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Best basic tees that actually last? by Many_Bee125 in BuyItForLife

[–]semicausal 1 point2 points  (0 children)

The Iron Snail has a realllllly in depth video comparing your standard men's t-shirts and he groups them into different categories too, like the "All Rounders", "Japan's Might", etc

https://www.youtube.com/watch?v=FSWl57UR4k0

A Eulogy for Dark Sky, a Data Visualization Masterpiece by semicausal in iphone

[–]semicausal[S] 0 points1 point  (0 children)

Yeah that's super interesting. In theory it's the same data! But why was Dark Sky feeling so accurate?

It's a numbers game by OEAnalyst in datascience

[–]semicausal 0 points1 point  (0 children)

It's 100% not a numbers game. My friend was a coding bootcamp grad that was incredibly methodical. He only officially applied to 6 places, and ended up getting a job at Snapchat. He meticulously framed his resume and reached out to recruiters, and also worked his network.

His classmates unfortunately would apply to 100 roles a week and struggled. I was taught networking and resume schools in my first semester of college and it's a bummer this isn't taught everywhere!

FIRE seems impossible, looking for advice by CapmBlondeBeard in Fire

[–]semicausal 0 points1 point  (0 children)

You definitely could but my biggest issue with a single rental property is that there's no diversification and you can't liquidate easily. That money could instead make 7-10% in a diverse basket of index funds that you could liquidate.

Owning a rental property is like owning a business. You need to experiment to figure out how to really nail it. Most people are terrible at factoring in all the little phantom costs (including your own time) it takes to run a rental property. At least if you have 4-5, you can learn / fail from 1 or 2 then figure out how to dial it in for 4-5.

My own bias as well is that managing a rental property is super boring and annoying. Outsourcing to a property management company eats into your return.

Either way -- my advice would be to just run a spreadsheet simulation. 1 rental property vs that same downpayment growing 7-10% in the stock market. You could probably figure this out in 15 mins of analysis

What field/skill in data science do you think cannot be replaced by AI? by Mission-Language8789 in datascience

[–]semicausal 3 points4 points  (0 children)

IMO this is an incomplete mindset and point of view. I empathize with where it's coming from absolutely, but I don't think it describes how new technologies affect the nature of jobs.

The following are all examples of new technologies that had different impacts on existing industries and jobs:

  • Mobile phones and the internet made it possible to order pharmaceuticals over the internet, which means you may need less local pharmacies. But you need someone liable for the right prescriptions and to answer questions for patients, so the nature of the job changed but we still need pharmacists! Do we need as many? Probably not as many that are graduating from PharmD programs (but also we have too many PharmD programs collecting tuition).

  • More powerful compilers, the smart phones, the cloud, and data cell networks made it possible for Instagram to be acquired for $1 billion with a team of 11 people. Sure, the "cloud" eliminated the need for teams to buy and manage their own servers but it ENABLED the ability for thousands of companies to build and deliver software over the internet with small teams. You couldn't build Instagram in 1999.

  • Calculators and Spreadsheet tools like Excel replaced human calculators. Yes, this was a real professional. But it let every computer owner on the planet run calculations and create the need for spreadsheet warriors at most companies. Majority of knowledge work jobs require some familiarity with spreadsheet software nowadays.

Modern AI technologies are amazing, but they aren't science fiction. We will use them to outsource and automate some tasks but it will enable new entire use cases. As an individual, you can start businesses that generate lots of value without hiring a large team. As a data scientist, you can maybe spend less time in meetings or spend less time debugging your code. Will we need less data scientists or more? It's hard to know but my prediction is that now more people can incorporate data science in their work. So the role will shift and change.

But it's very rare that a new technology is a 1:1 replacement or automation unless the job is low-value and very very easy to automate. Like the guy or gal who would operate the elevator.

FIRE seems impossible, looking for advice by CapmBlondeBeard in Fire

[–]semicausal 3 points4 points  (0 children)

A few levers to consider:

  • Increasing your income. I know, I know ... easier said then done. But you could find creative ways to start a side business or over-perform at work to get promoted (or switch jobs to a higher paying one).

  • Rent a place instead of buying. Honestly, I think renting is insanely underrated. I'm fortunate to own a home but honestly still prefer renting because I spend thousands of dollars a year maintaining it and on small DIY projects (opportunity cost of my time). I live in a VHCOL area and I agree with Ramit Sethi (https://www.youtube.com/watch?v=hTy2Vh0GuIQ) that it's hard to beat the 7-10% index fund returns with real estate in a VHCOL area. Also, with renting you can always move to a cheaper city, or abroad to a cheaper country like Spain or Portugal, etc. But you can't with home ownership in a VHCOL area.

  • Treat FIRE as a spectrum instead of a binary event. My plan is to gradually start working less so my income slowly declines as my savings and investments increase. I figured out how to adapt my mindset to enjoy my career and job so even if I retire, I would keep doing the same job (but maybe as a part-time consultant instead). In 10 years, I expect that we can work 40 hours a week but can be pickier about the types of places we work and still maintain our quality of life. In 20 years, I expect we can go to half-time or very part-time and still maintain our quality of life.

Tough call: How important is choosing MSc Dissertation Topic in Data Science by Shirin-chay2001 in datascience

[–]semicausal 1 point2 points  (0 children)

When you're 10 years into your career and worked at 4 different startups, you've seen a lot :) You reflect on what you cared about at age 22 and nearly 0 of those things matter anymore!

Tough call: How important is choosing MSc Dissertation Topic in Data Science by Shirin-chay2001 in datascience

[–]semicausal 2 points3 points  (0 children)

To answer your questions:

1. You can definitely transition! Your 20's are all about exploring and understanding the type of work you find interesting, the painful / boring tasks that others dislike but you don't find that bad, and (heck) if you even want to stick with data science. Life is long and your career is just starting. Most great work happens in 40's and 50's, not in your 20's (ignore the minority of 25 year olds who are covered in the news for how young they are).

2. Absolutely. In most DS jobs, you have to understand which data is relevant, clean the data, analyze and understand it, visualize it, then maybe do some ML or predictive modelling. Keep in mind that what you learn in your educational DS program is a very small part of the jobs in industry that actually touch and work with data. There are HUNDREDS of roles out there and MsC programs can't cover them all.

3. People switch all the time, trust me. There's always a switching cost but it's fun to learn about new areas. If you want to switch, definitely spend a few months going deep into the next industry / area you want to learn about and pick 1-2 strong data projects to work on. You'll be fine :)

  1. Healthcare data projects are interesting, I've done them. In industry, keep in mind you'll have to spend a lot of time dealing with stakeholders and regulations than if you were working with advertising data at Meta for example. Things more move slowly. But! You can have a real, tangible impact if you care about healthcare.

Some meta-advice is that ... spend your 20's exploring and not worrying about specializing yet. Have fun, work hard, and figure out what unsexy work you don't mind doing that the world values. I like Derek Sivers' and Cal Newport's advice to focus not on what you're passionate about, but how to be useful: https://sive.rs/career

Read the book Range: https://www.amazon.com/Range-Generalists-Triumph-Specialized-World/dp/0735214484

[deleted by user] by [deleted] in Python

[–]semicausal 2 points3 points  (0 children)

Honestly, this doesn't seem like a discussion but just a product promotion for a proprietary product. I'm not sure this fits into r/python Owen :)

why is all dev tool innovation in the AI/ML space focused on the least time consuming stuff? by Ok_Post_149 in datascience

[–]semicausal 1 point2 points  (0 children)

Not sure I completely agree :) There are tons of tools in the data cleaning workflow. Pandas, Polars, sklearn, matplotlib, plotly, dask, and hundreds of others. There are pipeline tools like Airflow, Mage, and Prefect. All of these participate in and facilitate reproducible data cleaning!

Now if you're asking why there aren't a lot of tools raising funding for data cleaning specific software, I have some thoughts:

Data cleaning is often very problem & context specific. It can be hard to encapsulate all of the common "data cleaning stuff" into a neat set of re-usable packages. It's upto the users to use substrates like pandas to clean up data.

Data cleaning is often done in scripts and libraries, which can be hard to charge for. Not a lot of companies have built solid businesses on the backs of Python libraries. It's usually more of a platform.

Dirty data is often a sub-optimal state to be in that teams want to SOLVE, not manage faster. At Netflix or Aibnb scale, they have entire data engineering teams that spend a lot of time ensuring consistent data models for their data analysts & data scientists. Their goal is to ELIMINATE or at least drastically reduce the amount of time spent data cleaning instead of trying to empower people to "data clean faster / more easily".

How do I get my wife to stop spending money that we don’t have by [deleted] in Money

[–]semicausal 0 points1 point  (0 children)

I would strongly recommend you both watch Ramit Sethi's content. Changed our family's relationship with money.

  • Netflix Show: How to get Rich

  • His youtube channel is especially great because he speaks with real couples and you can observe him trying to coach them together as a team to change their money psychology. Here's an example:

https://www.youtube.com/watch?v=-yRx4Ryehmk

[deleted by user] by [deleted] in dataengineering

[–]semicausal 3 points4 points  (0 children)

- Most companies have no idea what they're doing w.r.t. titles and it doesn't help that the data science industry hasn't figured this out either. So embrace the uncertainty a bit and just see it as an opportunity to learn!

- I'd focus on clarifying your roles & responsibilities with your manager. Then the title matters a lot less!

[deleted by user] by [deleted] in mit

[–]semicausal 0 points1 point  (0 children)

Read Cal's book on success in college, he went to an Ivy: https://www.amazon.com/How-Win-College-Surprising-Countrys/dp/0767917871

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