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jwilbs's comments

jwilbs | 7 years ago | on: I’m leaving China

I find this comment odd. I spent many years in Oakland and saw plenty of Indians (a quick google search shows Oakland is 17% Asian). Not to mention there are at least three Indian restaurants within two blocks of 12th street/Oakland City Center BART.

jwilbs | 7 years ago | on: Does inequality cause suicide, drug abuse and mental illness?

I hope I’m not coming off brash but I am having difficulty wrapping my head around how someone with 10 years of dev experience can’t break minimum wage. What kind of dev do you do? Have you looked into freelance work? Do you set salary expectations low from the get go?

jwilbs | 7 years ago | on: (Freelance) Math guy (likes Cryptography) needs work

May wanna drop the following:

“But also one of my favorite activities is to spend some wonderful hours with wonderful women to give them all my love. ”

Not only is it completely unrelated to a job hunt, but doesn’t come off very tastefully. Good luck!

jwilbs | 8 years ago | on: Why data scientists should start learning Swift

It may sound weird, but I believe if any data scientists switch to a ‘nontypical’ language for the domain, it should be JavaScript.

What’s required for data science is a healthy ecosystem of scientific computing tools. While js obviously isn’t as mature as python (anaconda stack + Jupiter, etc) or R (tidyverse etc) in this aspect, it has made great strides recently: - tensorflow.js - observable notebooks - mathjs - simple-statistics / jstat

Furthermore, with tools like d3 + leaflet, js has very little competition when it comes to data visualiation.

A big thing holding js back is a mature library for data manipulation, hopefully this changes in the future (anybody know of any potential fills for this gap?).

jwilbs | 8 years ago | on: Berkeley offers its data science course online for free

I personally thought Stats 134 was the hardest of those courses by far (though I took it under a visiting professor who was needlessly difficult). 188 was a breeze, and I believe the full course is offered for free on edx

jwilbs | 8 years ago | on: Ask HN: As a data scientist, what should be in my toolkit in 2018?

Spark sits on top of YARN/Mesos, and is used for data processing scalability that pandas can't handle.

Personally, I think two areas often lacking are software development skills and general statistics knowledge. The former is necessary for writing production-quality code, assisting with an sort of data engineering pipeline, writing reliable, reusable code, and creating custom solutions. Unfortunately, the latter is often skimped on (if not skipped entirely) in favor of more 'hot' fields like ml/dl, with the result being a fuzzy understanding across the board. (You'd be amazed at the quantity of candidates lacking fundamental knowledge about glm's, basic nonparametric stats, popular distributions, etc).

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