1
Data Science Myths
2
Coding
3
Biostatistics
4
Technologies
5
Machine Learning
6
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 2: Coding Basics for Cancer Data Science

Chapter Description

Do you need to know computer science to do data science? Aren’t data scientists just computer scientists? In this chapter, we’ll answer those questions and set you up with tips and resources to write code for cancer research projects.

Start the Course

Required:

Watch our video, “5 Tips for Learning to Code” (approx. 6 minutes long).

Test your knowledge!

1
What are two common programming languages used by data scientists?
a
Correct
Incorrect
2
Why is it important to write comments in your code for others to see how you did your work?
b
Correct
Incorrect
3
Why is it important for others to reproduce your code?
d
Correct
Incorrect

Other Related Materials:

  • Python Introductory Series, Lesson 1: Watch this course recording from NCI’s Bioinformatics Training & Education Program (BTEP) for information and guidance on Python (i.e., its command syntax, where you can find Python packages, and how it’s used to start a Jupyter Lab session on Biowulf). Note: Given this is a recording, you will have to access the resource tools specified within the video to follow along with the instructor. 
  • An Introduction to Python for Data Science (Part 1) (Part 2): Watch this beginner-oriented, two-part series on Python, presented by NIH’s National Institute on Minority Health and Health Disparities.    
  • Introduction to R: Watch this NCI BTEP course recording for an introduction to R and RStudio. Note: Given this is a recording, you will have to access the resource tools specified within the video (i.e., DNAnexus and RStudio) to follow along with the instructor).
  • GitHub Automation for Scientists: With this course, you’ll walk through the “why’s” and “how’s” for using automation to enhance your scientific software development process. This course is designed particularly for students in the biomedical sciences and researchers who use informatics tools.
  • Choose a Programming Language: Refer to this recording from the NCI Center for Cancer Research’s Bioinformatics Training and Education Program to learn about R and Python—from installation to execution.

Keep Going!

Continue to Chapter 3 to discover key statistics concepts that will enhance your cancer research.

Instructor

Daoud Meerzaman, Ph.D.
NCI Center for Biomedical Informatics and Information Technology (CBIIT)
Dr. Meerzaman is the branch chief for the Computational Genomics and Bioinformatics Branch at NCI CBIIT. He leads a team of bioinformaticists, computational biologists, and developers to analyze cancer research data for NCI.

Have a question or feedback about this course chapter?