I recently had the opportunity to speak on the Data Driven Security Podcast with Jay Jacobs and Bob Rudis about data science training. You can listen to the podcast here.
To underscore a few points from the interview:
Data science is not a binary condition. Many people with whom I have spoke, or read, talk about “real” data science and/or “fake” data scientists. Unlike medicine, or law, in data science one need not be a “data scientist” to employ data science in one’s work. In practical terms, this means that data science can be viewed as a spectrum of skills which can range from beginner to expert, and most importantly, you don’t need to be a “real data scientist” to use data science techniques. In fact, it is my opinion that in the next few
When designing training sessions for working professionals, I try to approach them with that in mind and build courses that teach the thought process behind data science, as well as practical skills which students can directly apply to their jobs. The objective of the classes are not to convert students into data scientists, but again, to teach useful data science skills which are relevant to their work.
If you view training development where the goal is to teach a professional a series of relevant skills instead of a new discipline, that translates into developing short, focused classes rather than lengthy bootcamps.
Data Science is more than Machine Learning
I’ve reviewed a lot of data science courses, and many focus very heavily on machine learning and statistics. While this is certainly an important aspect of data science, study after study shows that data scientists spend 50-90% of their time doing data preparation and cleansing. With that in mind, when designing courses, I try to spend a decent amount of time on data wrangling techniques.
Anyway, please listen to the podcast here and enjoy! Questions/comments are welcome!