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Chapter 1: Data Science Myths

It's never too late to learn cancer data science. 

We’ve spoken with early career cancer researchers and trainees, and many of them lamented that they wished they had started gaining data science skills earlier in their career. In this chapter, you’ll learn the truth about common data science myths and explore overview resources of the data science process, where you fit into the process, and how you can use it in your projects.

Watch the Video

Watch 5 Data Science Myths (approx. 6 minutes long).

Test Your Knowledge

Q: The best technology for conducting data science is expensive.

A. True
B. False

The correct answer is B.
Many data science resources, such as those produced by NCI, are freely accessible. You don’t necessarily need a big budget for technologies to use data science to do high-quality research. 

The incorrect answer is A.
Many data science resources are freely accessible. You don’t necessarily need a big budget for technologies to use data science to do high-quality research. NCI provides the cancer research community with many free tools.

Related Materials

  • Cancer Data Science Lifecycle: Walk through the 6 stages of the cancer data science lifecycle to find out how you would conduct a data science project.
  • NCI Brings Data to Life: Watch this short video sharing how cancer research data is collected, processed, and turned into meaningful insights through the life of a single genomic data point.

Keep Going

Continue to Chapter 2 to learn about big data technologies we think can accelerate your education and research.

Instructor

Shan Li, Ph.D., NCI Center for Cancer Research (CCR)
Dr. Li is a staff scientist with NCI CCR’s Cancer and Data Science Laboratory. Her research focuses on identifying the functional non-coding mutations in cancer progression.

For questions and feedback about this chapter, email our team at ncicbiit@mail.nih.gov. 

  • Updated:

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