u/GayTwink-69

I have taken some CS electives in my undergrad (intro python programming, databases, deep learning), and my econometrics major was more of an applied statistics major heavily focused on causality and time series.

The problem is that it was basically focused on the wrong thing. I basically have never done a data science project in python, only R for basically every single unit (some SAS and Stata sprinkled in...). I know a lot about causality, non stationary time series, and panel data, but have never used XGBoost in my life.

I do have an applied time series paper published, and worked as a research assistant for 1 year, also in time series.

I feel like the data science master's would fill in these technical gaps while also giving me further data science training. But I'm also scared I might be wasting my time (money isn't an issue).

Do you think it would be worth it for me?

I have taken some CS electives in my undergrad (intro python programming, databases, deep learning), and my econometrics major was more of an applied statistics major heavily focused on causality and time series.

The problem is that it was basically focused on the wrong thing. I basically have never done a data science project in python, only R for basically every single unit (some SAS and Stata sprinkled in...). I know a lot about causality, VECM, and panel data, but have never used XGBoost in my life.

I do have an applied time series paper published, and worked as a research assistant for 1 year, also in time series.

I feel like the data science master's would fill in these technical gaps while also giving me further data science training.

Do you think it would be worth it for me?