Richard Feynman's father taught him: you can know the name of the bird in all the languages in the world and it tells you absolutely nothing about the bird. The map is not the territory. This works both ways: don't read too much into the names of things; but also, don't get too upset when the names of things are wrong.
Yeah! She was certainly quite animated over something as benign as mislabeling a discipline. But, I suppose if I had earned a degree in physics (a hard-core science discipline) and found myself surrounded by computer nerds doing analytics with data and calling themselves scientists ... I might be a little triggered. Not enough to create a YT video and rant about it, though. LOL. If they called themselves "analysts" doing data analytics then that might be a more accurate use of the English language. But, this example of data "scientists" not really being scientists at all is hardly the most egregious example of inaccurate language in our society.
@@markteague8889 Data Scientists & Data Analysts are actually two distinct groups (professions/qualifications), and they are very understandably/reasonably distinct enough. There is overlap in knowledge & tasks, but the DA does not have the set of academic fundamentals of the DS. If expertise in Statistics or some other Math sub-specialty is not a 'science,' they probably wouldn't mind. They could be Data Mathematicians, but that those two terms do not congeal as well together as the objected form. Anyway, if a rant could be amusing, we would endure sitting through the entire thing & feel entertained by it; it seems many of us have just discovered that such exists.
Spot on, I enjoyed that. I did a three day course in FORTRAN 4 in 1964 to help with research in physics. I now realise that I was a data scientist on the side for decades.
I’m a machine learning engineer. No science, I don’t really build models, I collect data, get a model from R&D, and build training and inference pipelines, then jam the data through.
And software “engineering” isn’t engineering. We’ve had 15 years of inflated compensation for programmers and stats folks and opted to invent titles for themselves to justify ZIRP compensation
The term data science is just a euphemism for companies collecting huge masses of data about consumers in order to get clues for new product developments and - even more important - for improving marketing their products. The term data science has been created in financial industry and consulting businesses, and academic institutions have adopted that term welcomingly in order to sell new academic courses and titles. She is absolutely right. Statistical analysis of large amounts of data is just a household chore of every proper experiment yielding data. Experiments at CERN, the European nuclear research facility, are producing terabytes of data per second which need to be processed, analyzed and condensed quickly because the next set of data will probably produced within a second. Field experiments in medicine and biology can (should) be based on sufficiently large data collections in order to generate meaningful results which have to be processed for decades - actually an essential purpose of computers in science and one of their earliest uses.
You are definitely *_not_* eurocentric @5:05 Once computers became commonplace I viewed them as a tool, pretty clean and easy to use. Never took a single coding course in my life, but in getting on with physics, programming eventually took up a sizeable percentage of keyboard tapping time.
And yet you probably don't refer to your self as a software engineer either. I worked with nuclear engineers, I wrote software for them, but I was not a nuclear engineer nor would I ever claim to be. I also worked with power engineers. I wrote software for them, but I would never claim to be one. But I was a software engineer. they needed to be able to communicate effectively with me and I had to be able to design and write what they needed.
Economist working with data scientists perspective. I think data science sorta makes sense as a relabeling of applied statistics, which is somehow more attractive to business people in HR or management. Or it often ends up being used for the computer science of data, since it's so heavily dominated now by computer science people doing machine learning and work with databases. Maybe the data science label captures better the intersection of statistics with computer science, though machine learning is also a branch of statistics, and is probably less pretentiously called statistical learning (one of the best books on ML for free is Introduction to Statistical Learning). I think in both hard and social/behavioural sciences, statistics is considered just 1 important tool. The data science perspective in industry right now is limiting because if leads to a mindset in which if only we apply some statistical methods and simple data analysis to supplement business intuition/gut and 'common sense' thinking we'd all get much better business decisions (data science is really more of an industry/private sector term, most academic or government statisticians wouldn't advertise themselves as data scientists). Which is a priori wrong and empirically probably wrong because we had really big investments in big data, machine learning etc...yet in general the last 15 years of data science in advanced economies have seen quite slow economic growth/quasi-stagnation, contributing to populism and other social problems. So the returns to big data and machine learning in business must in general be quite low and maybe negative net of the costs. Both data science and more generally software engineering/development labour markets now are I think in a major recession, and I wouldn't be surprise if this is something more long-term as companies realise they've overinvested in both big data and computer tech (I also find it strange that the word tech is now practically used to refer only to computer science/electronics related stuff. To me, mechanical engineering is in many ways more representative of 'tech').
I largely agree with you. If you're not applying the scientific method, you're not a scientist. If your applying a known principle of science, you're probably an engineer. Howevere... You are remarkably abrasive. Turn it down. You're not going to change hearts and minds with that attitude. You will not win friends and influence people.
What about writing algorithms,predicting trends,creating models and using different mathematical methods.Does that incorporate science?it has theory and application,uses empirical data,it's interdisciplinary.
I’ve been running up against this, myself and I largely agree with your points. I’ve got an MS in computational chemistry. I specialized in quantum dynamics, published, and it was a *grueling* program. I ended up learning shell scripting, git, python , MATLAB, C++, HPC, DSP in those languages, AND all the projective geometry, classical dynamics, statistical mechanics… and we had to visualize the phase space surfaces from the simulation data! The section where you mention that HR people don’t hire because they don’t think my degree includes these skills… it hit me HARD because I now work as a freelancer electronics hardware designer because nobody seems to want to hire someone with *only* an MS (never mind my three pubs…) in quantum *chemistry*. Turns out I’m good at analog electronics. Go figure. It’s all just E&M and wave mechanics anyway… I should get a data “science” cert… most of my cohort from that program work as data scientists anyway… ugh.
What are your thoughts on Statistics degrees? And people that work as "data scientists" after statistics degree. Would you consider them as data "scientists"?
Maybe your view is a bit physics focused. I work at a proteomics lab and we have approx 70% bioloists/chemists and 30% data scientists. Our biologists have minimal knowledge about programming and data science, mostly not exceeding Excel. They handle everthing on the wet lab side up the to mass spec. From there on we data scientists get involved. Getting from m/z spectra to analyte identification is everything but trivial. It is an active field of research. Nothing we do is vanilla machine learning. Expecting from the biologists to learn all about the quirks with machine learning in cross-link mass spectrometry would be unfeasible. In the same sense we data scientists do not know enough about biology to design and execute the wet lab experiments. I agree that the term "data scientist" is used inflationary but in the same way many "software engineers" are no engineers.
thing is, to correctly design an experiment you need to decide how you’ll be calculating the results beforehand. so to do any experimentation you are required to know the basics otherwise it’s not a scientific experimentation but just poking around some random data
Computer science is science. You're just trying to make yourself sound more impressive. Physics and Chemistry are not the only science. You're clearly mixing up boot camp, which is actually data analytics, with actual data science, which you can get a BS and MS degree in at my local University of Oregon. I don't call myself a data scientist, but I am in the 300 level AI/Data Science math. I authored the Theory of Gradient Relativity, so I do some physics you would love to read. I do data science from time to time for my company. Real data science has a lot of High Performance Computing. I do HPC in C++ using with data driven programming. It's not physics, but there is a lot of math. Asynchronous internet things are very hard. Almost all of the math is in the BLAS library or Math Kernel Library.
2:08 : specialisation is one of the ways we get productivity growth. Just because specialisation can (and has, in your experience) led to inefficiencies, doesn't mean the attempt is in the service of "wage dumping". If it could lead to a physicist getting more physics done they could end up getting paid more, to the extent that they are more productive as a result. It's possible it can work. It might depend on the specific organisation. In my domain (software quality), I see computer programmers reaching the wrong conclusions because they don't understand data well enough. Employing statisticians would help. I can imagine this happening with physics, but I also get your point that physicists are closer to an understanding of their own data.
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).
This is true in my university as well. It’s hilarious how little math most the data science students want to learn. I was quite annoyed by how little focus on math there was in my data science degree’s coursework and ended up just deciding to get another bachelors in math cause the degree was basically useless imo. Now I’m in love with math and want to get as far as I can from the applied stuff.
Why focus so much on job titles? They’re just like names - labels that don’t always reflect true value. Most scientists publish work that can’t even be reproduced. The real issue seems to be arrogance-people talking just to sound important, when in reality, they aren’t.
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.
With all the anti-intellectualism going around it’s always refreshing to see a PhD in a field bashing other fields of science they don’t understand ❤ You do realise that knowing how to do regression on a dataset has as much to do with data science as an electrician has to do with physics? Also, I’ve already sent the part about misogynistic universities to a few women friends of mine that do interdisciplinary studies and we’re all having a giggle about the fake elitism of the division of labour inspired by the industrial revolution ☺️
It's true she didn't deal with the word "science" in "data science". I think perhaps it was too obvious? But the reason "data science" is not a science (and she said this) is that in physics (and any science) you can't separate the data from the *experiment* i.e. the way you get the data. I agree. "data science" should be called "mathematical modelling" or "computational modelling" or "statistical modelling". I also think that it isn't clear with the new title "data scientist" what skills are being asked for --- programming certainly, python, R --- but after that, what? ANN? SQL? Web infrastructure? Privacy issues in data/health? Financial data? I think she went too far with "medical physics", this is a real science, could also be called "medical engineering". I perhaps also agree that employers and HR don't really know what skills the hard sciences offer, and they want those skills, perhaps on the cheap. Personally I don't care what they call it, as long as it is a job, if it's easy, so much the better.
The discipline not truly being science is just the reality. It isn’t science. This appropriation of language is pervasive throughout Western Society; and especially, academia.
Yep. This was a Marxist I take and nothing more. The critique itself is fine. The conclusions are flawed. Given the skyle of academic fraud now being uncovered across the world, none of us should be surprised.
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.
So data science it not about the science about data, it is a tool. Fair enough but now I'm curious, how is the science about data called? I'm seriously interested.
14:00 "it works that way, not the other way" - this makes sense to me because you can't really reason about statistical models without understanding what you're modelling. But in other sciences you do see some bad statistics that appear to be the result of scientists not understanding statistics well enough. I think choices of statistical models in e.g. climate science are... questionable.
Thanks! Finally someone mentioning it. "Data science" is used in many fields. There is not the typical Data Scientists job. E.g. Data "Science" is applied by engineers (mechanical, electrical, mechatronics etc.) to analyze systems or devices. They often use much more sophisticated algorithms, and more advanced maths than the usual guys calling themselves "Data Scientists". Thanks for calling this Bvllsh*t out! 😅.
Imagine all these people complaining about having to juggle too many subjects at once, burning out, and griping about competition and dividing workloads.
Great video, I wouldn't call that "data science" certification a degree, it's not a degree it's just a certification, your bachelor's is a degree, don't lessen it by calling a short non university credit awarded certification a degree. Cheers.
What’s the value of a degree and years of study if someone can do the same job without one of these “super important” titles? 🤣 It’s like talent and intelligence are being overlooked in favor of degrees. That’s not exactly smart!
I am a Professor of Data Science 🧪 and this actually made me consider changing my title! 😂 For starters one might be a little suspicious of any discipline that has to put ‘Science’ after the title…like ‘Social Science’, ‘Computer Science’ etc. No one talks about’Physics Science’ or ‘Chemistry Science’… but I guess ‘Data Science’ is the practice of obtaining information from data….📊 surfacing signal from noise….I also noticed few people do courses in ‘medical statistics’ these days…it’s ‘Health Data Science’ now…anyway interesting and thought provoking video… vielen Danke!
You should quit being a professor and go find yourself again. Don’t educate others if you don’t understand why you are teaching them. Second, I would avoid listening to a random lady ranting about things she just judges on her preconceptions and opinions. Aren’t scientists supposed to be more grounded and humble? Nope. Historically, they are not. That is a huge flaw in science as a discipline but I’m not telling you to stop calling yourself a scientist right? Third, science is a subset of philosophy so technically we could claim science does not exist without philosophy but philosophers aren’t petty like scientists trying to gate keep their tenured jobs that pay less than software engineers and computer scientists! Let’s keep it real and be humble.
@@arto00-g2nActually I’m a research professor so do very little teaching these days. So I’m not ready to quit just yet. But you are correct- I probably should be spending less time on TH-cam….😂
It's really not. People apply pre-made methodologies from scientific fields. If you ask me, I wouldn't call data analysis very scientific either. A lot of the work done in both those areas builds on processes of building, cleaning, maintaining, and connecting tables, primarily a technical skill that relies on relational database principles. The idea involves linking data based on common attributes and doesn't require analytical proof because the relationship is inherent in a data model. This is the foundational step in data integration but distinct from analyzing the meaning or correlation between the variables within those tables. Obviously, not everyone is the same, but more often than not, people link tables just because it can be done and then run models without proving the relationship. Connecting attributes is one thing since attributes are static and descriptive fields used to identify and categorize. However, dynamic fields encapsulating quantitative or qualitative dynamics happen at higher dimensional spaces. Therefore, their relationship needs to be proven. This rarely happens... apparently... don't ask me why. It's irrational. Nevertheless, without an analytical process you can't call sth scientific, probably not even valid. It's like the three-body problem. Just because some elements coexist in a system, it doesn't tell you anything about how or whether they actually interact meaningfully-you need to analyze their relationship.
Let's not lose the forest for the trees. Nothing she says contradicts your statement. But what you're saying is that there is some engineering activity involved in data cleaning/scraping/preprocessing. Let's just agree that the engineers and scientists should work together in the domains that best suit them. Thus you find roles like "data engineer" in industry, which is what your comment is addressing.
Show me the MONEY. I'll produce & deliver the work expected. Call my role Data Fakist if it makes you (not you personally) feel warm & fuzzy. Just show me the MONEY. There is nothing else to discuss. Any yibber-yabber besides this is the real BS. If I can put lots of money aside while at it to afford to move to a lower cost-of-living and gain more time to myself as a result, I will/can practice real science whether anyone has ever given me a pat on the head with a diploma or not. And if I actually do that, I will not tell a single one of your self-inflating souls what I will have discovered. Go F your degrees.
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.
Nothing that follows is about you personally; it's just reasoning out-loud. Most regular people are not psychologically attracted to titles, not even one spelled like S-C-I-E-N-T-I-S-T. We are however ingrained to be attracted to food, especially ones intensely colorful. The ingrain primitive urges of a human being is not concerned the slightest with what you call yourself, even if your first-name was Greatest, your middle-name was Emperor, & your last-name was Of-All-Times. You could manage to compel others to go along with your naming convention; you could even manage to destroy some defenseless country to make yourself remembered in history. In the silent recesses of a human being, we see you as a hopelessly ageing, debilitating, perishing animate object that the cosmos inexorably converts from one form of matter to another. The big bad cosmos does not care that you even ever had a name, much less a title. If anyone is inflating the most, they are not the Data Scientists; rather, they are all of us: we are all culturally tuned to make more of ourselves than physical reality suggests we are worth. We even concoct myths to adorn as garlands around ourselves. Some of us give in to one such form of self-delusion or some other. Notwithstanding, no one will remain here to remember any of us. Go ahead, try skipping to Mars. Maybe 'Fiz6' is whispering in your ear that that is your ticket to escape.
It's a good job, notwithstanding how much it is a pet peeve to some. I'd be so lucky to be a DS (at least in the prevailing job market). If you cannot withstand the stigma that comes with DS, there is also DA (data analyst). Unless the term 'analyst' also drives the sensibilities of others bonkers.
@@treyGivens1We have AI Chatbot options to choose from now (not to imply you were unaware). We can prompt them to respond with a straightforward response or to give us a more thorough or thoughtful response. Some require an account (free) others do not. One such example is Google's Gemini (another is Microsoft's Copilot), which gave the following response to a prompt that specifies roles: Data Science Roles: • Machine Learning Engineer: Develops/implements ML models. • Data Scientist Consultant: Provides DS expertise to clients on a consulting basis. • Data Scientist Researcher: Conducts research in DS & develops new techniques. Data Analysis Roles: • Business Intelligence Analyst: Provides insights into business performance using data. • Financial Analyst: Analyzes financial data to support decision-making. • Market Research Analyst: Gathers/analyzes data for market trends & consumer behavior. Business Analysis Roles: • Systems Analyst: Analyzes business processes & systems to identify areas for improvement. • Process Analyst: Documents/analyzes business processes for optimization. • Business Consultant: Advise/guide businesses on a variety of issues. Data Engineering Roles: • Data Architect: Designs/implements data architectures & solutions. • Data Warehouse Developer: Develops/maintains data warehouses & data marts. • Big Data Engineer: Works with large datasets & Big Data technologies.
@@treyGivens1 I realize that wasn’t the answer you were looking for. That was an (approximate) overview to provide awareness of the bigger environment in which the participants operate. Here’s a more direct answer to your question (by Gemini): Data Scientist • Focus: developing/applying ML algorithms to extract insights from large datasets. • Skills: programming (Python, R, SQL), statistics, ML, DL, data viz, & problem-solving. • Tasks: ◦ Building predictive models ◦ Developing machine learning algorithms ◦ Conducting data analysis and research ◦ Identifying patterns and trends in data ◦ Creating visualizations to communicate findings Data Analyst • Focus: analyzing data for insights (to support decision-making). • Skills: analytical skills, data analysis tools (Excel, SQL, Tableau), & statistics fundamentals. • Tasks: ◦ Cleaning/preparing data ◦ Creating reports & dashboards ◦ Analyzing data to identify trends/patterns ◦ Providing insights to stakeholders ◦ Supporting decision-making processes
@@treyGivens1 Here are some generalized educational foundation for either role (by Gemini): DS vs. DA : Academic/Knowledge Foundation Data Scientists - often have advanced degrees in fields like computer science, statistics, or data science. More technical foundation & focused on ML and advanced statistical methods. • Mathematics: Linear algebra, calculus, statistics, probability theory • Computer Science: Programming languages (Python, R, SQL), algorithms, data structures • Statistics: Hypothesis testing, regression analysis, time series analysis • Machine Learning: Supervised and unsupervised learning, deep learning, neural networks Data Analysts - may have degrees in business, economics, or a related field. More business-oriented or more directly practical application & focused on data analysis tools/techniques. • Statistics: Descriptive statistics, basic inferential statistics • Data Analysis Tools: Excel, SQL, Tableau, Power BI • Business Concepts: Understanding of business processes and metrics
That's because most of them are chasing a universal field theory which needs 11 dimensions that literally cannot be falsified and they call that science 😅
Amen & Hallelujah!!! also - as you have noted - because "software engineers" & "data scientists" spend 90%+ of their time coding, corporate IT departments have been keen on absorbing these roles into their purview, thus increasing the size of their corporate fiefdoms & commodifying those skillsets 🤬!
@@somedudes6455 Engineering is a discipline focused on harnessing the potential energy available in the natural environment to perform useful work. And here, we are talking about the physical definition of work of a force moving a mass over some distance (w = Force x Distance). Software “Engineerimg” (a misnomer) is really a discipline focused on structuring software (the instructions provided to a digital computer) in ways that make those programs easier to enhance and maintain over time as the requirements from which they arose change and evolve.
@@somedudes6455 In each case, chemical engineering, mechanical engineering, electrical engineering, nuclear engineering ... these disciplines involve exploiting some natural phenomena for the benefit of mankind. I would prefer that software "engineering" be called software "design." This does not detract from the legitimacy of that discipline. Designing and building reliable, extensible, and maintainable software systems is no easy feat. It just doesn't involve interaction with the natural world in the way that engineering does. Software systems written for digital computers can be employed to solve engineering problems. But, the vast majority of them are not. The use of a digital computer and its associated software to solve an engineering problem doesn't make the design of the associated software an engineering problem. It's still a software design problem.
I always thought of data science as a blend between data analytics and computer science, where you're not only expected to handle data analytically, but also work within a software system. That takes quite a bit of knowledge of how software works and communicates. It's not simply a subset of skills a physisist may possess, but rather a whole other role with some overlap. For example, you should expect a data scientist not only to build a ML model, but also an API around it so it can be integrated within a larger build. Can't say the same for a biologyst. That being said, ETL pipelines, cloud computing, data version control and I could keep going. While I agree that the word "science" is not quite appropiate and it's largely been jeopardized (unless we step into deep learning territory, where a lot of research is being done day by day that you didn't consider mentioning), we could argue the same for computer science then. Speaking my mind as a fellow european. English is not my first language, but I'm trying my best here btw.
😂 you are pissed about something and taking it on data science. I think you know it’s not that simple. I agree to an extent but I also know that it depends on your focus. Degrees help to specialize. For example, I don’t want to have to take any other science degree just to do data specific work. Sure scientists use data and can learn to program in R and Python but I won’t say it’s their core focus, would you? Most science uses data in the side as a tool, like you said, but what about data as a science and discipline itself. The bridge between computer and data science is evolving faster than other classical sciences. I feel like that’s driving some hate as well. Being a physicist(etc) is not good enough in the world of AI, ML and complex software engineering. Coming as a software and data engineer I personally feel glad to have the opportunity to study data as a science both for career growth but also for my future aspirations in research. Yes as a tool but more focused. Is like saying math is not a science but just a tool other sciences use on the side. Hope to work with others in the sciences but I hope they don’t have the same attitude as you towards data science so I can enjoy research a little more. Thanks for sharing your opinion though!
You and the author are talking past each other because the language here is imprecise. Try this: Some Computer Science is data science, but not all data scientists are Scientists
I'm studying Health Data Science but honestly at the end of the day what do I use it for? Depends on the task. I used Data Science to analyze storage space. I used Python to automate my tasks. That's more like software development. I'm studying epidemiology as domain knowledge. That's Public Health.
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! th-cam.com/video/ZYO0sKtZzUU/w-d-xo.htmlsi=U6LOq0IU3NfjI--9
@@UndineAlmani I think TH-cam 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
Hello ma'am. I ran across this video while doing research, and I'm wondering if I could hire you to go through two chapters of a novel my client is writing to iron out any inconsistencies involving physics. It's a science fiction novel that is heavy on science, but my client is not an expert in physics and is feeling unsure. I edited his work for grammer, etc., but frankly, I'm at a loss when it comes to math and other sciences. It's around 15 pages long. If you're not too busy and interested, could you send me a quote?
Free software are sidekicks (assistants) for both grammar & spelling, just in case the time waiting for a physicist raises curiosity. Typos happen routinely, even spellcheckers miss some words at times.
It’s also funny how scientists base their studies on data from other studies without even checking if it makes sense. This happens because of the arrogance tied to the term “science.” Science is supposedly the highest authority or whatever 🤣.
It's not funny. Actually it can (and should) be a total career breaker. And it is sometimes. But then it just shows that a few scientists aren't immune to vanity and the temptation of money.
@@michaelburggraf2822 no money, no science. Simple. The arrogance stems from the illusion that ethics and real logic are involved. It’s the perfect breeding ground for narcissists.
@@ddddsdsdsd well, no science, no progress. And narcicists can be found in every walk of life. I didn't see more narcists in science than outside. And actually chances of falling flat on your face for narcicistic behaviour are possibly higher in science because results need to be reproducible. Sooner or later there will be someone relying on your results or repeating your research.
If you think Data Science is easy you should take a look at AI development. Basically just throwing data and money at the problem. There is of course some really tricky work at optimization but most AI engineers just download lots of content and throw it on the model until money runs out lol
I was very much around when the term took off around the early 2010s. I have rarely seen data scientists walk around with the hubris of an actual scientist. So it's nothing to get worked up over.
Data Science I will say are one of the few actual sciences. It is as rigorous as they come, go check geology and see how they do things over there. Now, are many using this term loosely? Yes and it irritates me sometime as well being the data scientist with an actual phd
Yup. As you stated toward the end, perhaps the true utility of "data science" as a career is for HR to notice us, however misguided this may be. Broken and imperfect? Yes... However the alternative requires that the corpo heads have at least three active brain cells... 😅
some of it is, but it kind of depends on how loose or annoyingly strict you are about definitions, in the same way that mathematics is not really science.
No. I have this argument all the time. The only science in computer science is physics and material science. Computer science is machine operating. On occasion, it may rise to the level of engineering, but mostly not. The math people try to call computer science is actually information theory and that predates computers.
I am learning data engineering and I like your viewpoints about data scientists, not saying that I agree with you or not but I like honest mind whether it aligns with my views or not.
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.
Ok, I’m all for shitting on title inflation and have no particular affinity to the label “data scientist”, nor that many people that call themselves that should call themselves that, but I’ll try to defend it. First, I agree that there is no such thing as “Data” that we are using “Science” on, data is data and it may come from many domains. Having said that, the need for a “data scientist” came in order to differentiate the skills from analysts, statisticians and developers. In business an analyst meant that you knew sql and did most of your work in excel, a statistician was more rigorous, but even when they could code in some language that did not cost 100k per license per year, the code tended to be sloppy, and developers tended to lack the business context and statistical rigor, so here comes the “data scientist”. Data scientist tended to specialize in two main domains - running many experiments at scale, building machine learning models with sometimes not that much overlap, one is more stats heavy, the other more CS heavy. The challenge in business when hiring people from an academic/stem background is that they tend to over index on the technical part losing sight of why. So as you mention, it sometimes might make sense to teach a less STEM-y person how to run a regression because it is not that difficult, the more “difficult” part, and I’d say the “Sciency” part is in applying your domain knowledge - what are you going to include in your regression, what is your “theory” of what drives dependent variable? And I doo find it cringe when people use the title data scientist to one up on others, but in many cases we have to use it instead of want to in order to differentiate. That is not to say that everyone that is called a “data scientist” should be called one, if you are a physicist working with physical processes, I’d say it should be fine to call yourself a physisist, not everyone should be a data scientist, and if I were in a field where we worked with physical processes I would want to hire physicists over generic “data scientist”
Yeah all scientist, statistician are data scientist. Frankly business degree, accounting, they deal with data and analysis too. technically data scientist. You can still do data science with pen and paper, very very slow, but you can. I suppose the intensity of data analysis, differentiate, if one is a data scientist and non-data scientist.
THANK YOU 😅 Using a calculator can't make one a scientist. Creating the math theory for the math processor in the $1 calculator, that's science. The computer world has great marketing tricks by creating trendy new names that go viral and most ppl have no clue what that is exactly and that boosts a huge employment rush in many ignorant companies with their ignorant hr dpt. Pls Keep up spreading smartness in this continuously dumbing world.
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.
@:48 no do get into to it because real disciplines that take years of work and degrees are being watered down to acute certifications with little to no comprehensive working knowledge. And that's coming from a layperson experiencing the lack of professionalism and overall cohesion and comprehensive care when receiving services.
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?
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 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.
And software “engineering” isn’t engineering. We’ve had 15 years of inflated compensation for programmers and stats folks and opted to invent titles for themselves to justify ZIRP compensation
I work in finance and would just call myself a “data janitor” because all I do is try to think of creative ways to clean messy accounting data. 🤣
The art of arranging positions of a balance in a way that the shady ones don't catch the eyes of the beholder at a glance.
You sweep it into a bin and put it out for collection?
I don't even call myself a Software Engineer if I can help it. I'm a computer programmer.
Same. I'm a web developer. I know only little about algorithms and data structures coz I almost never need it in my work.
Great!!!
It's like calling a drug dealer a pharmacist.
hey ...
Or "Heisenberg"
both can do the job of dispensing drugs, just pharmacists are better at explaining why shit went south after taking some drug
In this society where doctors are essentially drug dealers. Idk what you mean by context.
What a great example! Because even pharmacy (the scientific field) has it's shady corners too - see eg. the opoid crisis in the USA.
Richard Feynman's father taught him: you can know the name of the bird in all the languages in the world and it tells you absolutely nothing about the bird.
The map is not the territory.
This works both ways: don't read too much into the names of things; but also, don't get too upset when the names of things are wrong.
Yeah! She was certainly quite animated over something as benign as mislabeling a discipline. But, I suppose if I had earned a degree in physics (a hard-core science discipline) and found myself surrounded by computer nerds doing analytics with data and calling themselves scientists ... I might be a little triggered. Not enough to create a YT video and rant about it, though. LOL. If they called themselves "analysts" doing data analytics then that might be a more accurate use of the English language. But, this example of data "scientists" not really being scientists at all is hardly the most egregious example of inaccurate language in our society.
@@markteague8889 Especially because analysts are people predicting horror scenarios all the time! 😉
@@markteague8889 Data Scientists & Data Analysts are actually two distinct groups (professions/qualifications), and they are very understandably/reasonably distinct enough. There is overlap in knowledge & tasks, but the DA does not have the set of academic fundamentals of the DS. If expertise in Statistics or some other Math sub-specialty is not a 'science,' they probably wouldn't mind. They could be Data Mathematicians, but that those two terms do not congeal as well together as the objected form. Anyway, if a rant could be amusing, we would endure sitting through the entire thing & feel entertained by it; it seems many of us have just discovered that such exists.
What I learned:
1. This lady does not like the label "data science"
2. She also absolutely hates HR 😂
Spot on, I enjoyed that. I did a three day course in FORTRAN 4 in 1964 to help with research in physics. I now realise that I was a data scientist on the side for decades.
I sent this to a friend of mine who happens to be a professor in data science. He sent me back a middle finger haha.
😂
I’m a machine learning engineer.
No science, I don’t really build models, I collect data, get a model from R&D, and build training and inference pipelines, then jam the data through.
I'm not a Data Scientist, I'm a Data _Artiste_ .
I paint pictures with data.
Y'know, like Midjourney.
No wait
I'm not a data scientist, just a business structurologist
R is the language pirate's code in.
😂😂😂
And software “engineering” isn’t engineering.
We’ve had 15 years of inflated compensation for programmers and stats folks and opted to invent titles for themselves to justify ZIRP compensation
Surely, it is statisticians (i.e. as practitioners of a specialist branch of mathematics) who traditionally did data analysis.
Finally someone said it.
I think it is generally the case that any title that involves the word "science" is not actually science!
The term data science is just a euphemism for companies collecting huge masses of data about consumers in order to get clues for new product developments and - even more important - for improving marketing their products.
The term data science has been created in financial industry and consulting businesses, and academic institutions have adopted that term welcomingly in order to sell new academic courses and titles.
She is absolutely right. Statistical analysis of large amounts of data is just a household chore of every proper experiment yielding data.
Experiments at CERN, the European nuclear research facility, are producing terabytes of data per second which need to be processed, analyzed and condensed quickly because the next set of data will probably produced within a second.
Field experiments in medicine and biology can (should) be based on sufficiently large data collections in order to generate meaningful results which have to be processed for decades - actually an essential purpose of computers in science and one of their earliest uses.
Data analysis/engineering
You are definitely *_not_* eurocentric @5:05 Once computers became commonplace I viewed them as a tool, pretty clean and easy to use. Never took a single coding course in my life, but in getting on with physics, programming eventually took up a sizeable percentage of keyboard tapping time.
And yet you probably don't refer to your self as a software engineer either. I worked with nuclear engineers, I wrote software for them, but I was not a nuclear engineer nor would I ever claim to be. I also worked with power engineers. I wrote software for them, but I would never claim to be one. But I was a software engineer. they needed to be able to communicate effectively with me and I had to be able to design and write what they needed.
"I am doing that on my left ass cheek" 🤣🤣🤣 I fell out of my seat😂😂
Economist working with data scientists perspective. I think data science sorta makes sense as a relabeling of applied statistics, which is somehow more attractive to business people in HR or management. Or it often ends up being used for the computer science of data, since it's so heavily dominated now by computer science people doing machine learning and work with databases. Maybe the data science label captures better the intersection of statistics with computer science, though machine learning is also a branch of statistics, and is probably less pretentiously called statistical learning (one of the best books on ML for free is Introduction to Statistical Learning). I think in both hard and social/behavioural sciences, statistics is considered just 1 important tool. The data science perspective in industry right now is limiting because if leads to a mindset in which if only we apply some statistical methods and simple data analysis to supplement business intuition/gut and 'common sense' thinking we'd all get much better business decisions (data science is really more of an industry/private sector term, most academic or government statisticians wouldn't advertise themselves as data scientists). Which is a priori wrong and empirically probably wrong because we had really big investments in big data, machine learning etc...yet in general the last 15 years of data science in advanced economies have seen quite slow economic growth/quasi-stagnation, contributing to populism and other social problems. So the returns to big data and machine learning in business must in general be quite low and maybe negative net of the costs. Both data science and more generally software engineering/development labour markets now are I think in a major recession, and I wouldn't be surprise if this is something more long-term as companies realise they've overinvested in both big data and computer tech (I also find it strange that the word tech is now practically used to refer only to computer science/electronics related stuff. To me, mechanical engineering is in many ways more representative of 'tech').
Calling them "data wranglers" would have been much more accurate and cool! Just imagine the marketing possibilities for such a title!
Maybe “Data Cowboy”?
This would be far better if the comments on screen were shown for long enough to be read. This video will challenge even a speed reader. 😂😂😂
I largely agree with you. If you're not applying the scientific method, you're not a scientist. If your applying a known principle of science, you're probably an engineer.
Howevere... You are remarkably abrasive. Turn it down. You're not going to change hearts and minds with that attitude. You will not win friends and influence people.
Though blunt (abrasive), also very amusing.
What about writing algorithms,predicting trends,creating models and using different mathematical methods.Does that incorporate science?it has theory and application,uses empirical data,it's interdisciplinary.
I’ve been running up against this, myself and I largely agree with your points. I’ve got an MS in computational chemistry. I specialized in quantum dynamics, published, and it was a *grueling* program.
I ended up learning shell scripting, git, python , MATLAB, C++, HPC, DSP in those languages, AND all the projective geometry, classical dynamics, statistical mechanics… and we had to visualize the phase space surfaces from the simulation data!
The section where you mention that HR people don’t hire because they don’t think my degree includes these skills… it hit me HARD because I now work as a freelancer electronics hardware designer because nobody seems to want to hire someone with *only* an MS (never mind my three pubs…) in quantum *chemistry*. Turns out I’m good at analog electronics. Go figure.
It’s all just E&M and wave mechanics anyway…
I should get a data “science” cert… most of my cohort from that program work as data scientists anyway… ugh.
What are your thoughts on Statistics degrees?
And people that work as "data scientists" after statistics degree.
Would you consider them as data "scientists"?
Maybe your view is a bit physics focused. I work at a proteomics lab and we have approx 70% bioloists/chemists and 30% data scientists. Our biologists have minimal knowledge about programming and data science, mostly not exceeding Excel. They handle everthing on the wet lab side up the to mass spec. From there on we data scientists get involved. Getting from m/z spectra to analyte identification is everything but trivial. It is an active field of research. Nothing we do is vanilla machine learning. Expecting from the biologists to learn all about the quirks with machine learning in cross-link mass spectrometry would be unfeasible. In the same sense we data scientists do not know enough about biology to design and execute the wet lab experiments. I agree that the term "data scientist" is used inflationary but in the same way many "software engineers" are no engineers.
thing is, to correctly design an experiment you need to decide how you’ll be calculating the results beforehand. so to do any experimentation you are required to know the basics
otherwise it’s not a scientific experimentation but just poking around some random data
Nailed it....
SWE is easier to say & sounds better than SWD🤔.
Computer science is science. You're just trying to make yourself sound more impressive. Physics and Chemistry are not the only science. You're clearly mixing up boot camp, which is actually data analytics, with actual data science, which you can get a BS and MS degree in at my local University of Oregon. I don't call myself a data scientist, but I am in the 300 level AI/Data Science math. I authored the Theory of Gradient Relativity, so I do some physics you would love to read. I do data science from time to time for my company. Real data science has a lot of High Performance Computing. I do HPC in C++ using with data driven programming. It's not physics, but there is a lot of math. Asynchronous internet things are very hard. Almost all of the math is in the BLAS library or Math Kernel Library.
2:08 : specialisation is one of the ways we get productivity growth. Just because specialisation can (and has, in your experience) led to inefficiencies, doesn't mean the attempt is in the service of "wage dumping". If it could lead to a physicist getting more physics done they could end up getting paid more, to the extent that they are more productive as a result.
It's possible it can work. It might depend on the specific organisation. In my domain (software quality), I see computer programmers reaching the wrong conclusions because they don't understand data well enough. Employing statisticians would help. I can imagine this happening with physics, but I also get your point that physicists are closer to an understanding of their own data.
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).
This brings back fond memories of the look on students’ faces in an Information Security course when they met Galois fields.
This is true in my university as well. It’s hilarious how little math most the data science students want to learn. I was quite annoyed by how little focus on math there was in my data science degree’s coursework and ended up just deciding to get another bachelors in math cause the degree was basically useless imo. Now I’m in love with math and want to get as far as I can from the applied stuff.
Why focus so much on job titles? They’re just like names - labels that don’t always reflect true value. Most scientists publish work that can’t even be reproduced. The real issue seems to be arrogance-people talking just to sound important, when in reality, they aren’t.
Job titles determine whether you get work and how much you get paid.
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.
With all the anti-intellectualism going around it’s always refreshing to see a PhD in a field bashing other fields of science they don’t understand ❤ You do realise that knowing how to do regression on a dataset has as much to do with data science as an electrician has to do with physics? Also, I’ve already sent the part about misogynistic universities to a few women friends of mine that do interdisciplinary studies and we’re all having a giggle about the fake elitism of the division of labour inspired by the industrial revolution ☺️
Your basic argument is that it's not a science because you can do all of it with your fancy physics degree. That's not an argument. Get over yourself.
It's true she didn't deal with the word "science" in "data science". I think perhaps it was too obvious? But the reason "data science" is not a science (and she said this) is that in physics (and any science) you can't separate the data from the *experiment* i.e. the way you get the data. I agree. "data science" should be called "mathematical modelling" or "computational modelling" or "statistical modelling". I also think that it isn't clear with the new title "data scientist" what skills are being asked for --- programming certainly, python, R --- but after that, what? ANN? SQL? Web infrastructure? Privacy issues in data/health? Financial data? I think she went too far with "medical physics", this is a real science, could also be called "medical engineering". I perhaps also agree that employers and HR don't really know what skills the hard sciences offer, and they want those skills, perhaps on the cheap. Personally I don't care what they call it, as long as it is a job, if it's easy, so much the better.
You're a joke. Apparently you don't realize it.
The discipline not truly being science is just the reality. It isn’t science. This appropriation of language is pervasive throughout Western Society; and especially, academia.
Hello, as former data scientist.
Data science is not science.
You’re not forming hypothesis and testing it. You’re not establishing causal relations.
Yep. This was a Marxist I take and nothing more. The critique itself is fine. The conclusions are flawed. Given the skyle of academic fraud now being uncovered across the world, none of us should be surprised.
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.
its like asking you scientist in what? Nah I just a scientist
So data science it not about the science about data, it is a tool. Fair enough but now I'm curious, how is the science about data called? I'm seriously interested.
Never apologize for being Euro-centric.
Good on you for standing up against nonsense!
14:00 "it works that way, not the other way" - this makes sense to me because you can't really reason about statistical models without understanding what you're modelling. But in other sciences you do see some bad statistics that appear to be the result of scientists not understanding statistics well enough. I think choices of statistical models in e.g. climate science are... questionable.
Thanks! Finally someone mentioning it.
"Data science" is used in many fields. There is not the typical Data Scientists job. E.g. Data "Science" is applied by engineers (mechanical, electrical, mechatronics etc.) to analyze systems or devices. They often use much more sophisticated algorithms, and more advanced maths than the usual guys calling themselves "Data Scientists".
Thanks for calling this Bvllsh*t out! 😅.
Call them computing statistician?
Imagine all these people complaining about having to juggle too many subjects at once, burning out, and griping about competition and dividing workloads.
Great video, I wouldn't call that "data science" certification a degree, it's not a degree it's just a certification, your bachelor's is a degree, don't lessen it by calling a short non university credit awarded certification a degree. Cheers.
What’s the value of a degree and years of study if someone can do the same job without one of these “super important” titles? 🤣
It’s like talent and intelligence are being overlooked in favor of degrees. That’s not exactly smart!
Data technicians perhaps?
I am a Professor of Data Science 🧪 and this actually made me consider changing my title! 😂 For starters one might be a little suspicious of any discipline that has to put ‘Science’ after the title…like ‘Social Science’, ‘Computer Science’ etc. No one talks about’Physics Science’ or ‘Chemistry Science’… but I guess ‘Data Science’ is the practice of obtaining information from data….📊 surfacing signal from noise….I also noticed few people do courses in ‘medical statistics’ these days…it’s ‘Health Data Science’ now…anyway interesting and thought provoking video… vielen Danke!
You should quit being a professor and go find yourself again. Don’t educate others if you don’t understand why you are teaching them. Second, I would avoid listening to a random lady ranting about things she just judges on her preconceptions and opinions. Aren’t scientists supposed to be more grounded and humble? Nope. Historically, they are not. That is a huge flaw in science as a discipline but I’m not telling you to stop calling yourself a scientist right? Third, science is a subset of philosophy so technically we could claim science does not exist without philosophy but philosophers aren’t petty like scientists trying to gate keep their tenured jobs that pay less than software engineers and computer scientists! Let’s keep it real and be humble.
I agree that language is doing is no favors here. Try this:
"Some Computer Science is data science, but not all data scientists are Scientists"
@@arto00-g2nActually I’m a research professor so do very little teaching these days. So I’m not ready to quit just yet. But you are correct- I probably should be spending less time on TH-cam….😂
It's really not. People apply pre-made methodologies from scientific fields. If you ask me, I wouldn't call data analysis very scientific either. A lot of the work done in both those areas builds on processes of building, cleaning, maintaining, and connecting tables, primarily a technical skill that relies on relational database principles.
The idea involves linking data based on common attributes and doesn't require analytical proof because the relationship is inherent in a data model. This is the foundational step in data integration but distinct from analyzing the meaning or correlation between the variables within those tables.
Obviously, not everyone is the same, but more often than not, people link tables just because it can be done and then run models without proving the relationship. Connecting attributes is one thing since attributes are static and descriptive fields used to identify and categorize. However, dynamic fields encapsulating quantitative or qualitative dynamics happen at higher dimensional spaces. Therefore, their relationship needs to be proven.
This rarely happens... apparently... don't ask me why. It's irrational. Nevertheless, without an analytical process you can't call sth scientific, probably not even valid.
It's like the three-body problem. Just because some elements coexist in a system, it doesn't tell you anything about how or whether they actually interact meaningfully-you need to analyze their relationship.
Let's not lose the forest for the trees.
Nothing she says contradicts your statement. But what you're saying is that there is some engineering activity involved in data cleaning/scraping/preprocessing.
Let's just agree that the engineers and scientists should work together in the domains that best suit them. Thus you find roles like "data engineer" in industry, which is what your comment is addressing.
@Alxdb no, what I'm saying is that there is no analytical method involved. It's not about an engineering role.
Show me the MONEY. I'll produce & deliver the work expected. Call my role Data Fakist if it makes you (not you personally) feel warm & fuzzy. Just show me the MONEY. There is nothing else to discuss. Any yibber-yabber besides this is the real BS. If I can put lots of money aside while at it to afford to move to a lower cost-of-living and gain more time to myself as a result, I will/can practice real science whether anyone has ever given me a pat on the head with a diploma or not. And if I actually do that, I will not tell a single one of your self-inflating souls what I will have discovered. Go F your degrees.
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.
My problem is with them calling themselves scientists. They made one method used in science an entire fake ass degree.
Nothing that follows is about you personally; it's just reasoning out-loud. Most regular people are not psychologically attracted to titles, not even one spelled like S-C-I-E-N-T-I-S-T. We are however ingrained to be attracted to food, especially ones intensely colorful. The ingrain primitive urges of a human being is not concerned the slightest with what you call yourself, even if your first-name was Greatest, your middle-name was Emperor, & your last-name was Of-All-Times. You could manage to compel others to go along with your naming convention; you could even manage to destroy some defenseless country to make yourself remembered in history. In the silent recesses of a human being, we see you as a hopelessly ageing, debilitating, perishing animate object that the cosmos inexorably converts from one form of matter to another. The big bad cosmos does not care that you even ever had a name, much less a title. If anyone is inflating the most, they are not the Data Scientists; rather, they are all of us: we are all culturally tuned to make more of ourselves than physical reality suggests we are worth. We even concoct myths to adorn as garlands around ourselves. Some of us give in to one such form of self-delusion or some other. Notwithstanding, no one will remain here to remember any of us. Go ahead, try skipping to Mars. Maybe 'Fiz6' is whispering in your ear that that is your ticket to escape.
This is convincing me I need to become a data scientist lol.
It's a good job, notwithstanding how much it is a pet peeve to some. I'd be so lucky to be a DS (at least in the prevailing job market). If you cannot withstand the stigma that comes with DS, there is also DA (data analyst). Unless the term 'analyst' also drives the sensibilities of others bonkers.
What is the difference between data scientist and data analyst?
@@treyGivens1We have AI Chatbot options to choose from now (not to imply you were unaware). We can prompt them to respond with a straightforward response or to give us a more thorough or thoughtful response. Some require an account (free) others do not. One such example is Google's Gemini (another is Microsoft's Copilot), which gave the following response to a prompt that specifies roles:
Data Science Roles:
• Machine Learning Engineer: Develops/implements ML models.
• Data Scientist Consultant: Provides DS expertise to clients on a consulting basis.
• Data Scientist Researcher: Conducts research in DS & develops new techniques.
Data Analysis Roles:
• Business Intelligence Analyst: Provides insights into business performance using data.
• Financial Analyst: Analyzes financial data to support decision-making.
• Market Research Analyst: Gathers/analyzes data for market trends & consumer behavior.
Business Analysis Roles:
• Systems Analyst: Analyzes business processes & systems to identify areas for improvement.
• Process Analyst: Documents/analyzes business processes for optimization.
• Business Consultant: Advise/guide businesses on a variety of issues.
Data Engineering Roles:
• Data Architect: Designs/implements data architectures & solutions.
• Data Warehouse Developer: Develops/maintains data warehouses & data marts.
• Big Data Engineer: Works with large datasets & Big Data technologies.
@@treyGivens1 I realize that wasn’t the answer you were looking for. That was an (approximate) overview to provide awareness of the bigger environment in which the participants operate. Here’s a more direct answer to your question (by Gemini):
Data Scientist
• Focus: developing/applying ML algorithms to extract insights from large datasets.
• Skills: programming (Python, R, SQL), statistics, ML, DL, data viz, & problem-solving.
• Tasks:
◦ Building predictive models
◦ Developing machine learning algorithms
◦ Conducting data analysis and research
◦ Identifying patterns and trends in data
◦ Creating visualizations to communicate findings
Data Analyst
• Focus: analyzing data for insights (to support decision-making).
• Skills: analytical skills, data analysis tools (Excel, SQL, Tableau), & statistics fundamentals.
• Tasks:
◦ Cleaning/preparing data
◦ Creating reports & dashboards
◦ Analyzing data to identify trends/patterns
◦ Providing insights to stakeholders
◦ Supporting decision-making processes
@@treyGivens1 Here are some generalized educational foundation for either role (by Gemini):
DS vs. DA : Academic/Knowledge Foundation
Data Scientists - often have advanced degrees in fields like computer science, statistics, or data science. More technical foundation & focused on ML and advanced statistical methods.
• Mathematics: Linear algebra, calculus, statistics, probability theory
• Computer Science: Programming languages (Python, R, SQL), algorithms, data structures
• Statistics: Hypothesis testing, regression analysis, time series analysis
• Machine Learning: Supervised and unsupervised learning, deep learning, neural networks
Data Analysts - may have degrees in business, economics, or a related field. More business-oriented or more directly practical application & focused on data analysis tools/techniques.
• Statistics: Descriptive statistics, basic inferential statistics
• Data Analysis Tools: Excel, SQL, Tableau, Power BI
• Business Concepts: Understanding of business processes and metrics
Data scientist is a job description for the most part. That's it.
Hey if you don’t like data science, you can always go back to work at academia… oh that’s right, Physics is another over saturated field. 😂
That's because most of them are chasing a universal field theory which needs 11 dimensions that literally cannot be falsified and they call that science 😅
Amen & Hallelujah!!! also - as you have noted - because "software engineers" & "data scientists" spend 90%+ of their time coding, corporate IT departments have been keen on absorbing these roles into their purview, thus increasing the size of their corporate fiefdoms & commodifying those skillsets 🤬!
News flash! Software engineering is not engineering. 😉
I think it can rise to the level of engineering in certain cases. Computer science certainly isn't science.
Why isn't it?
@@somedudes6455 Engineering is a discipline focused on harnessing the potential energy available in the natural environment to perform useful work. And here, we are talking about the physical definition of work of a force moving a mass over some distance (w = Force x Distance). Software “Engineerimg” (a misnomer) is really a discipline focused on structuring software (the instructions provided to a digital computer) in ways that make those programs easier to enhance and maintain over time as the requirements from which they arose change and evolve.
@@markteague8889 no, what you're describing is a subset of engineering, i.e. mechanical engineering. There are other types of engineering.
@@somedudes6455 In each case, chemical engineering, mechanical engineering, electrical engineering, nuclear engineering ... these disciplines involve exploiting some natural phenomena for the benefit of mankind. I would prefer that software "engineering" be called software "design." This does not detract from the legitimacy of that discipline. Designing and building reliable, extensible, and maintainable software systems is no easy feat. It just doesn't involve interaction with the natural world in the way that engineering does. Software systems written for digital computers can be employed to solve engineering problems. But, the vast majority of them are not. The use of a digital computer and its associated software to solve an engineering problem doesn't make the design of the associated software an engineering problem. It's still a software design problem.
I always thought of data science as a blend between data analytics and computer science, where you're not only expected to handle data analytically, but also work within a software system. That takes quite a bit of knowledge of how software works and communicates. It's not simply a subset of skills a physisist may possess, but rather a whole other role with some overlap. For example, you should expect a data scientist not only to build a ML model, but also an API around it so it can be integrated within a larger build. Can't say the same for a biologyst. That being said, ETL pipelines, cloud computing, data version control and I could keep going.
While I agree that the word "science" is not quite appropiate and it's largely been jeopardized (unless we step into deep learning territory, where a lot of research is being done day by day that you didn't consider mentioning), we could argue the same for computer science then.
Speaking my mind as a fellow european. English is not my first language, but I'm trying my best here btw.
I don't see no white lab coats in a computer lab
😂 you are pissed about something and taking it on data science. I think you know it’s not that simple. I agree to an extent but I also know that it depends on your focus. Degrees help to specialize. For example, I don’t want to have to take any other science degree just to do data specific work. Sure scientists use data and can learn to program in R and Python but I won’t say it’s their core focus, would you? Most science uses data in the side as a tool, like you said, but what about data as a science and discipline itself. The bridge between computer and data science is evolving faster than other classical sciences. I feel like that’s driving some hate as well.
Being a physicist(etc) is not good enough in the world of AI, ML and complex software engineering. Coming as a software and data engineer I personally feel glad to have the opportunity to study data as a science both for career growth but also for my future aspirations in research. Yes as a tool but more focused. Is like saying math is not a science but just a tool other sciences use on the side.
Hope to work with others in the sciences but I hope they don’t have the same attitude as you towards data science so I can enjoy research a little more.
Thanks for sharing your opinion though!
You and the author are talking past each other because the language here is imprecise. Try this:
Some Computer Science is data science, but not all data scientists are Scientists
I'm studying Health Data Science but honestly at the end of the day what do I use it for? Depends on the task. I used Data Science to analyze storage space. I used Python to automate my tasks. That's more like software development. I'm studying epidemiology as domain knowledge. That's Public Health.
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
it might be worth cutting at around 1300Hz as well (the octave)
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! th-cam.com/video/ZYO0sKtZzUU/w-d-xo.htmlsi=U6LOq0IU3NfjI--9
@@UndineAlmani I think TH-cam 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
Hello ma'am. I ran across this video while doing research, and I'm wondering if I could hire you to go through two chapters of a novel my client is writing to iron out any inconsistencies involving physics. It's a science fiction novel that is heavy on science, but my client is not an expert in physics and is feeling unsure. I edited his work for grammer, etc., but frankly, I'm at a loss when it comes to math and other sciences. It's around 15 pages long. If you're not too busy and interested, could you send me a quote?
Try chat jippitty
@Alxdb Can't. I have issues with their intellectual property terms an conditions.
Free software are sidekicks (assistants) for both grammar & spelling, just in case the time waiting for a physicist raises curiosity. Typos happen routinely, even spellcheckers miss some words at times.
It’s also funny how scientists base their studies on data from other studies without even checking if it makes sense. This happens because of the arrogance tied to the term “science.” Science is supposedly the highest authority or whatever 🤣.
It's not funny. Actually it can (and should) be a total career breaker.
And it is sometimes.
But then it just shows that a few scientists aren't immune to vanity and the temptation of money.
@@michaelburggraf2822 no money, no science. Simple. The arrogance stems from the illusion that ethics and real logic are involved. It’s the perfect breeding ground for narcissists.
@@ddddsdsdsd well, no science, no progress. And narcicists can be found in every walk of life. I didn't see more narcists in science than outside. And actually chances of falling flat on your face for narcicistic behaviour are possibly higher in science because results need to be reproducible. Sooner or later there will be someone relying on your results or repeating your research.
If you think Data Science is easy you should take a look at AI development. Basically just throwing data and money at the problem. There is of course some really tricky work at optimization but most AI engineers just download lots of content and throw it on the model until money runs out lol
The leading edge of AI is doing vastly more interesting work than scaling data. Ignorance of research in this area does not make for a good argument.
I was very much around when the term took off around the early 2010s. I have rarely seen data scientists walk around with the hubris of an actual scientist. So it's nothing to get worked up over.
Data science is data mining. Data mining is not science
We have an educational industrial complex. Engineering and most of high tech are trades.
Happy string theory!!
Medical Physics probably comes from administrators misunderstanding or 'streamlining' interdisciplinary cross pollination.
Data Science I will say are one of the few actual sciences. It is as rigorous as they come, go check geology and see how they do things over there. Now, are many using this term loosely? Yes and it irritates me sometime as well being the data scientist with an actual phd
Yup. As you stated toward the end, perhaps the true utility of "data science" as a career is for HR to notice us, however misguided this may be. Broken and imperfect? Yes... However the alternative requires that the corpo heads have at least three active brain cells... 😅
1. Hiroshima
2. Nagasaki
3. This video
I clicked to watch the video because I agreed with the title and secretly hoped you were a physicist. You get it
Customer solutions engineer is a funny title. They are consultants, sales people who takes customers requirements to the technical people
Updooted before even watching. The title stands on it's own merit
Excellent video, Glad you got to release this!
I'm afraid for my life now lol ;D
Me looking at this title: well f 🐬 ck me. I didn’t know that 😅 To me data science is just some battery I can plug into another field.
Is computer science a science?
some of it is, but it kind of depends on how loose or annoyingly strict you are about definitions, in the same way that mathematics is not really science.
@@BS-jw7nfMath is not a science, and 99 percent of mathematicians wouldn't argue that it is
mostly math
No. I have this argument all the time. The only science in computer science is physics and material science. Computer science is machine operating. On occasion, it may rise to the level of engineering, but mostly not. The math people try to call computer science is actually information theory and that predates computers.
I am learning data engineering and I like your viewpoints about data scientists, not saying that I agree with you or not but I like honest mind whether it aligns with my views or not.
Yes, this is known. "Data Science" is just the name of the profession,, no one really thinks that it is a real stem science
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.
Most physicists nowadays aren't scientists too, anyways; and 99% of PhDs are not making real science too. Just calm your ass down.@@UndineAlmani
Ok, I’m all for shitting on title inflation and have no particular affinity to the label “data scientist”, nor that many people that call themselves that should call themselves that, but I’ll try to defend it.
First, I agree that there is no such thing as “Data” that we are using “Science” on, data is data and it may come from many domains. Having said that, the need for a “data scientist” came in order to differentiate the skills from analysts, statisticians and developers. In business an analyst meant that you knew sql and did most of your work in excel, a statistician was more rigorous, but even when they could code in some language that did not cost 100k per license per year, the code tended to be sloppy, and developers tended to lack the business context and statistical rigor, so here comes the “data scientist”.
Data scientist tended to specialize in two main domains - running many experiments at scale, building machine learning models with sometimes not that much overlap, one is more stats heavy, the other more CS heavy.
The challenge in business when hiring people from an academic/stem background is that they tend to over index on the technical part losing sight of why. So as you mention, it sometimes might make sense to teach a less STEM-y person how to run a regression because it is not that difficult, the more “difficult” part, and I’d say the “Sciency” part is in applying your domain knowledge - what are you going to include in your regression, what is your “theory” of what drives dependent variable?
And I doo find it cringe when people use the title data scientist to one up on others, but in many cases we have to use it instead of want to in order to differentiate.
That is not to say that everyone that is called a “data scientist” should be called one, if you are a physicist working with physical processes, I’d say it should be fine to call yourself a physisist, not everyone should be a data scientist, and if I were in a field where we worked with physical processes I would want to hire physicists over generic “data scientist”
Yeah all scientist, statistician are data scientist. Frankly business degree, accounting, they deal with data and analysis too. technically data scientist. You can still do data science with pen and paper, very very slow, but you can. I suppose the intensity of data analysis, differentiate, if one is a data scientist and non-data scientist.
YES. It’s Data ENGINEERING, not science.
I wouldn't even take it that far. we used to call them analysists.
THANK YOU 😅
Using a calculator can't make one a scientist.
Creating the math theory for the math processor in the $1 calculator, that's science.
The computer world has great marketing tricks by creating trendy new names that go viral and most ppl have no clue what that is exactly and that boosts a huge employment rush in many ignorant companies with their ignorant hr dpt.
Pls Keep up spreading smartness in this continuously dumbing world.
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.
Of course I am glorifying science. And I am de-glorifying crap that shouldn't call itself that.
Yeah. Words don't mean what words mean, didn't you know
@:48 no do get into to it because real disciplines that take years of work and degrees are being watered down to acute certifications with little to no comprehensive working knowledge.
And that's coming from a layperson experiencing the lack of professionalism and overall cohesion and comprehensive care when receiving services.
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?
I am 5 seconds into the video AND I'M REALLY MAD! (I just don't know why, yet!) 😅
Very, very good....
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.
At last somebody said it. Thank you😅
thank you for! finally someone who gets it
Of course it isn't science. CS isn't science either. It's engineering (ir at least it should be.)
Agreed.
Neither is Political Science, nor Social Science.
You made my day
make another video on psychology, that shit is fucked up too
My ex was a psychologist, I can say they get really pissed if you don't count them as a hard science 🙊
@@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 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.
True
XD so true.
And software “engineering” isn’t engineering.
We’ve had 15 years of inflated compensation for programmers and stats folks and opted to invent titles for themselves to justify ZIRP compensation