As a field, data science moves at a different speed than other areas. Machine learning constantly evolves and libraries like PyTorch and TensorFlow keep improving. Research companies like Open AI and Deep Mind keep pushing the boundaries of what machine learning can do ( i.e. DALL.E and CLIP). Foundationally, the skills required to be a data scientist remain the same: statistics, Python/R programming, SQL or NoSQL knowledge, PyTorch/TensorFlow and data visualization. However, the tools data scientists use constantly change.
Iused to work as a research fellow in academia and I’ve noticed academia lags behind industry in terms of implementing the latest tools available. I want to share the best basic tools for academic data scientists—but also for early career data scientists and even non-programmers looking to employ data science techniques into their workflow.