis using R and Python, along with essential statistical concepts. By developing these skills, learners can lay the groundwork for a career as a Data Scientist and contribute effectively to data-driven decision-making.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning systems to solve real-world problems. This course provides a foundation in programming, data analysis, and statistical concepts, which are fundamental for understanding and applying machine learning algorithms. The skills gained in this course will be valuable for individuals aspiring to become Machine Learning Engineers.
Research Analyst
Research Analyst
Research Analysts conduct in-depth research and analysis to provide insights and recommendations on various industries, companies, and markets. This course offers a comprehensive overview of data analysis techniques, programming, and statistical concepts, which are essential for conducting rigorous research and analyzing data effectively. The skills gained in this course will be highly valuable for individuals pursuing a career as a Research Analyst.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts (Quants) use mathematical and statistical models to analyze financial data and make investment decisions. This course provides a solid foundation in programming and data analysis, which are essential skills for Quants. By mastering these skills, learners can enhance their understanding of financial data and develop the analytical abilities required for success in this field.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course provides a foundation in programming, data structures, and algorithms, which are essential for Software Engineers. By mastering these skills, learners can enhance their understanding of software development principles and best practices, preparing them for a successful career in software engineering.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data to derive meaningful insights and inform decision-making. This course provides a comprehensive overview of statistical concepts and data analysis techniques, which are essential for a Statistician. By mastering these skills, learners can develop a strong foundation in statistical analysis and prepare for a career in this field.
Health Data Analyst
Health Data Analyst
Health Data Analysts leverage data analysis techniques to improve patient care and healthcare systems. This course provides a foundation in programming, data analysis, and statistical concepts, which are essential for Health Data Analysts. By mastering these skills, learners can develop the expertise required to analyze healthcare data and contribute to evidence-based decision-making in the healthcare industry.
Business Analyst
Business Analyst
Business Analysts bridge the gap between business and technology, analyzing business processes and identifying areas for improvement. This course provides a foundation in data analysis, programming, and statistical concepts, which are valuable for Business Analysts. By mastering these skills, learners can develop a deeper understanding of data-driven decision-making and enhance their ability to contribute to business strategy.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure to support data analysis and decision-making. This course provides a foundation in data management, programming, and data analysis, which are essential for Data Engineers. By mastering these skills, learners can develop the technical expertise required to succeed in this growing field.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to analyze biological data and draw conclusions about health and disease. This course provides a foundation in programming, data analysis, and statistical concepts, which are essential for Biostatisticians. By mastering these skills, learners can develop the expertise required to analyze complex biological data and contribute to advancements in biomedical research.
Epidemiologist
Epidemiologist
Epidemiologists investigate the distribution and determinants of health-related events and diseases in populations. This course provides a foundation in programming, data analysis, and statistical concepts, which are valuable for Epidemiologists. By mastering these skills, learners can develop the analytical abilities required to identify and analyze patterns of disease occurrence and contribute to public health initiatives.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data and make recommendations on investments and financial decisions. This course provides a foundation in programming, data analysis, and statistical concepts, which are valuable for Financial Analysts. By mastering these skills, learners can develop a deeper understanding of financial data and enhance their ability to make informed investment decisions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex problems in various industries. This course provides a foundation in programming, data analysis, and statistical concepts, which are valuable for Operations Research Analysts. By mastering these skills, learners can develop the analytical abilities required to identify and solve operational challenges effectively.
For more career information including salaries, visit:
OpenCourser.com/course/klacyq/fundamental
Reading list
We've selected nine 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
Fundamental Skills in Bioinformatics.
Save
Focuses specifically on Python programming for bioinformatics and provides a thorough introduction to the Python language and its applications in bioinformatics. It covers topics such as sequence analysis, data visualization, and machine learning, and is suitable for both beginners and experienced programmers.
Save
Provides a comprehensive introduction to data science using R. It covers topics such as data manipulation, data visualization, and statistical modeling, and is suitable for both beginners and experienced programmers.
Provides a comprehensive overview of bioinformatics and functional genomics. It covers topics such as DNA sequencing, gene expression analysis, and comparative genomics, and is suitable for both beginners and experienced researchers.
Provides a comprehensive overview of bioinformatics and its applications. It covers topics such as sequence analysis, gene expression analysis, and proteomics, and is suitable for both beginners and experienced researchers.
Save
Provides a comprehensive overview of bioinformatics and its applications. It covers topics such as sequence analysis, gene expression analysis, and genome analysis, and is suitable for both beginners and experienced researchers.
Provides a practical guide to programming for bioinformatics using Python. It covers a wide range of topics, including sequence analysis, data manipulation, and statistical analysis, and is written in a clear and concise style.
Provides a practical guide to data analysis using Python. It covers topics such as data manipulation, data visualization, and statistical modeling, and is suitable for both beginners and experienced programmers.
Provides a comprehensive overview of algorithms for computational biology. It covers topics such as sequence alignment, gene finding, and phylogenetic analysis, and is suitable for readers with a strong background in computer science.
Provides a comprehensive overview of statistical methods used in bioinformatics. It covers topics such as probability theory, statistical inference, and data analysis, and is suitable for readers with a strong background in mathematics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/klacyq/fundamental
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Effort
4-week course. On average the student will require approximately between 6 to 8 hours per week.
Level
Introductory
Via
Coursera
Institution
King Abdullah University of Science and Technology
Instructors
Vincenzo Lagani
David Gomez-Cabrero
Robert Lehman
Zafer Ali
Jesper Tegner
Language
English
Good to know
Designed for biology and biomedical learners who want to enhance their quantitative and programming proficiency
Suitable for beginners with minimal prior knowledge in programming or data analysis
Empowers learners to develop long-term skills in programming and data analysis, establishing a strong foundation for future growth
Imparts practical skills in data analysis through a series of hands-on exercises
Covers essential programming languages, including R and Python, while emphasizing their practical applications in bioinformatics
Provides guidance for learners to continuously expand their knowledge and skills in programming and data analysis
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