Data science is an enormous and growing field. Over time in a data science career, it becomes hard to stay sharp on all areas. Everyone has their own reasons, but here are some of the excuses that I have told myself:
- Life is busy enough. I don’t have extra time to train
- I’m doing pretty well in my job, so I must be good to go
- If I want to get ahead, it’s not going to be from more technical skills, but from leadership skills
Why Training is Not Optional
If you do not stay sharp, you expose yourself to two risks:
- Burnout
- Obsolescence
To avoid either of these two outcomes, a data scientist must periodically invest in training and retraining in their skills.
How to Develop a Data Science Training Regimen
- Identify the skills
- Identify the resources
- Block off time
Identify the Skills
To begin training, a useful starting point is to develop a “reading list” of skills or topics to cover. In data science, at a high level your reading list will generally cover statistics, machine learning, programming, and maybe some random topics like optimization. At a later blog post, I’ll share my own list.
Identify the resources
Your “reading list” obviously doesn’t have to be just text sources. I often prefer to cover the same topic with multiple types of media: academic articles, Wikipedia, YouTube, GitHub. Getting the same topic from different angles helps me feel more deeply engaged on the topic.
Block off time
Arguably the hardest part, you have to find time to actually do your training. I personally like to wake up before my family and do some reading or watch some YouTube lectures.
Data Science Principles 