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Nicholas James Provart
The past decade has seen a vast increase in the amount of data available to biologists, driven by the dramatic decrease in cost and concomitant rise in throughput of various next-generation sequencing technologies, such that a project unimaginable 10 years ago was recently proposed, the Earth BioGenomes Project, which aims to sequence the genomes of all eukaryotic species on the planet within the next 10 years. So while data are no longer limiting, accessing and interpreting those data has become a bottleneck. One important aspect of interpreting data is data visualization. This course introduces theoretical topics in data visualization through mini-lectures, and applied aspects in the form of hands-on labs. The labs use both web-based tools and R, so students at all computer skill levels can benefit. Syllabus may be viewed at https://tinyurl.com/DataViz4GenomeBio.
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What's inside
Syllabus
Week 1
In this module we'll cover 3 straightforward approaches for generating simple plots. As we'll see in the lab, often visualizing datasets can help us see the overall shape of the data that might not be captured in descriptive statistics like mean and standard deviation. Plotting datasets is also a useful way to identify outliers. In the mini-lectures we go over some common biological data visualization paradigms and more generally what the common chart types are, and we also talk about the context and grammar of data visualization.
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Syllabus
Week 1
In this module we'll cover 3 straightforward approaches for generating simple plots. As we'll see in the lab, often visualizing datasets can help us see the overall shape of the data that might not be captured in descriptive statistics like mean and standard deviation. Plotting datasets is also a useful way to identify outliers. In the mini-lectures we go over some common biological data visualization paradigms and more generally what the common chart types are, and we also talk about the context and grammar of data visualization.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Assumes a base level of comfort with command-line software and programming
Offers a great overview of data visualization
Places particular emphasis on the analysis of biological datasets and omics data
Uses Bioconductor R packages
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Reviews summary
Practical genomic data visualization
According to learners, "Data Visualization for Genome Biology" offers a largely positive and highly practical approach to interpreting complex biological data. Students frequently commend the well-structured, hands-on labs, particularly those utilizing R for genomic analysis, which are considered the strongest part of the course. Reviewers highlight the course's direct relevance to real-world genomic data and the clear explanations provided by the instructor, making complex topics digestible. While it provides a solid foundation and covers a broad range of visualization tools, some learners with limited prior R experience found the pace challenging, suggesting a potential prerequisite knowledge gap for absolute beginners in coding.
Covers a wide array of relevant visualization tools.
"The mix of web-based tools and R was useful, catering to different needs."
"Good overview of data visualization techniques relevant to genomics."
"Perhaps more emphasis on D3 or custom visualizations would be great for future iterations, but current content is solid."
Instructor explains complex biological concepts clearly.
"The instructor's explanations were clear and concise, making complex topics digestible."
"Excellent course... The instructor clearly knows their stuff and explains complex topics simply."
"The explanations of concepts like Gene Ontology and differential expression were very clear."
Builds strong R skills for advanced data visualization.
"I particularly appreciated the modules on gene expression and GO analysis using R."
"especially the hands-on sessions with R for heatmaps and GO enrichment."
"I learned so much about visualizing gene expression and protein interaction networks."
Highly relevant for visualizing diverse biological data.
"This course was exactly what I needed to bridge the gap... practical data visualization in genomics."
"The focus on real-world genomic data and the practical application of various visualization methods was invaluable."
"I gained a lot of insight into how to effectively visualize genomic data."
Offers invaluable hands-on experience with genomic data.
"The labs are incredibly well-structured, especially those using R."
"The hands-on coding and projects are the strongest part of the course for me."
"The practical exercises are key. I finally feel confident in visualizing my RNA-seq data."
May require prior R experience for smoother progress.
"I found it quite challenging without a stronger background in R. While it says 'all computer skill levels', the R sections ramp up quickly."
"The pace felt a bit rushed. I was hoping for more deep dives into advanced R visualization packages."
"Be prepared to supplement with external resources if you want to master specific tools."
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 Data Visualization for Genome Biology with these
activities:
Data Visualization Practice Drills
Show steps
Reinforce your understanding of data visualization techniques through hands-on practice exercises.
Show steps
-
Access online visualization tools like PlotsOfDifferences or Gapminder and explore different data sets.
-
Generate and interpret basic plots (e.g., histograms, scatterplots, line graphs) to identify patterns and trends.
-
Experiment with color schemes, chart types, and annotations to enhance the clarity of your visualizations.
Create different types of data visualizations
Show steps
Creating different types of data visualizations will provide hands-on practice, improving proficiency in selecting the most appropriate visual encodings and chart types for different types of data.
Browse courses on
Chart Creation
Show steps
-
Choose a dataset and create a variety of visualizations, including bar charts, line charts, and scatter plots.
-
Experiment with different data visualization tools, such as Tableau, Power BI, or Google Data Studio.
-
Share your visualizations with others and get feedback.
Advanced Data Visualization Tutorials
Show steps
Enhance your understanding and skills in advanced data visualization techniques by following guided tutorials.
Browse courses on
Data Visualization Best Practices
Show steps
-
Identify online tutorials or courses on specific data visualization tools or techniques (e.g., R's ggplot2, Cytoscape).
-
Work through the tutorials step-by-step, experimenting with the featured techniques and applying them to your own data.
Two other activities
Expand to see all activities and additional details
Show all five activities
Interactive Data Visualization Project
Show steps
Consolidate your learning by creating an interactive data visualization project that showcases your skills.
Browse courses on
Interactive Data Visualization
Show steps
-
Identify a biological data set of interest and explore it thoroughly.
-
Design and develop an interactive data visualization using a tool like D3 or Plotly.
-
Present your visualization, explaining the insights you gained and the techniques you used.
Develop a data visualization dashboard
Show steps
Developing a data visualization dashboard will provide experience in designing and implementing interactive visualizations, allowing students to apply their skills in a practical context.
Browse courses on
Interactive Data Visualization
Show steps
-
Identify a dataset and determine the key insights that you want to communicate.
-
Design the layout and structure of your dashboard.
-
Select and implement appropriate visualizations.
-
Make your dashboard interactive by adding filters, tooltips, and other features.
-
Deploy your dashboard and share it with others.
Data Visualization Practice Drills
Show steps
Reinforce your understanding of data visualization techniques through hands-on practice exercises.
Show steps
Show steps
Show steps
- Access online visualization tools like PlotsOfDifferences or Gapminder and explore different data sets.
- Generate and interpret basic plots (e.g., histograms, scatterplots, line graphs) to identify patterns and trends.
- Experiment with color schemes, chart types, and annotations to enhance the clarity of your visualizations.
Create different types of data visualizations
Show steps
Creating different types of data visualizations will provide hands-on practice, improving proficiency in selecting the most appropriate visual encodings and chart types for different types of data.
Browse courses on
Chart Creation
Show steps
Show steps
Show steps
- Choose a dataset and create a variety of visualizations, including bar charts, line charts, and scatter plots.
- Experiment with different data visualization tools, such as Tableau, Power BI, or Google Data Studio.
- Share your visualizations with others and get feedback.
Advanced Data Visualization Tutorials
Show steps
Enhance your understanding and skills in advanced data visualization techniques by following guided tutorials.
Browse courses on
Data Visualization Best Practices
Show steps
Show steps
Show steps
- Identify online tutorials or courses on specific data visualization tools or techniques (e.g., R's ggplot2, Cytoscape).
- Work through the tutorials step-by-step, experimenting with the featured techniques and applying them to your own data.
Two other activities
Expand to see all activities and additional details
Show all five activities
Interactive Data Visualization Project
Show steps
Consolidate your learning by creating an interactive data visualization project that showcases your skills.
Browse courses on
Interactive Data Visualization
Show steps
Show steps
Show steps
- Identify a biological data set of interest and explore it thoroughly.
- Design and develop an interactive data visualization using a tool like D3 or Plotly.
- Present your visualization, explaining the insights you gained and the techniques you used.
Develop a data visualization dashboard
Show steps
Developing a data visualization dashboard will provide experience in designing and implementing interactive visualizations, allowing students to apply their skills in a practical context.
Browse courses on
Interactive Data Visualization
Show steps
Show steps
Show steps
- Identify a dataset and determine the key insights that you want to communicate.
- Design the layout and structure of your dashboard.
- Select and implement appropriate visualizations.
- Make your dashboard interactive by adding filters, tooltips, and other features.
- Deploy your dashboard and share it with others.
Career center
Learners who complete Data Visualization for Genome Biology will develop knowledge and skills
that may be useful to these careers:
Bioinformatician
Bioinformatician
Bioinformaticians develop and use computational tools to analyze biological data, such as gene sequences and protein structures. This course, Data Visualization for Genome Biology, can be particularly helpful for bioinformaticians because it teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help bioinformaticians to identify patterns and trends in data, and to make inferences about biological processes. Additionally, this course can help bioinformaticians to communicate their findings to other scientists and to the public.
Computational Biologist
Computational Biologist
Computational biologists use computer science and mathematics to study biological systems. This course, Data Visualization for Genome Biology, can be particularly helpful for computational biologists because it teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help computational biologists to identify patterns and trends in data, and to make inferences about biological processes. Additionally, this course can help computational biologists to communicate their findings to other scientists and to the public.
Data Scientist
Data Scientist
Data scientists use data to solve problems and make predictions. This course, Data Visualization for Genome Biology, can be particularly helpful for data scientists who work in the field of biology or medicine. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help data scientists to identify patterns and trends in data, and to make inferences about biological processes. Additionally, this course can help data scientists to communicate their findings to other scientists and to the public.
Geneticist
Geneticist
Geneticists study genes and their role in health and disease. This course, Data Visualization for Genome Biology, can be particularly helpful for geneticists because it teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help geneticists to identify patterns and trends in data, and to make inferences about gene function. Additionally, this course can help geneticists to communicate their findings to other scientists and to the public.
Biostatistician
Biostatistician
Biostatisticians use statistics to analyze biological data. This course, Data Visualization for Genome Biology, can be particularly helpful for biostatisticians because it teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help biostatisticians to identify patterns and trends in data, and to make inferences about biological processes. Additionally, this course can help biostatisticians to communicate their findings to other scientists and to the public.
Medical Doctor
Medical Doctor
Medical doctors diagnose and treat diseases. This course, Data Visualization for Genome Biology, can be particularly helpful for medical doctors who are interested in genomics or personalized medicine. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help medical doctors to make more informed decisions about patient care.
Health Scientist
Health Scientist
Health scientists conduct research to improve human health. This course, Data Visualization for Genome Biology, can be particularly helpful for health scientists who are interested in genomics or personalized medicine. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help health scientists to make more informed decisions about research.
Epidemiologist
Epidemiologist
Epidemiologists study the causes and spread of diseases. This course, Data Visualization for Genome Biology, can be particularly helpful for epidemiologists who are interested in genomic epidemiology. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help epidemiologists to identify patterns and trends in data, and to make inferences about the causes and spread of diseases.
Science Teacher
Science Teacher
Science teachers teach science to students. This course, Data Visualization for Genome Biology, can be particularly helpful for science teachers who teach biology or genetics. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help science teachers to make their lessons more engaging and effective.
Science Communicator
Science Communicator
Science communicators translate scientific information into a form that is accessible to the public. This course, Data Visualization for Genome Biology, can be particularly helpful for science communicators who want to communicate about genomics or personalized medicine. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help science communicators to create more informative and engaging content.
Policy Advisor
Policy Advisor
Policy advisors advise policymakers on the development and implementation of policies. This course, Data Visualization for Genome Biology, can be particularly helpful for policy advisors who work on health or science policy. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help policy advisors to make more informed recommendations to policymakers.
Science Writer
Science Writer
Science writers write about science for the public. This course, Data Visualization for Genome Biology, can be particularly helpful for science writers who want to write about genomics or personalized medicine. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help science writers to create more informative and engaging articles.
Science Editor
Science Editor
Science editors edit scientific writing. This course, Data Visualization for Genome Biology, can be particularly helpful for science editors who work on scientific journals or books. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help science editors to make more informed decisions about what to publish.
Patent Examiner
Patent Examiner
Patent examiners examine patent applications to determine if they meet the criteria for a patent. This course, Data Visualization for Genome Biology, can be particularly helpful for patent examiners who work on patents related to genomics or personalized medicine. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help patent examiners to make more informed decisions about whether to grant a patent.
Intellectual Property Attorney
Intellectual Property Attorney
Intellectual property attorneys help their clients protect their intellectual property rights. This course, Data Visualization for Genome Biology, can be particularly helpful for intellectual property attorneys who work on patents related to genomics or personalized medicine. The course teaches how to visualize biological data in a way that makes it easier to understand and interpret. This can help intellectual property attorneys to more effectively represent their clients.
For more career information including salaries, visit:
OpenCourser.com/course/nc7rcu/data
Reading list
We've selected 12 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
Data Visualization for Genome Biology.
Save
Classic work on data visualization. It provides a wealth of information on how to create effective visualizations, and must-read for anyone who wants to learn more about data visualization.
Classic work on data visualization. It provides a detailed overview of the different graphical methods that can be used to analyze data. Useful as either a textbook or a reference book, this book should be on every data scientist's bookshelf.
Provides a comprehensive overview of data visualization techniques. It covers a wide range of topics, from the basics of data visualization to advanced techniques for creating interactive visualizations.
Practical guide to creating beautiful visualizations. It covers a wide range of topics, from the basics of design to advanced techniques for creating interactive visualizations. Useful for any level of designer, and an excellent reference guide.
Classic work on data visualization. It provides a theoretical framework for understanding how to create effective visualizations, and will give you a deep understanding across the history, theory, and applications of data visualization.
Save
Collection of essays on the topic of visual complexity. It explores the different ways in which we can visualize complex data, and provides a wealth of inspiration for anyone who wants to create more effective visualizations.
Provides a comprehensive overview of the field of genomics. It covers a wide range of topics, from the basics of DNA sequencing to the latest advances in genome editing. This book will be of interest to those who are involved in research on genomes or who wish to understand the impact of genomics on science and medicine.
Provides a hands-on approach to using the popular Python and JavaScript libraries for data visualization. In particular, this book covers using the popular Bokeh library to create interactive visualizations.
Provides a comprehensive overview of the field of bioinformatics. Suitable for beginners, this book covers a wide range of topics, from the basics of DNA sequencing to the latest advances in genome editing. Will be of interest to those who are involved in the field of bioinformatics or those who wish to understand how bioinformatics is used to analyze and interpret biological data.
Practical guide to choosing the right chart for your data. It covers a wide range of chart types, and provides guidance on how to use them effectively. Useful for anyone who wants to communicate data in a clear and concise way.
Provides a comprehensive overview of the field of molecular biology. It covers a wide range of topics, from the basics of DNA structure to the latest advances in gene editing. Essential reading for anyone who is interested in understanding the fundamental principles of molecular biology.
Provides a comprehensive overview of the field of biostatistics. It covers a wide range of topics, from the basics of probability to the latest advances in statistical modeling. Aimed at a general audience with little or no prior knowledge of statistics, this book will be of interest to those who are involved in research on biological or health sciences or who wish to understand the role of statistics in these fields.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/nc7rcu/data
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Rating
4.8
Effort
15 hours to complete
Level
Intermediate
Via
Coursera
Institution
University of Toronto
Instructor
Nicholas James Provart
Language
English
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