Career Confessions: NCI Earl Stadtman Investigator Shares Data Science Mentorship Advice
, by Tongwu Zhang, Ph.D.
As part of our Career Confessions from Cancer Data Scientists series, you can hear a cancer data science investigator’s advice for advancing in the field.
We spoke to an Earl Stadtman Investigator to hear his advice on advancing in the cancer data science field. At NCI, Earl Stadtman Investigators are tenure-track-level positions for creative, independent thinkers undertaking innovative, high-impact research. In his position, Dr. Zhang primarily focuses on cancer genomic studies.
Tongwu Zhang, Ph.D.
Earl Stadtman Investigator
Biostatistics Branch
Division of Cancer Epidemiology and Genetics
What made you want to pursue data science and the cancer research field?
I find it fascinating to uncover new biological hypotheses and validate existing findings through the exploration of large-scale genomic data sets. These discoveries are pivotal to advancing cancer research.
Based on what you’ve learned in the field already, what do you typically try to teach postdoctoral fellows in your lab?
When I mentor postdoctoral fellows in my group, I primarily focus on the following:
- I provide them with in-depth knowledge of our research projects by discussing each project's background, significance, hypothesis, aims, methodology, and potential challenges. I also provide them with key literature, relevant data, and code for testing to facilitate their immediate engagement and productivity in our projects.
- I equip them with essential bioinformatics skills and comprehensive training in data analysis, with a particular emphasis on R programming. NIH Fellows have access to a wide array of resources such as encompassing NIH-wide workshops, online training sessions, and instructional videos.
I believe these skills are crucial for effective data processing and summarization throughout their postdoctoral tenure.
What advice would you give someone looking to excel in cancer data science?
It's essential to stay informed about the latest genomics and cancer research developments. I’d recommend diligently reviewing scholarly publications through journals or Google Scholar subscriptions, attending key conferences and seminars such as those by the American Association for Cancer Research, and engaging with leading researchers on social media platforms like X (formerly Twitter).
These strategies will keep you updated on cutting-edge research and methodologies and enable valuable networking opportunities with experts in the field.
What skills does a cancer data scientist need?
Data Visualization
Statistics
Programming
What programming languages should they learn for data analysis?
Downstream Data Analysis
R
Upstream Data Analysis
Bash
Python
Perl