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Data Science Myths
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Coding
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Biostatistics
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Technologies
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Machine Learning
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Research

Cancer Data Science Course

Are you interested in the field of cancer data science? Whether you’re intending to transform your career with it, or you’re just looking for some perspective, we hope this beginner series can be your roadmap to better understanding this pivotal field. 

Watch, read, and review your way through each chapter to learn fundamental information and skills. 

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Chapters

Chapter 3: Statistics Concepts and Principles for Cancer Data Science

Chapter Description

Do you need to be a math genius to understand the statistical concepts of data science? Don’t let the math daunt you. In this chapter, you will learn how to avoid common pitfalls in designing studies for data science and explore common statistical concepts you’ll need to know.

Start the Course

Required:

Watch our ~6-minute-long video, “5 Common Stats Questions from Early Career Researchers” 

Test your knowledge!

You’re planning to do a study. When is the best time to talk to a statistician?
b
Correct
Incorrect

Other Related Materials:

  • NIH Statistics Courses: Register for upcoming courses on statistics.
  • Overview of Statistical Concepts: Part 1: Learn about statistical concepts, including hypothesis testing, p-values and confidence intervals, types of data, and bias and confounding. 
  • Statistical Inference for Non-Statisticians: Part 1: You’ll learn the basic thinking behind two schools of statistical inference. 
  • Overview of Common Statistical Tests: Part 1: Provided through the NIH Library, this course covers the general concepts behind statistical tests.
  • Overview of Common Statistical Tests: Part 2: You can attend both, or either, and still gain valuable understanding of how to understand and prepare data, interpret results and findings, design and prepare studies, and understand results. 
  • Overview of Common Statistical Tests: Part 3: This segment describes basic concepts for using common statistical tests such as Chi-square, paired and two-sample t-tests, and more. 
  • Reporting Guidelines: Find reporting guidelines for a wide variety of health research studies under EQUATOR (Enhancing the QUAlity and Transparency Of health Research). By looking at reporting guidelines relevant your type of study and their accompanying explanatory publications as you are planning your study, you’ll have a good sense for what you should anticipate needing to report about your study and why all of those aspects matter for others to evaluate the quality of your study and properly interpret its results.

Keep Going!

Continue to Chapter 4 to learn about big data technologies we think can accelerate your education and research!

Instructor

Lisa McShane, Ph.D.
NCI Division of Cancer Treatment and Diagnosis (DCTD)
Dr. McShane is the associate director of NCI DCTD’s Biometric Research Program. She is an internationally recognized expert on precision medicine clinical trial design, the development of tumor markers and omics predictors, and reporting guidelines for health research studies.

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