10 Lessons from my First Year as a Data Scientist (transitioning from academia)
It’s been a little over a year since I left my research job to take up a role in the industry. When I first started searching for data science jobs, I was surprised at how many companies were looking for one. It seemed like every company was trying to hire a data scientist, and the demand only appeared to be increasing. However, I quickly learned that the demand for data scientists doesn’t mean that the job is easy. It’s one of the most challenging jobs I’ve ever had. Every day, I’m faced with complex problems that require creative thinking and outside-the-box solutions. Hopefully, these lessons will be helpful for those of you who are considering making the transition from academia to industry or are in the early stages of your career in data science.
1. Data Quality over models
Good quality data is the foundation upon which machine learning models are built. Without good quality data, machine learning models would be nothing more than a guess. I’ve learnt that a big chunk of data scientists’ work is to craft datasets. Not only by cleaning the data and feature engineering but also by making sure that our input will produce the expected solution. At my work, we also advise on the user research trials, making sure that the data we’re collecting from our users (through…
