Practical AI R, Data Science, & Computational Biology
Oct 6, 2020
Daniel Chen, a data scientist at Lander Analytics and PhD candidate at Virginia Tech, shares his expertise in data science and computational biology. He highlights the importance of robust project organization and effective version control in data science. Daniel also discusses the integration of AI in medicine, particularly in epidemiology and medical imaging. Plus, he emphasizes reducing coding dependencies and maintaining data integrity as key to successful projects. His insights tie into the upcoming R Conference, making for an enlightening conversation.
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Early Programming Experience
- Daniel Chen's high school required all sophomores to take computer science, exposing them to NetLogo, Scheme, and Python.
- Initially overwhelmed by classmates with prior experience, he almost gave up on programming.
Pivotal Data Science Class
- Chen's pivotal moment in data science was taking a class with Jared Lander, which exposed him to new techniques.
- Attending a Software Carpentry workshop during that time further solidified his interest in data science education.
Prioritize Data Processing Skills
- Focus on learning the steps of data processing and tidying first, regardless of your chosen language (R or Python).
- Treat programming as a tool and look up language-specific syntax as needed.
