carlmcqueen

(example: phones off during day, on from 1am-5am then shut off again, no facebook browsing at all, etc.)

Some of the 'code' running massively profitable departments in finance are spreadsheets, on shared drives, with thousands of linked cells thought up by a brilliant employee who left the company five or six years in the past.

I wish I was kidding.

Thanks for how great you kept apollo.

In my case, it is a start of the day, every single day, issue where I'm unplugging and re-plugging in monitors waiting for it to 'take' and then it's fine for the day.

I was working with really, really smart PHDs and was doubting my hiring to the team. I felt like a lot of these guys had forgotten more than I'd ever known.

During my time in that job, I realized that my experience in navigating the complex databases from working with the business and operational teams gave me an advantage. I took my domain knowledge for granted and was able to work on the problems at hand much faster than the highly intelligent colleagues who relied on IT/DBAs to write their queries. They often had to go back and forth for a couple of days to clarify their requests, leading to misunderstandings and delays.

In the end, I fit in just fine, held my own, and was aware of my individual talents. I enjoyed learning from the PhDs, and they were happy to teach from their backgrounds.

A lot of the experience from the big bank has 0 transferability to any other company. What made me stand out was I knew which tables of the gigantic data environment were the best to use, I had hundreds of already built queries for many different problems and I was dependable. It got me really far before I went off on my own.

I will say the value of not having to worry about your toilets, oven, etc when you're career centered has intrinsic value not tied into the money you'd make over time owning the asset. I hate having to deal with that stuff with the little free time I have.

We did a 80 mile each way day trip to visit someone and bring them back and had to line up lunch in an area with no great food options so we could sit for the 30 minutes to get back up to 80%.

With it being cold in the midwest our advertised '280 range' or '180 range at 80%' never is even close to that.

It's like the olden days before really efficient ICE vehicles.

Middle aged traditionally is 40-60.

At the bank I worked before 2019 at I was on a advanced analytic team, we had this amazing teradata database and then there were these insanely fast (yet older) IBM DB2 databases and then there was a few big oracle databases.

We did amazing things with teradata + DB2 and then a leader who was tired of multiple databases asked us to vote, we chose teradata so we went with oracle and the migration was so bad I left.

Jerr Thorpe had an app that gave you your power back and tracked your phone info in a way you could have yourself for awhile but it became abandonware and started eating battery life so I took it off my phone. OpenPaths, that was it's name. https://www.jerthorp.com/openpaths

They are treated with stimulants that have no effect on bipolar disorder.

One started as a wire guy in television and worked his way up to a producer now, the other one I got a job in corporate banking and he moonlit in a wedding video company. He has his own wedding video company now.

I wasted my college experience as an awkward introvert. I didn't take college to be a time to make connections with my professors, I didn't take the time to see it as a time of ecosystem of closeness you'd never have again.

I was way to shell-shocked inwards. The becoming an adult wasn't something I was ready for yet. I look back at all the opportunities I had to really learn interesting things and network and grow and frown because I was still in my high school 'just survive school' mentality. I did as little as I could to get through each class.

If you learned data.table however, it's better to just stay in data.table. Nothing in the tidyverse can touch the efficiency of data table.

The body condition of construction workers, fork lift operators and even welders is worn down and in pain.

Not to mention, they have to live with unfair medical standards. When she does hand strength assessments for workers comp the number is based on natural average. So lets say a normal office worker squeezes on the test at a score of 100, a construction worker squeezes at 320. Workers comp says they can return to work if they can squeeze at like 120. Which terrifies the construction workers but they won't get any more time off.

Many would ask to see the page I'd read and I'd show them and they would ask how I got my code working from an example that had nothing to do with what our task was.

I think there is a lot of value to looking at a SO answer, generalizing it and making it work in your code and doing it quickly. Knowing which SO articles are junk and which ones are gems is something many take for granted but is actually something close to having the right 'gut' feeling.

Which has been done before.

My side is that I worked for a bank on the brokerage side for ten years in different positions. What always struck me was that my access was very carefully controlled, I was a background checked employee and had to meet with compliance once a year, etc etc.

However when a law firm asked for anything or consultants said they needed more data they just sent massive data dumps to the network admin guy, no questions further asked. At least not at my pay grade.

As I've consulted I ask for only what I need to keep my own risk down but it is always a surprise to my clients I don't want PII I don't need and only the data that my model will help enhance.

Not perfect, but at least sort of helpful.

The main point, let me have a nice version without the touchbar is shared.

The data science team at a major bank I used to work for historically had a team that sourced and cleaned the data, then a team who would explore and build the models and then a team who would learn and run the models going forward. In my opinion the final team was the most important and the most tragic for being condensed, they noticed when the models needed to return to the model builders to be adjusted when missing the mark.

The "lost jobs" in this case is that when I was on the team we had to learn the entire bank database structure and source our own data, build the models and then automate them in such ways to "catch" when they were missing the mark.

The team will be further shrunk as the software tools provided have better auto-sql for sourcing the data, automated model building functions, and automated visualizations thus removing even that as a special skills.

Pretty in depth article and well laid out explanation of natural gradient descent with a small pre-fit dataset for a conclusion of 'too computationally expensive for machine learning/big data world'.

This is what I struggled with in school. You'd spend a class week learning some tough stuff only to be told 'this is no longer done, better methods are now used.'

Sometimes the work is needed to allow you to understand why/how the new method is used, but in many cases I didn't find that to be true.

So if you have windows office because it has traditionally been better than the one offered to mac you'd have to remember to use control instead of command.

Wasn't a homeless person, but in St. Louis Missouri a house isn't out of the question.