Resources for incoming members

Joining the lab might be overwhelming since we work on a broad range of applied molecular biology and bioinformatics. As most of incoming students may come from biomedical background, learning some tools as you join the lab will be very useful later on. I listed some of the useful tools below:

Python

Python has been used in a lot of our development. Therefore, programming in Python is almost needed if you decide to join the lab. There are a lot of resources on how to learn Python. Try to learn linear algebra, data science, and machine learning with some of the Python libraries such as numpy, pandas, and scikit-learn.

Machine Learning and Deep Learning

Today, Machine Learning (ML) and Deep Learning (DL) are a popular tools to build models in many projects. Understanding the basis of these tools are useful. Make sure that you learn the core concept of learning algorithms before applying to your project. There are multiple free online classes that you can learn ML and DL such as Andrew Ng's Machine Learning, Neuromatch Academy, Deep learning's book by Goodfellow and Bengio, and more.

Recommended books

• The Science of Science, by Albert-László Barabási and Dashun Wang
• Calling Bullshit: The Art of Skepticism in a Data-Driven World by Carl Bergstrom and Jevin D. West

... I will continue adding more resources to this page.