780: How to Become a Data Scientist — with Dr. Adam Ross Nelson

Portfolio Projects

Dr. Adam Ross Nelson emphasizes the importance of portfolio projects for aspiring data scientists. He suggests that showcasing practical projects can significantly enhance one's chances of landing a data science role. Among his top recommendations is creating an original dataset, documenting the collection process, and making it available for others to use.

Collect original data, document the process used to collect that data, why you collected the data, how you collected the data, engineer that data engineer features make the data available for others to use.

--- Adam Ross Nelson

Another valuable project idea involves implementing natural language processing (NLP) using pre-trained models, which allows candidates to demonstrate their ability to put projects into production without the need to train a model from scratch 1.

   

Project Implementation

Practical implementation of portfolio projects is crucial for demonstrating data science skills. Jon Krohn and Adam discuss data augmentation as a method to enhance datasets, such as using techniques like rotation, flipping, and translating text through multiple languages to create variations. This process not only increases the dataset size but also showcases important skills in data science.

To augment that, I would translate the English into German, from German, maybe to French, from French to Spanish, and then Spanish back to English. And the new English version is going to be different.

--- Adam Ross Nelson

Adam highlights that these projects can be accessible to individuals at all levels, providing a platform to showcase skills without necessarily building a model 2 3.