Researcher's guide to omic fundamentals from Coursera

The course is intended for students in the biomedical sciences and researchers who have been given data and don’t know what to do with it or would like an overview of the different genomic data types that are out there.

This course is written for individuals who:

- Have genomic data and don’t know what to do with it.

- Want a basic overview of genomic data types.

- Want to find resources for processing and interpreting genomics data.

Goal of this course:

Equip learners with tutorials and resources so they can understand and interpret their genomic data in a way that helps them meet their goals and handle the data properly. This includes helping learners formulate questions they will need to ask others about their data

What is not the goal

Teach learners about choosing parameters or about the ins and outs of every genomic tool they might be interested in. This course is meant to connect people to other resources that will help them with the specifics of their genomic data and help learners have more efficient and fruitful discussions about their data with bioinformatic experts.

The course is intended for students in the biomedical sciences who have been given data and don’t know what to do with it or would like an overview of the different genomic data types that are out there.

What's inside

Syllabus

Introduction

In this module, we cover the basics of what will be covered in this course and what you should expect. Next we get into an overview of what omic data types and their workflows often look like.

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Suitable for beginners in biomedical sciences and researchers

Provides tutorials and resources for understanding genomic data

Covers basic concepts and tools for genomic data analysis

Helps learners ask the right questions about their data

Connects learners to resources for further exploration

Focuses on understanding data rather than specific tools

Reviews summary

Researcher's omic data guide

According to learners, this course is a highly valuable resource for researchers and students in biomedical sciences seeking to understand omic data. Students praise its ability to clearly explain complex concepts and demystify bioinformatics for non-experts. A major strength is its focus on equipping learners to ask the right questions of bioinformaticians and engage in more informed discussions about their data, filling a critical gap. While many found it perfectly aligned with its goals, some learners found the course to be a high-level overview and not sufficient for hands-on analysis, which aligns with the course's explicit disclaimer about not teaching specific tools in depth. Overall, it's considered an excellent guide for navigating the omic landscape.

Valuable resources like a tool glossary are provided for further exploration.

"The resources provided are also very valuable for further exploration."

"I found the Tool Glossary particularly useful."

"The instructor's approach to teaching what questions to ask and where to find resources is very effective."

Complex omic topics are broken down and explained clearly.

"The instructor clearly explains complex concepts and provides practical insights."

"It breaks down complex topics into digestible chunks and truly helps you understand the bigger picture..."

"It simplifies complex bioinformatics concepts without oversimplifying them."

"The explanations are clear and easy to follow."

Empowers non-bioinformaticians to understand and discuss omic data effectively.

"I particularly appreciated the emphasis on understanding the data and knowing what questions to ask bioinformaticians..."

"It demystifies the world of omics for non-bioinformaticians. ...It perfectly aligns with its stated goal of helping you ask the right questions."

"I feel much more confident discussing my data with collaborators now. The focus on what *not* to do and what questions to ask is brilliant."

"It’s exactly what the description says it is: a guide for understanding and having better conversations, not a deep dive into coding or analysis."

Provides a high-level understanding, not hands-on tool mastery.

"I was hoping for more practical, hands-on guidance on using specific tools or analyzing data myself, rather than just knowing what questions to ask."

"While the theoretical overview is decent, it largely serves as a vocabulary lesson rather than a practical guide. If you're looking to actually *do* analysis, this isn't it."

"It's a good starting point if you literally know nothing, but for someone with some prior exposure, it might feel a bit basic."

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 Researcher's guide to omic fundamentals with these activities:

Review Basic Statistics and Probability

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Strengthen your foundation by revisiting essential statistical and probability concepts used in omic data analysis.

Browse courses on Statistics

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Review your notes or textbooks on basic statistics and probability.
Solve practice problems to test your understanding.

Compile a Glossary of Omic Data Terminology

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Deepen your understanding of omic data by creating a comprehensive glossary of key terms and concepts.

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Review course materials and identify unfamiliar terms.
Search for definitions in textbooks, online resources, and scientific papers.
Organize the terms into a structured glossary, such as a spreadsheet or wiki.

Explore the Bioconductor Project

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Reinforce your understanding of omic data workflows by exploring the widely-used Bioconductor Project.

Browse courses on Bioinformatics

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Visit the Bioconductor website and browse the available packages.
Identify a package relevant to your research interests.
Follow the tutorials provided for the package to gain hands-on experience.

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Form a Study Group for Omic Data Analysis

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Enhance your learning by collaborating with classmates and discussing omic data analysis concepts and challenges.

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Identify classmates who share your interests and learning goals.
Establish a regular meeting schedule and meeting format.
Prepare discussion topics and assignments to facilitate knowledge sharing.

Practice Data Wrangling with R and Bioconductor

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Solidify your data manipulation skills by practicing with real-world genomic datasets using R and Bioconductor.

Browse courses on Data Wrangling

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Find a public genomic dataset that aligns with your interests.
Load the data into R using appropriate Bioconductor packages.
Perform data cleaning and preprocessing steps.
Explore the data using visualizations and statistical tests.

Attend a Bioinformatics Workshop

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Gain practical experience and connect with experts by attending a workshop focused on omic data analysis techniques.

Browse courses on Genomics

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Search for upcoming bioinformatics workshops in your area.
Select a workshop that aligns with your interests and skill level.
Actively participate in the workshop and engage with the instructors.

Develop a Data Analysis Plan for an Omic Data Project

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Apply your knowledge by creating a detailed plan for analyzing a real-world omic dataset, ensuring a structured and efficient approach.

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Define the research question and objectives.
Select appropriate data analysis methods and tools.
Outline the steps of the analysis workflow.
Consider potential challenges and mitigation strategies.

Career center

Learners who complete Researcher's guide to omic fundamentals will develop knowledge and skills that may be useful to these careers:

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Researcher's guide to omic fundamentals

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What's inside

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Researcher's omic data guide

Activities

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