foreign hey YouTube It’s Dimitri and today we’re going to answer a subscriber’s questions specifically on the future of quantitative Finance here so I was going to
read this whole thing but I don’t want to bore you too much essentially what the question is here which I’ll put on the screen um is they’ve been talking about you know what do you think is going to happen to the future of quantitative style="font-weight: bold; color: #1a73e8; text-decoration: none;">Finance
basically like you can get in with an undergrad uh but you know they’re kind of wondering what’s going to happen with Quant Finance and essentially like
they’ve seen you know kind of mixed perspectives on you know do you need a masters do you not need a master’s in Quant Finance in the future where do I think this is kind of going
to go um and they understand you know that you know for data scientists a lot of companies don’t know what they really are and so that kind of muddies the water a little bit um so basically what are the minimum requirements here of a Quant in the future these CashNews.cos you guys ask the
tough questions uh honestly it’s because data science I think is a massive joke um I’m gonna find so many people watching this but that’s okay um the longer I study data science the more data scientists I work with uh the more I realize it’s like everybody just wants a
shortcut and everybody wants the get rich quick scheme and I don’t blame actual true data scientists per se I’m going to put a little pin in that for now because there’s really yeah it’s just been degraded down to nothingness like data analytics is the same as data science
now uh using Excel and GBA apparently is now data science and everybody I talked to as a data scientist like even people that have degrees that aren’t even related like oh I’ve taken a day of the science course as if like they’re gonna like you know stamp their resume or stamp
their graduate degree like their data scientist because they took a course um I mean it’s laughable it’s like me taking you know English 401 or something and then being like I’m an English expert I’m going to write a bunch of books and novels and you know stories and fairy
tales and I’m excellent of course I’m not I’m terrible at these sorts of things um but no data science and machine learning as a whole is one of those weird Fields now or if you look at a lot of the job descriptions so I’ve been in this industry a bit here in Quant href="https://cashnews.co/finance" style="font-weight: bold; color: #1a73e8; text-decoration: none;">Finance um I sit on fintech now it’s where I’m working currently I advise and discuss and chat with people in D5 fintech bold; color: #1a73e8; text-decoration: none;">Finance traditional Finance it’s Quant none;">Finance Banks investment firms like I rub shoulders a lot of different people even in Industries that are analytical driven that have full staffed data science teams that are highly rigorous but the reality is is that now people have kind of gotten away from data science because when it started being a data scientist with somebody who could actually build models and they took it seriously as an actual science unfortunately though what’s ended up happening is that it’s degraded down into like I mentioned people using Excel and you know coding things now in Python and R so simply that they have no idea what’s even going on they can’t even address the issues themselves and so uh all these firms now all these big Tech firms those other companies have started now just changing titles like you’re now a machine learning engineer or you’re I don’t know a machine learning scientist or I work in AI uh again they’re technically different things but the industry is now coping with the struggle of you’ve degraded data science what it was originally down to like lowly data work and there’s nothing wrong with that but to do General analytical data calculations and things like taking averages or just haphazardly fitting something like a model to you know a bunch of dots on the screen it doesn’t really matter you don’t need a rocket scientist or a Quant to do this or someone super rigorously educated and trained with a master’s degree from top university or even a PhD and so you have to kind of weed through this this is the one piece of this this is the you know this is what’s happening to data science as a whole uh Quant Finance has got already gone through this so Quant #1a73e8; text-decoration: none;">Finance is much more mature uh in the life cycle of career development education and things and it is struggling as well here which I perhaps make another CashNews.co on in the future um but it went through that phase where it was like being a Quant was like top paying amazing stellar and then you know people couldn’t pay you enough people couldn’t find you if you had a master’s in Quant Finance uh before like 2007 2008 it was the dream time to be a Quant so what ended up happening here is that financial Markets blew up 2007 2008 uh way too many Master’s programs have been created over the years and again the quality and standard of quants has been degraded down to very very minimalist portions but I think more importantly here we have had many many banks and firms so quantitative none;">Finances much more structured and defined uh so Banks and firms that need people to build models to to do things where you’re either right or wrong and if you’re wrong you lose a lot more money it’s easier to see when you’re right and wrong these sort of firms they’ve sampled and tried doing the undergrad path and there are still some which I won’t name here firms and banks that are doing it and most firms that have attempted and tried this have failed absolutely miserably the ones that are still continuing to do it here in 2023 they’ve actually split these into two different jobs and you have like a junior quantitative person in a senior quantitative person and realistically what’s happening is Junior quantitative people are doing like the data scientists are doing they’re just doing data analytics and simple things uh and then what’s ended up happening is they have the actual quants which now they’re bringing in a lot of them are just phds and they’re having the phds actually do the model fitting and the theoretical part of actually putting all the pieces together making sure it’s correct and it’s going to work um so they can say and I’ve seen many of them standing on their uh you know Ivory Towers saying oh we’re equal opportunity employers and we’re trying to bring in undergrads here but Quant style="font-weight: bold; color: #1a73e8; text-decoration: none;">Finance could create massive training programs and I’ve seen firms do this where they’ve brought in undergrads and then they have all this mandatory Education and Training and it’s like hundreds or thousands of hours of training to get these undergrads to that point but again it’s an investment piece here do firms really want to do this some do some don’t 99.9 of all Quant Firearms don’t want to do that because to hire a bunch of training and educational people which is just expensive and often it’s not worth the effort when you can just go out and find Master’s students now data science as a whole let’s just break this down more specifically it’s kind of like a weird I don’t know it’s like the little brother of Quant none;">Finance in many ways but a lot more General so iview data science as just like the whole picture of your model Developers for a wide area but unfortunately you realistically only use tools in the machine learning space uh that’s kind of what’s happened here because if you start backing out the reality of this when firms hire such as myself in the quantitative Finance space I view data science as just a subset in machine learning and AI as a subset AI is kind of on the border because you can do automation with that but the model development portions of these as a subset of Statistics so and then data scientists get up in arms and machine learning people and they’re yelling and screaming oh you don’t know it’s completely different uh you’re still using logistic regression you’re still using OLS as much as you cry and complain and hate linearity and you know oh what about these non-linear cases here you know you can do all that actually with linear regression again I’m not going to go into that doing data Transformations and variable Transformations going to the models but what ends up happening is that they’re so specialized into only one area it’s like you kind of have a specialty but you only can use a couple tools because if you use all the tools you’re not really a data scientist or machine learning expert you’re just a statistician or a Quant in the Finance space but more or less I figure like data science was going to take a more well-rounded role which they have not the community itself I think is quite um quite toxic to be quite honest with you like people I’ve met and ran into it ends up in this weird weird nuanced space where it’s like they don’t want to use any tools except for those in their space and there’s this like I’m on LinkedIn the last few weeks you’re scrolling and it’s like there’s so many just garbage pieces of people complaining about how horrible statistics are how horrible econometrics are uh there’s even people in Quant href="https://cashnews.co/finance" style="font-weight: bold; color: #1a73e8; text-decoration: none;">Finance like this which I don’t have much respect for in this aspect though they have other great contributions as well the industry um but it’s like why would you do anything with half the tools like I wouldn’t go fix my car and say I’m only going to use this half the toolbox because the other half isn’t good and this applies you into the stats for them as well there are many people that are pro and anti machine learning on the stat side I think though it is starkly different that data science machine learning is very toxic as a culture in a community it’s very anti-stats where on the stats I think a lot of us are just more or less like we’re leery like why are you like you’re doing this new approach and often I put it in air quotes new approach which is typically a traditional approach been relabeled and then it just takes us time to figure out what you’re trying to do uh and then a lot of it’s just nonsensical so the data science approach I am a hundred percent against for most problem solving there are cases where you could use it but the data science approach being I have data I need maximum accuracy Do or Die let’s get maximum accuracy and that’s what ends up happening and they even have these so someone who actually managed teams and runs people and worries about Profitability and things that have nothing to do with the model development process um on the management side of this right I don’t want to have to have models blow up because in Quant text-decoration: none;">Finance again here it’s going to act my Finance background the issue with this is I can’t afford to have a model just blow up and just not have a model like this might be okay in the investing side because again in investing you have so many dollars and if you have a model blow up and you just want to close the position so you’re like ah the model is not really working anymore we didn’t really lose too much but it’s not working you can just close that and just hold on to cash now on the banking in the sell side of this uh we have to make Loans to people that’s how we make money and that’s how we employ thousands and thousands and thousands of people at these massive Banks and even fintech uh D5 and Crypto firms as well a lot of these that are not focused on the buy side the investing piece of it but are on the sell side you have to have a fraud detection model to detect fraud you have to have all these operational models to determine optimizations of different sorts of problems like Portfolio positions for example perhaps more on the investing side and when these things blow up you have to have something there the problem with the data science approach is that you know you just slap something together to hurry to get to a solution and you don’t care if it blows up or not because you’re just going to redevelop a new model and there’s always this idea that keeps getting pushed so it’s not actually implemented in practice by many firms which is that you’re going to automate this whole process completely so now you take machine learning and Ai and you put them together and what people oddly don’t understand is you can take statistics and Ai and automate statistics as well that’s what stepwise regression or stepwise uh variable selection is so stepwise forward and backward selection you could literally just automate it and have it go out and magically pick variables throw them in find the best fit and then just keep generating model after model after model and when the models blow up it just automatically generates new models now the problem with this is in practice I mentioned it’s just when they blow up data scientists just go wasn’t me I don’t really care it was just a model and like I just want to strangle people to death often because I see this everywhere and it’s not even like it’s not even in firms I’m at it’s like you see people on LinkedIn posting this I look in forums and communities I talk to friends of mine that are running large teams I talk to friends of mine that are on the data science side and I’m like I respect you and you’re an expert and there are good data scientists out there so do not take this as they’re all bad I have friends that work in the data science Community top-notch um again master’s degrees and they are excellent and I bring these up I’m like doesn’t this just drive you absolutely nuts like they fit do you have this issue and I’ll explain like this person fit a model there’s no consideration for the actual usage of it it was all just slapped together and yes the fit was Stellar nobody understood the model nobody could figure out how Dependable the model is going to be and nobody could tell me how robust the model was going to be and there was almost no testing because again why the hell would you test anything when you can just slap it into python or into R and it will magically shoot out a model and it just gives you magical operational you know execution of it so ml Ops as we like to call it um but who cares how it really works like it just and I asked you don’t doesn’t this drive you nuts do you not look at the mathematical equations do you not need to understand how these things are freaking working and my friends like yeah yeah Dimitri it does it does drive me nuts but you get over it and I think that’s going to be the difference between so going back to the point of this CashNews.co uh that’s going to be the difference here between I think machine learning data science and Quant Finance I think that as we’re seeing now data science is starting to like segment into um not very technical roles like simple analytic roles where you can do an undergrad I think the more technical roles are starting to require Masters again it’s still there so I think in many ways if you’re on the job search one easy way to sort them is to look at does it require a master’s degree it’s probably pretty rigorous if it does not require a master’s degree it’s probably going to be data analytics because again they’re all labeled data science but again I think the problem with machine learning data science as a whole is it has to merge back in with traditional statistics at some point and just the way that it’s set up and operates um I think you’re going to continue to see this weird segmentation inside of machine learning and I think unfortunately it’s going to take probably at least 20 years or more for the machine Learning Community to realize like we need to know what we’re doing before just slapping things in or like trying to hurry and get a solution and trying to reinvent the wheel every single day which is just tiring beyond belief to deal with and so I think that piece of it once it finally becomes more mature I think you’ll finally get maybe some new titles where you have uh like we’re having data scientists now it’s kind of viewed as like a not real skilled position and now we have like ml Engineers is a more skilled technical position I think you’ll continue to see that split um where eventually ml will hopefully mature enough as a community and as a field of study that it will come full circle and actually utilize all the tools just like stats is trying to do in many cases and I don’t know how we’re going to merge these two things back together um because I mean the terminology is different current and yet it’s the exact same thing in many situations so that’s going to be a little bit of a challenge here but I think you will start to see that again those that actually need a master’s degree will continue to need it and those it can do with an undergrad continue to do with an undergrad I don’t think Quantum Finance though is going to deviate down that I have seen so many banks so it’s a side note wrap up here in a story I have seen so many banks pushing for this because they do not want to pay the price tag that Master and PhD quants cost firms do not want to pay it they are desperately looking for solutions to hire undergrads unfortunately though it just it never results in good quality models because there’s so much effort work that goes into this and even as someone who hires and trains training someone with a masters in a PhD is still a ton of work even when you hire the best of the brightest from the best programs in the country in the world it is still a ton of training and a ton of cost and because of that it’s just easier to hire Master students who already have that rigor who have that you know that drive to actually get that graduate degree and have all that additional education that a university actually did for them and they paid you know 70 to 100 000 for so anyways thanks for listening thanks for watching and as always until next time thank you
CashNews, your go-to portal for financial news and insights.
Data Science was and continues to be a generalist jack of all trade kind of role, you can do some stats, but also you are expected to present it, and if there is outage fix it, build some data pipelines, maybe take some decisions as well, get things moving, reach out to people constantly., pick up new tools, and regimes quickly. Some businesses are excellent accommodating this kind of role, those who are typically small to mid size, not too mature, not too much regulatory pains, and where being wrong is not a disaster, so where not the entire business model/capital hinges on a set of ML/stats models.
The prime example is probably e-commerce and anything in the advertising space.
I heard on some channels the term "Faker Scientist" is very fitting. My role is actually a Data Scientist in a relatively big, well known company, but I hold a not too respectable humanities degree from a shit university and I'm sweating if I need to do algebra in front of people, so I avoid that like the plague.
Quite a stupid video. Many people who call themselves DSes use statistics, parametric models too, and it is only proud and inexperienced people who follow approach you described.
You are just generalising, you are basically saying something like "I have seen some bad DSes, so all DSes must be useless and fake". A conclusion bad ML model could make, but you can do better.
Hey great video, I had to comment, because currently I'm a graduate student in a Masters of Data Science and Artificial Intelligence. So everything you're talking about is something I think about almost daily. The split you describe is even apparent in my university, where there is a program in the CS department(my program) and one in the Math Department called "Statistical Data Science". And my program really pushes the ML and programming aspect, but I'm finding that a lot of my classmates don't really understand how the models work on a mathematical level, and it makes me really skeptical. So my focus in my program has been to shore up my statistical skills as much as possible and fill up my courseload with as many Mathematical Statistics, model development, and higher level statistics classes as possible. It strikes me as really strange that a programmer would know how to program A.I. to solve a problem, yet can't do a basic linear or logistic regression and it makes me a bit uneasy. So my goal is to avoid that if possible. I am teaching myself finance by the way since my undergrad was not in the financial field, and am reading books like "Quantitative Financial Analystics" and "Option Volatility and Pricing"(with the workbook 🙂 ).
Statistics is not a branch of mathematics. It's an empirical field of study, like physics. Rigorous proof (by the standards of mathematicians) is neither required nor even desirable.
Any "theorems" in statistics require such narrow and precisely defined hypotheses that they only apply in very restrictive situations (for instance, the Neyman-Pearson theory of hypothesis testing).
Take, for example, the very commonly taught rule of thumb: When the sample size is 30 or more, the sampling distribution of the sample mean will be approximately Normal. Apart from being simply incorrect, there’s typically little or no justification for where this rule comes from. I have to think this really gets under the mathematician’s skin. Of course, rules like this come from “experience,” and it’s simply loosely true for most populations… whatever I mean by most. This kind of loose, vague language really affronts the mathematician’s sensibility.
I'm in an undergrad DS program and all the points in this video are analysed and over analysed so we can actually implement the models and undestand what is happening under the hood, because why re invent the wheel, just build a car instead.
yeah good take i remember going to a hackethon meeting and one of the participates who identified who was a data scientist it was honestly a data analyst role mostly python some data pipelines and tableau.
That's actually good problem You've pointed out. I got hired in credit risk department in a large consulting firm. They're naming themselfs as the quants but the tools being used are precisely data science'ish. Mostly classification algos. Of course, they're not working on investment risk, it's just a banking, thus sophistication of tools being used is largely limited by regulation entities. But still. I'm undergrad with a DS course done, after that I've enrolled Financial Maths MSc, but offer was too good too wait for the end of the MSc. IMHO situation on the market is that it is better to hold on a decent job offer rather than pursuing masters, but it's a good idea to acktually pursue it finally and I'm sure about this.
if you know SQL, you are now a Data Engineer, all job titles are becoming a joke. Irrespective on the job title the focus should be on the actual job/task
Hi Dimitri! Thank you for your video 🙂 I recently got admission from University of Maryland with Quantitative Finance masters program. Do you think is it helpful to get a job as a quant in the United States? I’m an international student from Korea 😂
data science is just a beginner friendly mode of computational statistics
I have been in various data roles/jobs in the past 8 years. Only 3 of these roles/jobs included a "Data Science" string in the job title (one job was at a major consulting company, another at a financial services corporation with $4bln AUM, and the last one was a smaller specialty finance family-run business). Two of these data roles were roles with decision-making authority and accountability (call it leadership?). I noticed the following pattern: when you interact with individuals, at any level of power in the corporate world, who have not taken a real analysis, fundamental math, or mathematical statistics class when they were undergrads, they assume – without asking you for details of your areas of responsibility – that what you do as "Data Scientist" is SQL + Excel+Data Integration+ Report Generation+ Data Visualization. However, those who did suffer through epsilon delta proofs dedicating 20 hours a week to homework just for one 3-credit math class, proved consistency and efficiency of estimators on their midterms, and can explain in less than a minute what a Rao-Cramer lower bound is, normally do not assume anything about my role, but ask me probing questions with increasing difficulty about the methods I use at work, and none of those questions are about SQL, Excel, Python Data Pipelines, Reporting or Tableau. I had two of the three "Data Science" jobs after I received the Master's degree ( in Economics). When I interviewed for the last two jobs, I made conscious effort to interrogate the hiring managers about the reasons they needed a particular skill from their candidate and how this skill maps to the tasks they estimate the person in my position would be assigned to do. These interviewing techniques helped me avoid miserable and toxic environments ( I refused to continue interviewing in some cases where answers were vague, misleading, or interviewing techniques were otherwise manipulative), and breeze through relatively satisfying and challenging quantitative roles with decent colleagues.
What is you opinion on data science masters that dont require quantitative background?
Haha, in so called date scientist. I totally agree with you. Date science can be anything from working with Excel to building models in python etc. It's super washed up. I like to call my self a "days scientist focusing in insurance pricing" just because ds is about nothing haha
So do you suggest going into a Finance PhD? I'm really interested in doing so and further my knowledge in Quant Finance. What do you suggest?
I'm a consultant who did his master's in Operations Research. I also work with Corporate Finance as a domain.
I agree that the democratization of data science has led to a general degradation of discourse in the space. I can not believe I got hired simply because I could clearly explain a linear regression. The bar is low if you have been rigorous in your academic life. I have seen software devs who do data science not be able to explain what would be basic reasoning taught in an econometrics course, but will use the OpenAI APIs to sell their deliverables. It's sad.
"Data science is a joke". I'm sold. I'm a data scientist, and you're right, the field is pathetic. It's also basically impossible to break into since I've been out of university for many years. And since the job these days is so incredibly easy, it's hyper competitive, and pretty much impossible to break in. If I ever want to work in the field again, I'll have to go back to school to get a degree where i'll learn absolutely nothing new.
This is sooo validating to hear!! So frustrating joining a team (or interview) and being asked ridiculous questions about overly complicated 'trendy' models or packages, and essentially being forced to ask why they don't do something much more straightforward, simple, and maintainable. Very hard to always be providing that feedback. Folks don't want to hear it!
8:06 😂😂😂
Honestly all of this is just playing with language. Of course real Data Science requires rigorous scientific thinking and solid software engineering. And it's the same for Quant Finance. Just ignore all the fake it till you make it people that think they can work in a technical role without understanding what they are doing. Imagine a mechanical engineer developing critical parts of a car without understanding the models…
Hey Dimitri, thanks for all the information! I'm about to start my masters in data science and artificial intelligence, and I'm curious about the relationship between quants and data science. Is it possible to transition from a data science role to becoming a quants professional after completing my masters? I'd love to hear your insights on this. Thanks!
Great jon
Wait until ai take over that job
Im a math undergrad and I knew from the jump that data science was just a subset of stats but this has convinced me I should just focus on learning the stats. I don't think black box models are going to hold my attention for very long. I can't deny not "needing" a masters for data science is pretty damn attractive though 😅 that might just be my laziness
Should be taken with a serious grain of salt. It’s an N of 1 viewed through an incredibly narrow scope. The man himself should’ve taken a correlation it’s not causation approach/tone to his critique.
Do you think a 'Co-op BBA (Management & Finance) and Co-op BSc (Statistics – Quantitative Finance)' degree would be enough to get a job in quant finance?
What's the difference between financial engineering and quant finance masters?
Hi Dimitri, can you make a video about how the new Chatgpt4 can impact or facilitate different types of quant?
No? Not really. A great model usually comes down to doing everything yourself from Researching to Implementing or a team of people that's reduced into different roles, even then.. it's a gamble. You could have a team that half asses everything or disagrees without the same vision in the place.
After talking to a Data Scientist recently, they are just told what their suppose to do but without validation. That's like light hearted plagiarism without being detected by the AI Bots in school. Most of them, don't even care, If it works… slap a model, make sure it looks good and calling it a day.
There are great Data Scientist but usually become Quants… if they ever dabble into Financial Markets
Well said. Very well said
Is it worth getting a MFE or MSCF after a MS stats for quant finance?
Data science nowadays can mean anything between making pizza and proving deep statistical theorems
Hi Dimitri, hope you can answer my question.
So I have a background in Industrial Engineering, I really enjoyed my analytical and modelling classes here (Operations Research, Dynamic Systems, Statistics etc.). I started my career in Data Science, as a Data Analyst doing mostly data modelling, business intelligence, and some data engineering work, but not much modelling. I've always been passionate about trading, and while working that job I started developing some trading strategies and automated them (Algo trading). I took a shift towards Quant Finance to a certain extent, now working as an Algo Trader specifically for a Market Maker.
I'm only 25, and right now I am just trying things out. I am currently on my second job (3 yrs total exp). Let's say for the next 3 years I decide to continue developing my career and working towards being a Quant (Alpha/Trading side specifically) through working or grad school. Here are my questions
1. Would the skills I would gain here be portable to other industries if after 3 years I decide I don't enjoy it as much as I thought I would? What if I were to go for Data Science (ML), and after 3 years I realized the same thing?
2. If I were to shift between the two, would it be easier to shift from Data Science to Quant Fin, or Quant Fin to Data Science
In summary, between Data Science (ML) and Quant Fin (Alpha/Trading), which path/field would put me in a better position to shift industry – assuming that industry required analytical/data analysis skills?
Thank you for your time.
An issue when modeling phenomena which evolve through time is that you can't know for sure it won't blow up. My understanding is that, if you try to do forecasting, you either have a set of variables serving as a state which captures how your response evolves, or you have to rely on things like trend extrapolation, which feels like a gamble. The first option is ideal but rarely attainable, and for the second option, I'm not quite sure how you could avoid the pitfalls.
Really informative video. Out of curiosity, do "junior quants" typically progress into senior quants in firms without educational programs, or do they end up leaving after a few years?
Dimitri, what do you think of this? https://www.youtube.com/watch?v=LlfGTSAgZkI
He’s an ML engineer and thinks modeling is a solved problem for the tech industry
The fact data scientists do not test hypotheses like econometricians do does not mean they do not have any specific questions they want to answer. Econometrics and statistics are like, I think or believe, or theory says A affects B, I want to test that hypothesis. Data science is more like, I don't have a theory, but I want to predict the future value of A…using several hundred or thousands of variables that I think affect A. Eventually, unimportant features would be dropped or given less weight. An econometrics model that can be tested does not necessarily mean it is a better predictor. And if you have hundreds or thousands of features with no formal theory, and prediction as the goal, maybe ML models are a better choice. What you have done in this video is trying to put econometrics/statistics models above data science models in quantitative finance. All your negative comments were on data science models, and all your positive comments were on econometrics/statistical models. These are two completely different models, doing different things. One should not try to compare them and choose one over the other. Econometric models want to estimate and test an effect given some predefined theory or hypothesis. Data science models, on the other hand, are about prediction when you have a lot of features with no formal predefined theory or hypothesis. If you are gonna criticize the data science field because of fake data scientists, why not also criticize the econometrics/statistics field because of fake econometricians and statisticians? Because there are fakes in any field, it does not matter whether data science or econometrics or statistics. I agree, though, data science has become easier, but so is econometrics and statistics. Defining a hypothesis is not rocket science…and anyone can come up with a testable hypothesis and use statmodels or STATA, SaaS or R, etc. One reason why data science has become popular (and not econometrics or statistics) is that you can find new insights from massive data with no predefined theory or hypothesis. But in your view, I guess that advantage is rather a flaw and not an advantage. Well, I would say do not compare oranges to apples. Besides, I also think data science has become popular because the resources to do data science are free…unlike econometrics/statistics in the past, where you needed paid software (SaaS, MATLAB, STATA, EVIEWS, SPSS, etc.). If these were free in the past, maybe, econometrics/statistics would have been like data science, where everyone can do it with almost no cost. Your trashing of the data science field here is largely unjustifiable because the same arguments also apply to the econometrics and statistics fields. In any case, testing a hypothesis is no rocket science…and it can even be easier than looking for meaningful insights in a huge data set. Just a thought. I am a fan of your channel. …. an economist trying to switch to quantitative finance.
I am an undergrad who has taken courses like graduate string theory, quantum field theory, have experience with stochastic calculus and probability theory. I don't come from school per se but has a super famous theoretical physics department (Yang institute) and I worked with many of those Professors. How do I approach a company with a preparation but not much life maturity? I have lot of coding and some ML skills.
Hey do you have a video on job opportunities for people who just have or want a undergraduate degree?I understand quant jobs are out of reach (b.s economics minor in math, know python and have used it in internships)