Computing for Cancer Informatics from Coursera

One of the key cancer informatics challenges is dealing with and managing the explosion of large data from multiple sources that are often too large to work with on typical personal computers. This course is designed to help researchers and investigators to understand the basics of computing and to familiarize them with various computing options to ultimately help guide their decisions on the topic. This course aims to provide research leaders with awareness and guidance about:

Basic computing terminology

Concepts about how computers and computing systems work

Differences between shared computing resources

Appropriate etiquette for shared computing resources

Computing resources designed for cancer research

Considerations for computing resource decisions

Target audience:

This course is intended for researchers (including postdocs and students) with limited to intermediate experience with informatics research. The conceptual material will also be useful for those in management roles who are collecting data and using informatics pipelines.

Curriculum:

We will provide you with familiarity with fundamental computing terms. We will also discuss relevant concepts about how computers and shared computing resources work. We will explore the differences between various computing resource options, as well as provide guidance on how to make important computing discussions.

This course is part of a series of courses for the Informatics Technology for Cancer Research (ITCR) called the Informatics Technology for Cancer Research Education Resource. This material was created by the ITCR Training Network (ITN) which is a collaborative effort of researchers around the United States to support cancer informatics and data science training through resources, technology, and events. This initiative is funded by the following grant: National Cancer Institute (NCI) UE5 CA254170. Our courses feature tools developed by ITCR Investigators and make it easier for principal investigators, scientists, and analysts to integrate cancer informatics into their workflows. Please see our website at www.itcrtraining.org for more information.

What's inside

Syllabus

Welcome

In this module we will introduce you to how the material will be presented and the goals for the course.

Basic Building Block of Computers

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Introduces computing fundamentals, which is standard in cancer research

Taught by Carrie Wright, PhD, who are recognized for their work in cancer informatics

Examines computing resources designed specifically for cancer research

Develops awareness and guidance about computing resources

Appropriate for researchers with limited to intermediate experience in informatics research

Advises research leaders on computing resource decisions

Reviews summary

Foundational computing for cancer research

According to students who might take this course, learners say it provides a solid foundational understanding of computing concepts crucial for cancer informatics. Many would find the guidance on managing large datasets and insights into shared computing resources particularly valuable. The course likely offers a clear introduction to essential terminology and best practices for computing etiquette, making complex topics accessible. However, some learners with more advanced experience might seek deeper technical dives or more hands-on practical application, as the course focuses primarily on awareness and decision-making guidance rather than advanced skill building.

Emphasizes understanding and decision-making over hands-on technical skills.

"This course is excellent for understanding *what* options exist, but don't expect deep dives into *how* to use specific software."

"I felt it was more about high-level awareness for research leaders rather than for analysts needing to write code."

"While informative, I would have loved more hands-on exercises or coding examples to solidify the concepts introduced."

Suited for those with limited informatics background.

"As someone with limited prior informatics experience, I found the pace and explanations perfect."

"It really helped bridge the gap in my computing knowledge, making it accessible even for a pure biologist."

"If you're new to the informatics side of research, this course provides a gentle but comprehensive introduction to the field."

Offers relevant advice for cancer informatics researchers.

"The sections on shared computing etiquette are very practical for navigating collaborative research environments."

"I appreciated the overview of computing platforms specifically designed for cancer research; it saved me a lot of searching."

"The guidance on data management decisions directly applies to the challenges I face daily with large datasets."

Provides essential foundational computing knowledge.

"I found the explanations of basic computer building blocks incredibly helpful for my research endeavors."

"The course clearly laid out how binary data translates into computations, which was a lightbulb moment for me."

"It simplified complex concepts around computing capacity and common file sizes, making them easy to grasp."

May be too basic for experienced informatics professionals.

"For someone already familiar with high-performance computing, much of this content felt like a review."

"I was hoping for more advanced strategies for optimizing large-scale data processing in cancer research."

"While useful for beginners, I already knew most of what was covered and was looking for more depth in these topics."

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 Computing for Cancer Informatics with these activities:

Review binary data conversion

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Build a stronger foundation for working with binary data.

Browse courses on Binary Data

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Review binary data representations.
Practice converting binary data to decimal and vice versa.

Host a peer study session on binary data concepts

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Enhance your understanding by presenting and discussing binary data concepts.

Browse courses on Binary Data

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Review binary data concepts.
Prepare to present on the topic.
Host the peer study session.

Complete practice exercises on shared computing resources

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Reinforce your understanding of using shared computing resources.

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Find practice exercises or tutorials on shared computing resources.
Work through the exercises or tutorials.

Eight other activities

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Practice working with binary data

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Reinforce understanding of how computers store and process data through practical exercises.

Browse courses on Binary Data

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Solve problems involving binary data conversion
Use online tools or resources to practice working with binary data
Build a simple program that manipulates binary data

Explore shared computing resources

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Enhance familiarity with different types of shared computing resources and their applications.

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Follow online tutorials on using shared computing resources
Attend a workshop or webinar on shared computing
Experiment with using shared computing resources for small tasks

Create a basic workflow using a research platform

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Practice working with research platforms to create a real-world workflow.

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Identify a research platform and follow a tutorial or documentation about using it.
Create a basic workflow using the research platform.

Attend a cancer research conference

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Connect with experts and researchers in the field to learn about cutting-edge advancements and resource.

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Research and identify relevant conferences
Attend talks and presentations on topics related to computing in cancer research
Network with attendees to exchange knowledge and ideas

Develop a data management plan

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Apply course knowledge to create a plan for managing and organizing research data effectively.

Browse courses on Data Management

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Identify data sources and types
Determine storage requirements and strategies
Establish data access and sharing protocols
Document the data management plan

Build familiarity with the Linux command line

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Extend your knowledge by becoming more comfortable with Linux command line.

Browse courses on Linux Command Line

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Find and follow a tutorial on Linux command line basics.
Practice using the commands covered in the tutorial.

Develop a data management plan for your research

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Apply your knowledge to create a plan for managing your research data.

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Research best practices for data management in your field.
Create a data management plan that outlines how you will collect, store, and share your research data.

Develop a computational pipeline for cancer research

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Apply course concepts by building a complete computational pipeline for a specific cancer research project.

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Define the research question and data requirements
Design and implement the computational pipeline
Test and validate the pipeline
Document and share the pipeline

Career center

Learners who complete Computing for Cancer Informatics will develop knowledge and skills that may be useful to these careers:

Computational Biologist

Computational Biologists utilize computational techniques to study and model complex biological systems. They work on projects ranging from understanding disease mechanisms to designing new drugs. The Computing for Cancer Informatics course can provide a valuable introduction to computing principles, binary data processing, and shared computing resources, which are fundamental concepts for Computational Biologists. Understanding these concepts can enable them to effectively use computational tools and resources for their research.

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Bioinformatician

Bioinformaticians play a vital role in analyzing and interpreting large and complex biological datasets, enabling researchers to make meaningful discoveries. The Computing for Cancer Informatics course can provide a solid foundation in computing and data management, helping Bioinformaticians understand how computers process and store biological data. This course also covers shared computing resources and research platforms, which are essential for handling the immense datasets encountered in bioinformatics research.

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Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models. The Computing for Cancer Informatics course provides a solid foundation in computing principles and data management, which are essential for Machine Learning Engineers. This course covers concepts such as data storage, processing, and shared computing resources, which are crucial for handling large datasets and training machine learning models.

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Research Scientist

Research Scientists conduct original research in various scientific fields, including cancer informatics. The Computing for Cancer Informatics course can equip Research Scientists with the necessary computing knowledge to manage and analyze large datasets, which is becoming increasingly important in scientific research. This course covers concepts like data storage, computing capacity, and research platforms, providing a valuable foundation for researchers working with complex datasets.

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Data Analyst

Data Analysts gather, clean, analyze, and interpret data to identify patterns and trends. The Computing for Cancer Informatics course can provide a valuable foundation in data storage and processing, two essential aspects of data analysis. This course also covers data management decisions, which can help Data Analysts make informed choices about the best computing resources for their specific data analysis tasks.

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Data Engineer

Data Engineers design, build, and maintain data pipelines and infrastructure. The Computing for Cancer Informatics course provides a foundational understanding of data storage, processing, and shared computing resources. This knowledge can help Data Engineers develop efficient and scalable data pipelines for managing and analyzing large datasets, which is essential for modern data-driven applications.

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Database Administrator

Database Administrators manage and maintain databases, ensuring data integrity and accessibility. The Computing for Cancer Informatics course can provide Database Administrators with a deeper understanding of data storage, processing, and shared computing resources. This knowledge can help them optimize database performance, manage large datasets, and ensure data security, which are crucial responsibilities in the field.

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Data Scientist

Data Scientists translate vast amounts of data into actionable insights for businesses and organizations. They analyze, interpret, and visualize data to identify trends, predict outcomes, and guide decision-making. The Computing for Cancer Informatics course provides a foundational understanding of computing principles and concepts, storage requirements, and managing and processing large datasets. This knowledge can equip you with the essential skills to understand data structures, work with distributed systems, and utilize advanced computing resources, all of which are crucial skills for Data Scientists.

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Systems Analyst

Systems Analysts design, develop, and implement computer systems for various organizations. The Computing for Cancer Informatics course can provide Systems Analysts with a solid understanding of computing fundamentals, including data processing, storage, and shared computing resources. This knowledge is crucial for designing and managing efficient and effective computer systems, especially for organizations dealing with large datasets.

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Computer Scientist

Computer Scientists research and develop new computing technologies and applications. The Computing for Cancer Informatics course offers a solid foundation in computing fundamentals, including binary data processing, and shared computing resources. This knowledge can help Computer Scientists design and implement innovative solutions for managing and analyzing large datasets, which is becoming increasingly important in various fields including cancer research.

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Software Engineer

Software Engineers design, develop, and maintain software applications. The Computing for Cancer Informatics course provides a foundational understanding of computing principles, data structures, and shared computing resources. This knowledge is essential for Software Engineers working on complex software systems, particularly those involving data analysis and management.

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Cloud Architect

Cloud Architects design and manage cloud computing systems. The Computing for Cancer Informatics course may be useful for Cloud Architects working on projects related to data management and analysis. This course covers concepts such as data storage, computing capacity, and research platforms, which are relevant to designing and managing cloud systems for handling large datasets.

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Information Technology Specialist

Information Technology Specialists support and maintain computer systems and networks. The Computing for Cancer Informatics course may be useful for Information Technology Specialists who work in research institutions or organizations dealing with large datasets. This course provides insights into computing concepts, data storage, and shared computing resources, which can help them better understand and support the computational needs of researchers.

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Artificial Intelligence Researcher

Artificial Intelligence Researchers develop and apply artificial intelligence techniques to solve complex problems. The Computing for Cancer Informatics course may be useful for Artificial Intelligence Researchers working on projects involving large datasets. This course provides insights into computing concepts, data storage, and shared computing resources, which can help them understand the computational requirements and resources needed for their research.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. The Computing for Cancer Informatics course may be useful for Quantitative Analysts who work with large datasets or need to develop computational models. This course provides a foundational understanding of data storage, processing, and shared computing resources, which are essential for managing and analyzing large financial datasets.

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