HN user
There is so much variation from company to company within each of those titles that the distinction between them basically doesn't exist. So if you particularly care about the title - apply for jobs with that title.
It's unlikely they will move out of the market which gives them the most recognition as a brand and accounts for a disproportionately high % of their revenue.
I use Blank New Tab Page on Chrome - https://chrome.google.com/webstore/detail/blank-new-tab-page...
Originally I got it because I noticed that seeing the same few sites on the new tab page influences my browsing patterns. I suppose it also works for avoiding messages from Google.
Is there any conclusive evidence for this?
Over the last day since you wrote this I opened an account with Tandem, had to start a savings account with them even though I wasn't interested in it, applied for the cashback card, got declined despite of a good credit score, and closed my account. The person who I contacted about the account closure was ending his messages with ":D". Just a bad experience overall and I wouldn't recommend it.
Revolut isn't just a prepaid card. What they offer is very similar to Monzo, even if they started out differently. That's why they're competing with each other and it's fair to compare them.
Yep, that's a massive positive that I forgot to mention. In that space they're competing with Revolut rather than the traditional banks. Many people's setup will be traditional bank for everyday banking + Revolut for travel. Monzo can potentially replace both if it gains traction and people trust it enough.
Can you top up your HSBC account with a credit card from another bank? A Monzo account is just another bank account.
That's a good point. But with the low exchange rate and a modern app, they're also (willingly or not) competing with Revolut. I can top up a Revolut account with a credit card.
This is where they actually bring value right? - Delivering a good banking product outside of the UK.
Because as a UK customer I see no reason to switch after trying Monzo for a couple of months.
Positives:
- a nicer mobile banking app
Negatives:
- no credit cards and no option to top up using a credit card from another bank
- no physical branches
Everything else is comparable to most other banks in the UK. Which isn't bad, just nothing worth switching banks for.
Programmers who write code like this should not be allowed to practice.
This is really misplacing the responsibility and shows a lack of understanding about what is actually happening in the industry.
Yep, you're right, I misunderstood.
My disagreement is towards the wording of the part where one may assume a memory is being forever altered or modified and I don’t think that’s how our brains handles storage.
The majority of cognitive neuroscience disagrees with you. Doesn't mean that you're necessarily wrong but you'll need to make some really good points for your argument to be taken seriously.
The brain doesn't know to overlap memories by some hidden timestamp of oh these both happened at the same time!
It kind of does. Each time you recollect something you change the memory of the event (this is also mentioned in the article). Repeated recollection can have varied effects from (simplified) "I don't recall the original event, I recall the last time I thought of it" all the way up to creating false memories. I think there's been quite a bit of research on how this relates to interrogation techniques and the reliability of eyewitness testimony.
Any reasons for this? Not knowing too much about the space, I would say the exact opposite - consumer desktop apps are less and less likely to succeed while enterprise and industry-specific ones still have a place.
What would you like to see in the better version of Python?
I don't think that OP is comparing Rust and Go. Just pointing out how with Go there is a list of points of criticism that are frequently brought up, and he's asking if there are pain points that can be raised up in a similar way with regards to Rust.
Khan Academy has content all the way up to multivariable calculus and differential equations. And for more advanced material than that you'll need textbooks anyway.
Kudos to you. Speaking as someone who has taken their eyesight for granted, it's absolutely incredible that anyone can do this at all.
You mean like keras? or tensorflow? Or base random forest. You know, like the original Breiman implementation.
...
Since we're moving some big applications to keras/ TF we do use python and will be using more in the future.
Not sure if I misunderstood, or you're contradicting yourself there.
R is far superior in its the quality of the packages, their documentation, their ability to behave predictably on a given data type.
I not only disagree but I think that the exact opposite is true for each one of these points. But if things are working well in our shop, I'm not going to try to convince you otherwise.
built-in dataframe support
Not an advantage if you ask me - exactly because data.frame is built in, people have been building their own versions (tibble, data.table) instead of improving it. That's how R ended up with 3 different structures that are similar but have inconsistent apis and behaviour.
lots of domain-specific packages
That's true.
more consistent interfaces for basic statistics and machine learning models
Can't disagree more - there is no one go-to library for ML in R (like sklearn in Python) and each package has it's own strange interface and implementation.
Not sure how that's relevant. Everything is written in something else.
Python itself is written in C. The Julia github repo shows Julia 68.2%, C 16.3%, C++ 10.4%, Scheme 3.2%. R is a mix of C, C++, R and some Fortran I think...
SAS is still used by companies who don't want to use open source tools and would rather pay a lot of money for an established product name and have a support line that they can call if anything goes wrong. Understandably, it's slowly dying out.
then maybe ESL or some advance course
A few of my friends work on Data science and they said that maths isn't that important
Without the math you won't understand anything in ESL.
Which might be okay if the job doesn't require you to go into that much depth - some data science jobs are more focused on research (very math-heavy), some on ETL and/or engineering, others on business understanding and communication; it's a really broad title.
Depends on what you're interested in, your goals and your starting point. You have two main learning lanes:
1. Theory.
Math, calculus, linear algebra, probability, statistics, ML algorithms. ISLR is a very good beginner-ish resource that helps you understand the algorithms but doesn't go too deep into the math. As you go deeper, you may realise that you have gaps in your math knowledge and you need to cover a lot more probability, calculus and linear algebra.
2. Programming.
2.1. Good software engineering practices - writing maintainable code, design patters, version control, unit testing etc.
2.2. Tooling - knowing the language-specific ecosystem of libraries (the OP is an attempt to teach you this in two languages at the same time). This is what most beginner resources focus on; your knowledge here has the least transferability and tends to go out of date quickly. Still, using the right tools and knowing them well goes a long way.
Yeaah, to your last thought - without being too cynical, looks like the author wanted to write a beginner data science book but realised that the good beginner data science books are already written (as well as many bad ones).
I can list a bunch. But the answer depends on a lot of things.
1. Which language?
2. Do you have any background in programming and if yes how much and in which language? Beginners can range from "I don't know what a loop is" to "I've been a front end developer for a few years and want to try something new". These people will obviously need to approach things very differently.
3. What's your math background? Do you know what a derivative is and how matrix multiplication works? Do you want to go in depth or do you need just a general understanding of the algorithms? Some people will need to start at precalculus if they really want a solid foundation. The same people also most likely won't have the patience or interest to stick with the theory for long enough.
4. How will you use what you're learning? Is there a specific goal related to this and does it have a time horizon?
...
Anyway, I'm rambling. Data science is just programming and statistics. Your general learning lanes are
1. Theory - calculus, linear algebra, statistics, ML algorithms.
2. Programming.
2a. Good software development principles - writing maintainable code, version control, testing, design patterns etc.
2b. Tooling - learning the language-specific ecosystem of libraries. This is what most beginner resources (including the OP) focus on and is also the one that constantly expires and has the least transferability to other skillsets and fields. Obviously, it's still necessary - using the right tools and knowing them well goes a long way.
You mentioned ISLR - it's a good beginner-ish theory book that helps you understand how the algorithms work without going too deep into the math.
This just doesn't seem to have a place.
1. It's aimed at beginners.
2. If you're a beginner, you're best off picking one language and sticking with it for a while.
3. There are so many other beginner resources that are much better.
So you're telling us not to waste our time with R or Python... but to constantly interop with R and Python?
If an industrial grade NN library was written for it
Can you give tell us which language and industrial grade NN library you're using?
Because from where I'm sitting I see that Python is the only language that gets first grade support for both Tensorflow and Pytorch. It's so ahead for working with NN that it's not even close.
Yep - bank transfers are free and fast, all my bill payments are automated, contactless is everywhere - it's great.