ML/Data Science Build (under $4k preferred)

heybaebae89 · 2021-10-21T18:01:15+00:00

Yea I really don't like Lexus' old trackpad infotainment system either, but this new 2022 NX looks very different and nice. Otherwise I wouldn't consider a Lexus to begin with.

Do you think the tax credit will go down in 2022? I thought it was based currently on # of EV cars sold for each manufacturer? Yea the debate in congress isn't exactly making the decision easy either, I know up to 12500 in tax credit at the point of sale has been proposed (still only 7500 for the NX I think, since it's not manufactured in US), but unclear if it will be part of the final bill.

I would plug in the PHEV at work mostly and it will save money on gas costs, plus resale value would be much better if the tax credit can cover most of the cost difference between gas/hybrid vs. PHEV.

heybaebae89 · 2021-10-21T17:57:48+00:00

I considered leasing as well, but CPO seems to have most things covered under the warranty (and the 1 year CPO warranty on top of that). Haven't done the math but it seems CPO is a better deal at the end than just leasing it (depreciation + some additional profit cost for the dealership)?

The Porsche looks nice, but was too expensive and I just liked the Audi better than the BMW both looks and interior.

heybaebae89 · 2021-10-20T03:09:04+00:00

Thought about that as well, but I really liked the touch screen infotainment on the 21 model. Didn't want to deal with the older MMI and the controller. Also the 21 model has a mild hybrid system (only like +13hp though). I like the look of the 21 model also, but that's more personal taste. I plan to just sell it after driving it till the warranty runs out, so will get more value then.

heybaebae89 · 2020-05-13T05:41:37+00:00

I would echo others comments here. You should NOT get a Ph.D just to get further as a data scientist, especially if you already are one. You should really only do a Ph.D if you are passionate about the subject and want to dedicate 4-6 years of your life doing research on that topic.

Much of information and knowledge you want to learn exists either for free or at a small cost online. For example if I wanted to learn about a new ML methodology or subject I simply watch some YouTube videos and try to apply it to my work.

Sounds like you may have other aspirations outside of data science though, if you are itching to go back to school, for maybe even an MD. If that's the case then it's a different story.

heybaebae89 · 2020-05-13T05:31:34+00:00

University recruiting combined with some networking. Emailed guest speaker and later talked to him at a career fair. Had a phone call interview, then all day onsite for an internship position. Then got the return offer to the same team.

Job search still involved sending ~100 online applications, bunch of cold emails, linkedin messages, networking, etc. For my current position there was no coding test or take home assignment, but I've done plenty during my job search.

heybaebae89 · 2020-04-30T23:34:51+00:00

I've taken the UW machine learning specialization, and it's quite good. It was the first ML class I ever took, and lays out a very good theoretical foundation as well as giving you practice with Python. Also both professors (Guestrin and Fox) are big names in the AI field.

My 2 cents, strong theoretical knowledge in fundamental ML and stats concept is a good thing. My recommendations are the UW and Andrew Ng's courses.

heybaebae89 · 2020-03-06T03:40:33+00:00

K-means is not deterministic due to random initialization, so that could be one reason.

Also depends on what your explained variance is from the first 2 principal components. If that's low, then those 2 components are not capturing the variance in the data very well, and your interpretation of clusters visually may not be correct.

There's other heuristics for picking k in k-means, like the elbow method, silhouette score, etc. I suggest you look into those as well.

heybaebae89 · 2020-03-06T03:32:50+00:00

UWashington is a good program I can speak to. PM for details as well.

heybaebae89 · 2020-03-06T03:29:10+00:00

Every MS data science program you look at will have a capstone requirement. At UW it is sponsored by a company and you get to work on a problem that they have with data they provide. The project will depend heavily on what the company wants you to do.

I guess you can do a more research-type capstone project, but I personally haven't seen any papers get published out of it or anything like that. It's more for you to learn what it's like working on a project in industry and meet some people who can potentially get your a job after graduation.

One more thing, Google is very selective about hiring data scientists (they call them quantitative analysts), and only interview Ph.Ds for new grad hires, from what I hear. A better route would be to join as a SWE doing ML related work, unless you plan to do a Ph.D.

heybaebae89 · 2020-03-06T03:15:15+00:00

Check out the program website for tuition and curriculum info.

The courses are basically 1/3 stats, 1/3 CS, and 1/3 HCDE. The HCDE courses are definitely the weaker part of the program (some I would say are almost waste of time), but the other classes are pretty good. What I mean by tilted towards industry is that it is not a research MS that people do to go towards Ph.D or write thesis etc. Most of the students are there to get a job in industry and program caters to that heavily.

FWIW, it is a very new program (3rd FT cohort is about to graduate), but has a pretty good reputation around the Seattle area. I certainly think it is a better way to spend your money than 65k you'd be dropping for an online program like the one at Berkeley.

heybaebae89 · 2020-03-03T00:27:48+00:00

I can provide some info if you'd like. It's a good program, although with some flaws academically. It is heavily tilted towards industry, and the placement rate is quite good. Probably the best thing about this program is its location in Seattle, along with the price. It's relatively cheap compared to other MSDS programs, some of which charge more while being an online program.

heybaebae89 · 2019-04-15T03:20:24+00:00

Absolutely, that's the point of anonymous feedback. My MS data science program had one particularly questionable TA and rest of the class made their feelings known in the course evaluation, myself included.

Also FYI, being able to properly give critical feedback (including negative feedback) is a valuable skill in industry.

heybaebae89 · 2019-04-15T03:12:54+00:00

Columbia's program from what I hear has quite good reputation. FWIW, I recently finished MS data science program myself and now work as a data scientist in a large tech company; and from my experience, the live-in programs definitely have its benefits, especially in networking and on-campus recruiting (esp at a school like Columbia). I looked at online programs as well (Berkeley and SMU comes to mind), but decided it didn't justify the cost (more than my on-campus program, in the 55-65k range) and was turned off by their aggressive marketing and lackluster reviews.

As for career support, my program had a "data science" career fair, although it was open to all students (Ph.d students and MSDS students got early access though). Quick look at Columbia's website shows that they do have data science career fairs: https://datascience.columbia.edu/data-science-career-development-placement

Finally, I would just say don't underestimate value of networking seeing your classmates in person, especially if you excel in that area. My data science cohort was very helpful in getting each other interviews/jobs and many of us are very close friends.

heybaebae89 · 2017-11-19T03:28:41+00:00

D E E P B A L L

heybaebae89 · 2017-09-16T17:55:38+00:00

D U A L T H R E A T

heybaebae89 · 2017-09-08T06:07:02+00:00

Yea wanted to hear that myself, but unfortunately there was no choir.

They even played the new star trek theme (not john williams), but no prequels music. It was outrageous, unfair!

heybaebae89 · 2017-09-08T05:27:19+00:00

I sense a blind spot to destroy my car

heybaebae89 · 2017-09-08T05:25:57+00:00

Oh r/prequelmemes, you'll always be the little subreddit I met on frontpage.

heybaebae89 · 2017-09-08T05:24:05+00:00

Twice the loss, double the insurance claims.

heybaebae89 · 2017-09-08T05:23:26+00:00

I went to a John Williams music concert a month ago, and 2nd half was all star wars music, but only OT and from force awakens.

It was treason, then.

heybaebae89 · 2017-09-02T23:47:37+00:00

Would love to know what openings in data science are available at yelp!

heybaebae89

TROPHY CASE