The Soft Skills You Need to Succeed as a Data Scientist
Overview of Your Journey
- Introduction
- Skill 1 – Communication
- Skill 2 – Collaboration
- Skill 3 – Curiosity
- Skill 4 – Project Management
- Skill 5 – Mentoring
- Wrapping Up
Introduction
When you are working on your career as a data scientist, it's easy to focus on the hard skills. You might want to learn a new ML algorithm like an SVM with a non-linear kernel, a new software technology like MLflow, or a new AI trend like ChatGPT.
These skills are comfortable to learn because it is easy to measure success in them. Take MLflow as an example. You might first start to learn about what MLflow can provide to your ML lifecycle. You learn about model artifacts, ML project structure, and model registration. You finish a course, spend a few hours reading the user guide, and even implement it in a real-life project. Great! After doing this, you can confidently say that you know some MLflow and can add this as a skillset in your CV.
What about a soft skill like, say, time management? How would you go through the same process? Really stop and think about this. There are certainly books on time management you could read, but it is not nearly as concrete as reading the documentation on MLflow. You could implement time management in your daily routines, but it is not as demonstrable as implementing Mlflow in an ML project. You could list time management in your CV, but what does that even mean?
