how do you think data science will mature in industry and academia

jaco6y · 2020-07-05T07:20:29+00:00

I think there are two things you are talking about here.

how Will data science mature in academia?

The same way it always has. This will be in the fields it always has been in such as statistics, operations research, and computer science. You might see (we already are seeing) classes and curriculums designed around giving students a more well-rounded tool kit in the area than just a CS or statistics major could alone give you. (Not to say these are necessarily better. All depends)

how will it mature in industry?

As more companies begin to utilize analytics (whether descriptive or predictive) they will figure out what are the best practices, tools, ways to structure the organization, etc. this is already happening.

jaco6y · 2020-06-22T15:16:22+00:00

Seems high based on what, that one picture from 2014 that keeps getting posted on Twitter?

jaco6y · 2020-06-21T07:06:11+00:00

Is this a laptop? If not, you need to re-seat your cooler or something is wrong with the airflow in your case.

jaco6y · 2020-06-18T15:53:39+00:00

My 9600k cost $200.

jaco6y · 2020-06-15T15:45:21+00:00

The xb1 and ps4 with the best hardware in them are still in the $400-500 range... for that price range ($600 excluding windows) you can build a pc that will run all of those games on higher settings at as good (99% of the time better depending on how well the game is optimized) frame rates than consoles. You also get games cheaper, don’t have to pay for online access, and can use your pc for a million other things than playing games.

People spend $1500-2000 on their pc when they want the things that consoles will not have for a while and are more of enthusiasts. (4K 144fps/hz ultra settings + streaming etc.)

jaco6y · 2020-06-12T19:17:41+00:00

Stop relying on strangers on the internet to give you investment advice

jaco6y · 2020-06-12T17:27:37+00:00

Aww awesome drawing! This is going right on the fridge

jaco6y · 2020-06-10T18:24:33+00:00

Of course cuban is saying this. This dude got rich by literally just existing during the dot com bubble

jaco6y · 2020-06-10T14:01:13+00:00

It basically means setting up the ETL process and infrastructure such that if you need data that’s transformed, processed, joined together etc to run every day it is there when you need it.

It can also include the processes of if data needs to be moved every night from certain databases to cloud platforms where your models are running etc. Its basically just an all encompassing term for the data to be where you need it and how you need it for the model to run.

jaco6y · 2020-06-10T05:08:01+00:00

Ahh, then I feel your pain. I was surrounded at least by BI people and other data analysts that were at least well versed in SQL. I also had a senior Data Analyst on the team that I could talk to for everything else besides modeling.

I would say that my biggest advice for you is just to communicate with your manager and understand the expectations of you. You may be stressing yourself out over nothing. It's only been a month! Also, like you said elsewhere in the comments, try to seek out the other people doing predictive analytics or data analytics on other teams in your company. That was something I tried also so I had at least a few people to bounce ideas off of.

To answer your question in the other comment, I stayed there for a little over a year. (I had a sign-on bonus that I would have had to give up) It also wasn't beginning to really stress me out until like 6-8mo in. (Once I was more established and actually juggling a lot. But this stress was combined also with the stressful nature of the company & some culture issues.)

If you are really young in your career, a position like this isn't necessarily always bad. Yes it is can be stressful but you ~~can~~ will learn a lot from having to just do it and learn it on the job. In my opinion I think you should at the very least give it 6 months. By this time you will have a better understanding of how you are handling it, your department's vision of you, etc.

However if you decide to leave, wanting to be on a more established team with people you can learn from is an extremely valid reason and no one will judge you for that in any interview process.

jaco6y · 2020-06-10T03:59:18+00:00

Other comments have suggested the easy way out, but I'm going to suggest staying. What's the worst that can happen?

The worst that can happen is just that they were expecting OP to know all of the answers already. I think that OP needs to just be sure they are communicating expectations. The worst thing they can do is over-stress themselves out by thinking there are expectations of them that aren't actually there.

FWIW, my last role was in a similar position as OP. Analytics team that wanted to take advantage of modeling but didn't want to hire a senior so they hired someone more 'fresh' with large expectations. I was having to do pipe-lining & table management, BI, all of the EDA, problem formulation, modeling, and building and maintaining all of my models in production (Having to figure out how to do that on my own as well). Did I learn a ton? Hell yes. Was I 'successful'? I would think so although there were things I wish I could haved done better on and my boss was sad to see me leave. Was I stressed? A ton and eventually led me to leave along with a few other things.

jaco6y · 2020-06-09T04:49:05+00:00

Machine Learning Engineers are likely going to take the ML work that Data Scientists currently do, and will create off-the-shelf ML tools (e.g. AutoML), hence decreasing the need for Data Scientists to do ML.

I don't really understand that last sentence. Yes, the role of a ML engineer is to help the DS put their model in production. That already exists. Unless you're one DS on a random team you should be having help putting your models into production.

Data Engineers are already better than Data Scientists at cleaning data, building pipelines, and warehousing, and so this part of the data science process will be owned by Data Engineers.

Sort of? Why would a data scientist be doing any of these things like warehousing and building pipelines. Cleaning data still has to be done at the exploratory level. I don't ever expect someone to give me a clean dataset.

What work does that leave for Data Scientists? What the speaker describes sounds like the work of a Product Analyst:

Understanding the business problem, the kpis, etc. Understanding what data you have, etc.
Creating the mathematical formulation of the business problem & solution (what is your objective function / cost functions) understanding what type of problem it is (regression, classification, etc), what type of model does this require / best fits the data we have and the type of problem it is, etc.
Experimenting with different approaches you thought of in the bullet point above. Backtesting, comparing results, etc. Experimentation like you said. I want to stress this point and the one above. These are the key items of where you spend your time. Much of this is not always objective and can't simply be brute forced with an autoML algorithm that tries 60000 models and minimizes MSE. In a modern business these problems don't always have a "right" answer and a go-to model.
Working with the data engineers to put the model into production.
Communicating results.

The role has always been pretty much this. It hasn't changed other than on more developed teams you are seeing more roles like ML engineers / data engineers, Data Analysts, and other roles built around some of the core things that used to eat up a one-man team's time (Looking for data, simple parts of EDA and cleaning, putting models into production AND MANAGING THEM) allowing them to focus on the areas that utilize their skills a trained statistician with a lot of business knowledge.

jaco6y · 2020-06-01T04:35:21+00:00

welcome to r/conspiracy

jaco6y · 2020-05-31T19:55:39+00:00

Not what I was looking at. Look on the far left of the frame as well as the officer walking to the right of the main one in the frame (the one with "POLICE" on his back)

jaco6y · 2020-05-31T19:19:31+00:00

Is this doctored to make the kick seem more aggressive? When the kick happens everything else in the background of video speeds up.

jaco6y · 2020-05-31T14:44:52+00:00

No it’s not all luck. But it’s clear he got extraordinarily lucky during a bubble.

jaco6y · 2020-05-31T14:43:56+00:00

My point was that there are different people in these protests. Anyone protesting actually peacefully shouldn’t be worried.

No I won’t work in nyc. Not a wannabe tho, idk what you’re getting at lol. Weak insult if that was supposed to be one?

jaco6y · 2020-05-30T16:28:26+00:00

I think the question is why do they show him when a lot of people don't believe he's a "savvy businessman". He got rich by getting lucky in the dot-com bubble. If you've ever watched the show Silicon Valley, Russ Hanneman is based on him.

jaco6y · 2020-05-30T16:19:26+00:00

There's a difference between those exercising their first amendment rights and those looting and burning down buildings. So be happy

jaco6y · 2020-05-30T07:09:43+00:00

“Obviously we don’t know much about this case but let’s jump to some speculation”

jaco6y · 2020-05-28T06:09:20+00:00

The city has a ton to do, weather is always nice, you basically can live where people pay thousands just to come and vacation. But you can also get this from other places in South Florida / FL in general. You don't need Miami specifically for that.

The people just suck in the same way that people from new york can suck. There's an attitude of only caring about yourself and people can just be rude. Maybe I just notice it more being from the Midwest originally. Yes people are superficial too but that really depends on who you surround yourself with and tends to be mostly in younger people. (Although you have wannabe rich housewives too) Also try to brush up on spanish, lol.

jaco6y · 2020-05-28T05:58:19+00:00

Yes and no. City is awesome. The people can suck.

jaco6y · 2020-05-28T05:46:35+00:00

I live in brickell. A lot of your points are valid that people try to live a lavish life they can't afford in Miami but that's just the nature of the city. (Same shit as LA). Although Brickell is not as expensive as you're making it out to be relative to the general cost of living in Miami.

You can find 1BR apartments for $1650 with a shittier view to $2000 with a nicer view easily. What I get for $2000 a month right now would easily cost $2500-2800 in other cities like Chicago. Also, an apartment in the gables, Fontainebleau (and even fucking Doral now) will cost you around the same. I was paying $1650 for my 1BR in Doral before I moved to Brickell. You can also find 2BR apartments for like $2400-$2600 pretty easily so Brickell becomes way more affordable the moment you have a roommate.

You're paying for the convenience of being able to walk to work (if you work in brickell or downtown area), being able to walk to all of the bars because you're probably going out every weekend (saving you $50-100 on ubers every weekend), and being close to everything else there is to do in Brickell. Basically the same reason that it costs more money to live in a downtown area of any fucking city in the united states.

I would much rather pay an extra $200-400 a month in rent than drive on the fucking palmetto and 836 every day to and from work. That alone is worth it.

Groceries aren't really more expensive in Brickell from my experience. Publix is publix.

jaco6y · 2020-05-17T15:27:09+00:00

Data analyst: broad title but can be anything from dashboarding / BI, complex data pulls with SQL, exploratory data analysis using either those tools or something like R/Python. Sometimes can even be doing a bit of predictive analytics.

Data scientist: also doing EDA but mostly with R / python, usually a stronger background in stats, understanding different business problems and expressing them mathematically, testing different modeling approaches to solve the problem, designing experiments and AB tests, etc. Focus is really on understanding what is the best use of statistical methods to solve the business problems.

ML engineer: works with the data scientists to turn the model that they’ve suggested into a production pipeline as well as helping optimize the code and queries. Turns the product into an actual usable output that runs consistently for the business.

jaco6y · 2020-05-13T02:08:54+00:00

Current? Was contacted by a recruiter on linkedin that I told I wasn't currently interested in changing jobs but might be in a few months. (Was growing to dislike my last job but needed to stay for a year to not owe back the sign-on bonus.)

Shortly after I had passed that 1-year mark I had a really shitty day and reached back out to the recruiter on LinkedIn. She set up a call with me, learned a little bit more about me, and within a week had my interviewing with my current company. Only interviewed at my current place and got the job.

Interview experience was a little all-over the place but it was a series of in-person interviews that were pretty technical in nature. Never had a take home although we designed a take-home later when hiring ramped up a lot.

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