Good stuff, excpet:
>
This is dangerous advice if you go all in on this.
Lessons Learned from Two Years as a Data Scientist
31–40 of 89 posts
Re: Lessons Learned from Two Years as a Data Scientist
#32Nice pictures. (Didn't read the words... but I liked the pictures.)
I was distracted (in a good way) by the pictures but I did read most of it. I thought it was a fairly honest and good blog I did like the diagram of the different company styles I'll will read it 100% when I have enough time
> I did like the diagram of the different company styles
That's quite an old internet cartoon.
Re: Lessons Learned from Two Years as a Data Scientist
#33About Java vs Python, modern Java would be: import java.util.ArrayList; var cars = new ArrayList (); Python would be: cars: list[string] = [] The big difference seems to be that ArrayList is not a "default" data structure in Java, but it is in Python. While I like the Python example better, I'm not offended by the modern Java.
Actually, as a Java dev, I quite like modern Java for data work. Streams + static typing make aggregating data a breeze.
I assume for more advanced work like the one OP mentions you'd still want to stick to Python because of the superior ecosystem, but I was pleasantly surprised by Java.
Can't believe I'm writing this, but I actually like modern Java.
Re: Lessons Learned from Two Years as a Data Scientist
#34Good stuff, excpet: > This is dangerous advice if you go all in on this.
I believe the available evidence suggests that over the long term, a small amount of leverage increases returns. Obviously it increases volatility as well, but if your time horizon is long, you can cope with that.
You do not want to invest in ETFs that are themselves leveraged & rebalance daily however, that’s going to eat all your money if you hold them for any length of time - those products are designed to be held for short periods only.
Re: Lessons Learned from Two Years as a Data Scientist
#35About Java vs Python, modern Java would be: import java.util.ArrayList; var cars = new ArrayList (); Python would be: cars: list[string] = [] The big difference seems to be that ArrayList is not a "default" data structure in Java, but it is in Python. While I like the Python example better, I'm not offended by the modern Java.
The author goes full circle by introducing types in python.
Almost every java developer uses an IDE with autocomplete so they would most likely not use a var (where they wouldn't) ..type the interface they want to use, add the assignment operator and then just let the IDE suggest the implementation, add the import and even format the line/file.
Such trivialities don't help when attempting to distinguishing the flexibility python bring for arbitrary/quick code
Re: Lessons Learned from Two Years as a Data Scientist
#36About Java vs Python, modern Java would be: import java.util.ArrayList; var cars = new ArrayList (); Python would be: cars: list[string] = [] The big difference seems to be that ArrayList is not a "default" data structure in Java, but it is in Python. While I like the Python example better, I'm not offended by the modern Java.
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Re: Lessons Learned from Two Years as a Data Scientist
#37About Java vs Python, modern Java would be: import java.util.ArrayList; var cars = new ArrayList (); Python would be: cars: list[string] = [] The big difference seems to be that ArrayList is not a "default" data structure in Java, but it is in Python. While I like the Python example better, I'm not offended by the modern Java.
Actually, as a Java dev, I quite like modern Java for data work. Streams + static typing make aggregating data a breeze. I assume for more advanced work like the one OP mentions you'd still want to stick to Python because of the superior ecosystem, but I was pleasantly surprised by Java. Can't believe I'm writing this, but I actually like modern Java.
I've only followed Java from far away (last time I used it was Java 7 in college) but modern Java seem like a very nice language. There are also Graal, alternative ecosystems to Spring, good things like that.
Re: Lessons Learned from Two Years as a Data Scientist
#38Good stuff, excpet: > This is dangerous advice if you go all in on this.
SPXL also has an expense ratio over 1%, which will eat away at earnings unless in the best of bull rushes (now).
Re: Lessons Learned from Two Years as a Data Scientist
#39I really liked this post. Tons of small tidbits I feel I'd only get from working in the teams they worked on. Some things I noted: * Check out `ray` as an alternative to `multiprocessing` * Check out `tqdm` * Use `pdb` more * See if fast.ai or https://jalammar.github.io/illustrated-transformer/ are worthwhile * Prioritize the papers I read better * _Leveraged_ index funds?
Risk-adjusted return (Sharpe)
- VTI/VOO: 0.6 to 0.8
- Leveraged S&P500 ETFs: 0.5 to 0.9
- Savings account: positive infinity
- Treasury bonds: 1.0 to 1.2
Leveraged index funds give approximately the same risk-adjusted return as passive index funds but with 2-2.5x the absolute return (eg, UPRO).
However, if you have a weak stomach (you don’t like seeing your balance drop), then leveraged ETFs are not for you.
Re: Lessons Learned from Two Years as a Data Scientist
#40Good stuff, excpet: > This is dangerous advice if you go all in on this.
SPXL also has an expense ratio over 1%, which will eat away at earnings unless in the best of bull rushes (now).
Leveraged EFTs outperform VTI/VOO (in terms of total return) over 30-40 year investment horizons. Period.
Now, the risk (potential one-year downside) is not for everyone.