Does anyone know how to do time series on partially overlapping time frames? Each data point is a score averaged across the year, and a new score is published every quarter. No access to raw data. Appreciate any help!
Twitter friends, does any of you know anyone who works at Verily? I am interested in their data scientist position and feels like the job description matches my experiences well. I would love to get to know the job a bit more and potentially find an internal reference!
@Dr_Sayers @ryanwebler45@dp_moriarity I feel the thousands of pre-internship practicum and externship hours are more than enough to conduct quality clinical research in certain areas. I have surely improved during the internship year but the huge financial sacrifice doesnβt justify the marginal amount of leaning gain
@JSchleiderPhD@IRIfellowship As Taiwanese, I wholeheartedly recommend Din Tai Fung! Iβve been to Maneki for Japanese food once. Also recommend it. For site viewing, Chihuly Garden and Glass is totally worth the ticket price!
@JSchleiderPhD@NorthwesternU@NUFeinbergMed Congrats! Jessica! This is amazing! They're so lucky to have you, and I'm sooo lucky to have the chance to work with you during my time at SBU!
Does any of you have a friend who is working as a behavioral scientist @ Apple (or any position along the line)? I'm very interested in this role and hope to build some connections. Appreciate any help!!!!!
Academics: It's very easy to land an industry job with lots of transferable skills you obtain during your Ph.D.
Me with all the coding/statistical/quantitative/ML skills asked on the job post but without a CS degree: Got a rejection letter every other day π
Reading an oncology paper about integrating psych services with oncology treatment: "Unlike advanced medical trainees at a similar academic stage (medical residents), behavioral medicine externs are of little to no cost to the institution." Our free labor is very well recognized!
@hawesmt Yes, increased complexity definitely plays a role. There are teams rooting for complex models. Some even suggest always adding random slopes. Model complexity itself is not a sin. We just need to think about our sampling adequacy and pay close attention to model generalizability.
In theory, mixed-effects modeling for repeated measures should improve our model estimation accuracy. However, I tend to find that using a trained mixed-effects model for predictions on new samples performs worse than a regression model. (1/3)
Solution:
Only use a mixed-effects model for prediction purposes when you can use both the fixed- and random effects. For example, if you got students nested within schools and you were predicting incoming students' future performance in each of these schools. (3/3)
Potential Cause:
1. The random error terms are not truly independent (and in reality, they often correlate with each other), which violates the assumption
2. Model overfitting (2/3)
@hawesmt Yes. My CS, finance, economics, and stats friends can easily earn over $10,000 over the summer and it's their norm (but not a requirement) to do an internship. Well...it's our norm to do an externship for $0 and a mandatory internship for low wages.
Disclaimer: Not saying you can't do fabulous research with the software mentioned above. I personally know SPSP and SAS well too. Just saying it's going to be slower and more restrictive, especially when handling large-scale data.
Start looking for a data scientist job in Health Techs. Most of them claim to be innovative, and this is my reaction when I see them asking for experiences in SPSS, STATA, and SAS...Thought we were Gen Python/R???