prev: Note of data science training EP 13: Regularization – make it regular with Regularization

This is the last class of the training and I’d like to thanks Mr.James Larkin, Mr. Greg Baker, and Mr. Attapol Thamrongrattanarit for devoting their efforts to share knowledge to me and friends.

And here are the advices for frequent mistakes committed by data scientists.


What mistakes do data scientists often make?

Mr. Larkin answers

There are mistakes generally made by data scientists new to the field. Some high level issues I’ve seen in work environments are:

  1. Focusing more on the algorithms than the data itself
    Success is needed but data insights or something lies in it are prior.
  2. Picking unfit algorithms for their work
    This often occurs because the data scientists want to test out some new, powerful but black box options. It may not be fit or suitable for data of the work.
  3. Poor data understanding
    First steps are data exploring, cleansing, and visualizing. There are 99% of the time that we attempt to skim through data and choosing an algorithm. A veteran data scientist tends to efficiently dive deep into the dataset.
  4. Experience-wise
    Experiences are good. Nevertheless sometimes we should focus more on data not the learned experience. The data can be changed overtime and we data scientists need to chase it.
  5. Team and communication
    A lot of fairly good works can go nowhere in an organization. This is because they were not explained clearly and effectively to the appropriate stakeholders.

They are benefit to us and some are able to be applied in other works.

This is the end of this series, I would say.

And I will pick it up for you for what’s next. Stay tuned.