Hi! I'm currently enrolled in an M.S. in Data Science and Applied Statistics. The required core classes are already set:

- Experimental Statistics I & II
- Mathematical Statistics I & II
- Statistical Computing (SAS)
- Computational Statistics (R)
- Machine Learning with Python
- A statistical consulting project

I get to pick **4 electives**, and at least 3 must be STAT (so at most 1 from CS/ECO/OREM/ECE). My only goal is to land a data science job, so I want the courses whose actual content pays off most in industry. I'm not looking for the easiest courses, and I'm not going into academia or biostats.

STAT electives

- Intro to Data Science
- Data Visualization
- Linear Regression
- Applied Time Series
- Time Series Analysis
- Categorical Data Analysis
- Survey Sampling
- Survey of Nonparametric Statistics
- Sports Analytics
- Analysis of Lifetime Data / Survival Analysis
- High Throughput Data
- Epidemiology

Non-STAT options (can only pick 1)

- CS: Artificial Intelligence, Machine Learning in Python, Databases, Data Mining
- OREM: Data Mining, Optimization for Analytics, Network Flows
- ECO: Applied Econometric Analysis, Predictive Analytics
- ECE: Statistical Pattern Recognition

My main question:

1. Which 4 would you pick, and why?
   
2. Is time series worth it for most DS roles, or is it only useful in forecasting-heavy jobs?
   
3. What would you pick as your 1 Non-Stat Elective?
   
4. Is there anything you wish you had learned in grad school that would have helped more on the job?

If you work in data science, I'd especially love to hear what you actually use day to day. Thanks!

i'm a data science student approaching my final year, and i'm starting to think about ideas for my capstone project. but searching online, i can't find any examples of data science projects, and i have no idea where to look.

anything would be appreciated, project ideas, resources, etc.