
When I started practicing Data Science, I haven’t thought much about using the Machine Learning model outside of a Jupyter Notebook. My mindset was "the model achieves great classification accuracy so my work here is done". By mentoring junior Data Scientists, I’ve noticed that this mindset is quite common. In reality, the model in a Jupyter Notebook isn’t of much use to the company. There are a few steps needed to move the model out of a notebook to a real-world environment. This is where many Junior Data Scientists fall short (and not just Juniors!)—so don’t be like them and learn at least the basics of these 5 skills.
Machine Learning is evolving – it is not just about math and statistics anymore
Here are a few links that might interest you:
- Labeling and Data Engineering for Conversational AI and Analytics
- Data Science for Business Leaders [Course]
- Intro to Machine Learning with PyTorch [Course]
- Become a Growth Product Manager [Course]
- Deep Learning (Adaptive Computation and ML series) [Ebook]
- Free skill tests for Data Scientists & Machine Learning Engineers
Some of the links above are affiliate links and if you go through them to make a purchase I’ll earn a commission. Keep in mind that I link courses because of their quality and not because of the commission I receive from your purchases.
Continue reading
This article is now published on AIgents’s Blog: 5 skills Data Scientists should learn.
Before you go
Follow me on Twitter, where I regularly tweet about Data Science and Machine Learning.
