Hello, lovely content! The audio has quite extreme resonances (especially around 650Hz) because of the acoustics in your room. They're unpleasant and distracting from your otherwise captivating content. I want to give you a tip that if you have some kind of "EQ" or Equalizer available in your editing software, you should make a deep cut around 650Hz. It might surprise you how much clearer and better it will sound. Alternatively recording in a room with more stuff in it (furniture, carpets, book shelves) can remove the need for any post processing of the audio. All the best
I actually tried but it doesn't make much of a difference. Some of these videos are older and I have a newer setup now. Let's see if that'll be better! I can definitely not just buy more furniture :D
@@UndineAlmani thanks for trying! hmm it seems very weird to me that it didn't make a big difference. i'm going to investigate. i hope you don't mind (also i hope that you don't find my comment rude, i only mean to help out as i am an audio professional with time on my hands - most people will only notice these things on a subconscious level so i think that might be an explanation here. I will be back)
@@UndineAlmani i uploaded a video on my channel now where i took a snippet from your audio to showcase the resonant frequency. I hope you don't mind! ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-ZYO0sKtZzUU.htmlsi=U6LOq0IU3NfjI--9
@@UndineAlmani I think RU-vid removed my comment because it had a link in it. so I'll say it like this: I uploaded a video demonstrating the resonant frequency in your voice recording. I hope you don't mind. it's the only video on my channel
The only data scientists I heard of were actually people with a degree in computer science and specifications in data analysis. I thought these guys were primarily developing new mathematic models for analyzing data, like neural networks, LLM and so on. That’s what I think of when I hear the term „data scientist“. In general most people with an STEM degree are not doing anything scientific nor something you would need an hs degree, especially after they bachelor. They just apply knowledge and models in standard processes. I had an internship were the senior engineer was basically just planing smaller construction sites and a friend of mine who made her bachelor in environmental engineering is now calculating co2 emissions of products by measuring some specifications of input materials for their products and use simple stoichiometric equations to calculate the emissions. In university we learned how to make life cycle assessments and modeling of processes and balances, which was a pain in the … to make.
It has been this way for some time now... I remember I spontaneously made this video after I made fun of some guy on TikTok about his "data science bachelor's degree". And he sent me like 2 pages of text why I suck and his degree is amazing and shit. That initially triggered the video. Cause I kept thinking: You know what, I got a certificate too... and it's such BS... eventually I feel like I just got certified for what I already knew, refreshed some of it, okay... and learned a new / old programming language / or added some code / refreshed what I learned a while back... I prefer the path of people just "becoming" analysts as in having a background in STEM, and deciding they like to work with datasets, models etc. As scientists, who can see further than what are just the numbers. But honeslty, if you advertise (like MOOCs and at this point whole universities do) to everyone basically, you will introduce maths to people who don't get maths. And who just look at numbers like a person who polishes a shoe looks at it, but not like someone who knows how to make that shoe from scratch. That's my whole issue with it. The promotion of these fake degrees that do not give you a deep expertise, and you end up as a master of none. As you say, it is quite natural to acquire that knowledge along a course of study.
thank you so much! i'm currently studying math degree and some first year modules are joined together with physics/computing/data science and cyber security students. data science and cyber security students are the most vocal ones about hating maths in general 'i hate this bs, why do i need this.. etc..' when talking about introductory level college/uni maths (calc1/2, linear alg. etc.)... both cybersec and data science are such buzzwords that attract total riffraff right now looking for a cushy job.. when usually people who work in those fields are stem/cs grads (for anything "data sciency") or computer engineering students (for actual "cybersecurity" and not just glorified low level IT).
You're just glorifying science. The title of "scientist" doesn't have to correlate with the amount of science you do. If you do a little bit of science and follow scientific methodologies, you're a scientist. If you write, you're a writer whether it's short stories or novels. I'm a computer science graduate. I could get into a scientific field.
What data scientists even mean is vague. I followed two a few months long data science boot camps, one of which had a few days(!) of coding at most. Also, working with people who have a cs degree made me realise how much I have been lagging behind in so many basic cs skills (not that they had data skills themselves). We need some more descriptive grading for data skills to make any sense out of what skills a person possesses. Also, data skills come in handy when you have some specialization in some actual science.
Yes! This. Many are just code camps sprinkled with some (very!) basic statistics. But my beef is also with whole degrees. Because why not just study mathematics, if you're actually good at mathematics. Why make a "degree" for a bunch of people who don't want to do the work? So people who want to do even less work in life (HR) can hire them for a fancy but useless title? It's pure ignorance and disrespect.
ach undine, ich wollt mich gerade in dem bereich weiterbilden weil ich für nen echten science abschluss nicht genug hirnschmalz hab. jetzt bin ich demotiviert für's leben ^^ ich glaube als datenanalyst ist man eher sowas wie ein trüffelschwein, man schnorchelt wild im wald herum und muss alles umgraben um den einen versteckten leckeren pilz zu finden (die information auf die man aus ist) und alles was es dazu braucht ist eine gute nase bzw ein paar nützliche tools und herangehensweisen. mit wissenschaft hat das wirklich wenig zu tun. wissenschaftlich war nur die entwicklung der methoden dafür und so. vielleicht ist das irgendwie hängengeblieben.
Mach's nicht! Studier einfach Informatik, das kann man auch an einer Hochschule. Immer noch besser als "Data Science". Ich hab sogar 2 Weiterbildungen in dem Bereich gemacht, in Python und R. Sowie 1 Jahr als "Data Scientist" gearbeitet... es war soooo ätzend. Und du wirst in der Industrie auch total außen vor gehalten. V.a. wenn du mit confidential Zeug arbeitest, ist ja alles "need to know". D.h. du wirst nie so tief bei einem Projekt (hence its team) dabei sein wie ein Ingenieur, Developer oder ... scientist :D Das Ding ist, die Nische an sich ist total cool. Ich liebe es zB auch sehr sauber und statistisch korrekt mit Daten zu arbeiten, was auch viele Wissenschaftler übergehen... aber in der Data Science Ecke wird das dann wiederum so hochstilisiert zu etwas Eigenem. Wenn es eigentlich viel geiler ist, gleich ein MINT-Fach zu machen. Also nur Mut! Ich glaube, mit etwas Arbeit an sich selbst kann man da einen tollen Ing-Studiengang finden oder was an einer Hochschule und überall wo gebaut wird, wird auch analyisert.
@@UndineAlmani Especially psychoanalysis. It's basically quack science, which has no practical use in reality. Based on Freud everything is related to sex (with your parents), sick shit! Did you know that Freud took cocaine? I would rather talk to someone considering themself a clairvoyant.
data science covers a broad range of expertise. It is not just about playing with data. Companies hire PhDs and postdocs as data scientist to develop novel NNs, or modify the autograd such as zero coordinate shift or create a NN which can satisfy a functional operators such as FNOs to solve parametric partial differential equations etc. Then there is digital twins which is the extreme of data science.These things do come under problem solving skills which an actual scientist would do.
It's a sub-discipline. None of this comes close to science. It's appropriation and we need gatekeeping from this bullshit. It's monday morning left ass cheeck work. It doesn't reach the level of difficulty by far. Not in the slightest. None of the degrees I've looked at. This shit is a TRUE subset of maths.
Science = have a hypothesis, conduct experiments, and formulate results as a theory. Data Science = have a huge amount of data, create crawlers, and aggregate results The two can complement each other, but Data Science != Science.
Lol. No, you fetishize the term "scientist" in an elitist way. There are plenty of fields, other than physics, in which the data gathering and processing is waay less involving, and you could do a good paper by yourself and even as an undergrad. Typical "data scientists" here, really do have the methodological skillset to produce such "cute" papers (which by volume, is most of the "science" in general) and may lack just the field-specific expertise component - which they can pick up quite easily, filling in the field specific knowledge up to the "masters" level (as by your example, just in an opposite direction). If you want to differentiate "a real scientist" from essentially a grad student - congrats, you are cementing the hierarchic structure of the academia, and diminishing a ton of important but not very sophisticated scientific work, just on the basis of your personal aesthetics...
…also, about the supposed experimental data gathering requirement and physics: isn’t a great chunk of theoretical physics, just a mental gymnastics in maths, „invented/explored” on a whimsical assumptions, which won’t ever match to anything in real world, just because of their apriori nature, and them being a gamble that may or may not lead to something interesting? Is such modelling even science, then?
Data analysis is like data entry: its a task, not a title. Data is ubiquitous, everyone uses data at every level. Best case scenario: you're a superuser for very niche database software.
1. "Science is a systematic discipline that builds and organises knowledge in the form of testable hypotheses and predictions about the world." - (en.wikipedia.org/wiki/Science). I would be more than happy to have a better definition and be enlightened. 2. The term 'Data Scientist' is flawed as data is the end product of observation. So what has been largely referred on the video is 'data analysis'. Which is knowing what tools to use to understand the result of the observation at hand. 3. Craftsmanship is not lesser than science. If there were no craftsmen, we would not survive. However, the critical thing is the ask the question "How can I be a better craftsmen?". 4. It's ingrained in human psychology to be attracted to titles such as 'scientist'. This applies to both parties. When people dealing with data analysis deem themselves 'data scientist', 'real scientists' feel like their territory is being invaded, hence the term 'real scientist'. I think labels are distractions. Are we focusing on the prestidge the label is attached with or are we really focusing on the problem at hand? Because at the end of the day, what matters is the good quality output. 5. As a side note, I think we can discuss the science aspect of data in Mathematics or Computer Science as these are the disciplines that establish our relationship with the tools we build to use to analyse. As an end note: I had a bit of trouble reading the annotations as they are disappearing quickly, are at the bottom of the screen and the last line disappears behind the video controls when I pause the video.
