Best data science to quant reddit Thanks I've applied to Operations Analyst, BI/Business Analyst, and Quant Analyst roles on top of Data Scientist/Data Analyst. Nov 6, 2019 · What are the advantages (stability, pay, employment opportunity, etc. This is where time series/GLM comes into play Sounds like the second choice is up your alley. I would say take numerical methods in python. You don’t need a finance back ground to work in quant trading. Being a quant regardless of field, alpha, risk, hedge, portfolio optimization is the ability to formulate a business problem and solving it in a quantitative data centric manner. However, I don’t want to be stuck in academia. Academia was and continues to be getting more competitive at every stage of the process: increasing hiring/tenure standards without the compensation to match. I have a chance to study econometrics and data science (it’s not a double major) at a bachelors level and possibly continuing with graduate level degrees. Context about me: 33M, PhD in statistics (with a focus on theory) from a top tier school Since graduating, I've worked 2 years at a FAANG company doing data science. The you can easily apply that in quant fi or data Sci. Under the value-driven definition of data science, the best route to getting data science work is to be faced with real client data driven problems. Data sci may even be used as a tool for QF, so some skills can be transferrable. quant research, quant trader, and developer. I agree that some questions raised doubts about actual applications but overall I felt tested rather than overwhelmed which is why I gave my opinion as such. Your math/stats skills matter much more than your communication and software engineering skills (assuming there’s are quant developers at the firm to implement strategies for you). S. g. It’s 100% more academic. For more info go to /r/Save3rdPartyApps/ ​ https://redd. 200 covers inference which is essential for any quant/data science work, you will need to know things like p-values and hypothesis testing and apply inference into case studies, 75% As for the degree's level of prestige, if you will, involving masters programs and job applications, hardly anything will look better than data science. You need the ability to apply quantitative principles to unknown sets of data. Lower than F/G? Maybe, but I'd want to see the numbers to be truly convinced. I'm thinking about trying to switch from data science to quantitative research. Nov 4, 2024 · You can go study real analysis, abstract algebra and measure theory. The MCAT (Medical College Admission Test) is offered by the AAMC and is a required exam for admission to medical schools in the USA and Canada. With an advanced degree you are looking at 5-6 more focused classes and likely research in a relevant area. 3). CDOs are completely different disciplines. e. I want to enter in these following roles (Data Science, Data Engineering, Data Analyst, Quantitative Analyst). A community for people applying to, pursuing, or having completed a Master's degree in Computer Science or related programs (MHCI, MSDS, MSAI, MS ECE, MSBA, MCS, MIS, MEM, MSIM, MSOR etc. (Info / ^Contact). If you look at the actual people getting these promotions/jobs, they already come from a highly technical/quantitative backgrounds (Master's in Data Science, Engineering, and other STEM subjects) and/or years of experience programming or working in data science type jobs. I’m more pointing out that all the cs kids here (who are still students and know little of the real world) keep spreading nonsense about it being the best degree for quant finance, when in reality it’s not. I’ve generally found the people I work with that have MFEs bring in semi dated concepts. Preference: Math, Statistics, Operational research, computer science, (edge profile) Engineering Capital Quant A capital quant works on modelling the bank’s credit exposures and capital requirements. Reply reply throawayjhu5251 Also keep in mind, most quant finance and data science classes start as a 4th year class or as a 1st year masters class. My 2c at least. 8M subscribers in the datascience community. This is including all technical roles i. I'm wondering what projects helped you land your first job or internship in the data science field. Its going to come down to how much you are interested in the pure science with no relation to finance such as ms in CS, ms in data science, or MS in math / physics / stats. Of course one shouldn't read it as "data science BAD" without any qualifiers, or that "data science-like quant" is bad. Completely agree. Even though it sounds you skipped the internships and this year's recruiting season, still might be best to give it a try and try to get a job, if only for interview experience, as long as you have some basic familiarity with standard quant interview material Data Science. This sub will be private for at least a week from June 12th. That’s true, and it’s a very powerful combo, I’m not denying that whatsoever. Working as a "quant" in HFT vs. Agree with the sentiment in physical markets - Data science and quants in a physical firm are there for S+D modelling, hedging, predictive price etc. Below are some details about my background. Nothing yet The best Python references are online, mainly. See levels. 1. I'm quite comfortable with scikit-learn and PyTorch. Physics geek here, who's worked in data science. That being said, MFE grads have an opening for quant trading roles in the following ways: Currently majoring in Computer science and am looking at masters programs in my state that lead me towards careers in tech or trading. Nothing yet expecting recommendations for other languages and generic CS texts here. If you are analyzing labor market data (or The data science team at my firm (quant hedge fund) focuses on data platforms, data engineering, sourcing data, and processing data, all in collaboration with the quant research teams who use the data to actually do their research and come up with or refine strategies. hedge and prop firms) and I can give you some insights i gained. A vague-ish answer is that data science is more broad whereas QF is more focused, like you mentioned: stochastic calc, volatility/ risk models etc. "Data science" has been a big buzzword the past few years and the field is only going to exponentiate throughout the decade. NYU has the employment advantage of the city. Applied Data Science Lab by World Quant University: A great course to understand the concepts behind Data Science, learn advanced Python and showcase some real world projects. Thank you! Bayesian Data Analysis and Doing Bayesian Data Analysis were eye-openers for me, plus McElreath's book on Bayesian inference and R. ) Data science was still in its nascent stages and was more of a hybrid software engineering role at most places. Quant finance is the confluence of 3 courses of study: mathematics, computer science, and statistics. For undergrad I think the most important electives for me was complex analysis (for learning about the intuition of higher-dimension modeling in machine learning) and non-linear dynamics (for understanding emergent complex behavior, which is very common in financial modeling). With both data science and software engineering I've noticed having AWS/Azure or some other cloud platform certifications can be huge for hiring and getting promotions/raises. quant devs are math and comp sci. Over the past year, my interests have shifted away from the pure computer science aspects of Data Science, and I'm drawn to the prospect of becoming a quant. I have also realized that without any kind of domain knowledge, I am absolutely useless as a data scientist. I interned in quant research for a bit. At the end of the day the only thing that matters is how much you know and how well you interview, if you get past the initial resume screen, an MS in data science is viewed as a stat + CS guy and their interview questions will revolve around those topics (more so in ML). I would be pretty surprised if that were true. Someone has linked to this thread from another place on reddit: [r/algoprojects] Quant/data science at physical commodity trader If you follow any of the above links, please respect the rules of reddit and don't vote in the other threads. A space for data science professionals to engage in discussions and debates on the subject of data… I call them the data scientist and analyst, before the term was coined, it is essentially portfolio optimization and inefficiency finder. That may change over time as data science degrees mature, but so far I haven't been impressed with those. So keep that in mind. in IB at risk management vs. ) of being a quant over data science in your opinion? Is it relatively easy for a person with quant skillsets to take on a job as a data scientist/data analyst with some side project experiences or MOOCs? Jan 27, 2019 · Hard to say how many new grads these firms hire but I’d estimate it’s in the low hundreds. I just switched from quant dev to a "data scientist" and my job is more applied math (optimization problems, improving computational efficiency, stochastic modeling, with some statistics/ML). it seems the average pay of quant is worse than SDE. Quant will be great, but volatile. Heard on the Street: Quantitative Questions from Wall Street Job Interviews - Falcon Crack. Did real analysis undergrad for mathematicians and it's way too theory focused for a dummy like me. Someone with a few years of experience in an analyst role who has cursory experience building ML models is probably going to be more successful in a “standard” data scientist role than a recent college grad who’s handy with ML but has very little Sounds like the author might not have realized this upfront. I would focus less on job title-based career progression and focus more on what their respective roles entail and whether they meet your expectations and wants. I've been trying to get into the quant industry (espc. If I choose this major, there is a concentration for financial econometrics/quant finance. It was great! In response to your question about pursuing a master's degree in data science, I wholeheartedly agree that it's a strategic choice that can significantly enhance your skill set for a successful data science career. I had to move into data science due to financial reasons. These classes taught me what statistics is really like, and showed me all the parts of data analysis I missed in my first four years of taking AI classes only here. Undergrads aiming at this career typically have 1-2 internships and get their best top firm recruiting shot right out of college. It’s also one that can help you get into quant finance. Quant research roles are primarily for advanced degrees like Masters and PhD’s. it/144f6xm/ IME, 70% of "real data science" is data cleaning / understanding what limitations and problems data have, which *to my knowledge*, is not typically reflected by kaggle competitions, but I could be wrong. I decided to do 2 Coursea courses IBM Data Science and Short Course in Machine Learning from UNSW if I want to put my foot through the door. But for vice versa, not so sure. (Info / ^Contact) Personally for trading I prefer data science students over statistics. Current program: MS Data Science at Vanderbilt More importantly however, the behavior of reddit leadership in implementing these changes has been reprehensible. It really depends on what you want to do as a quant. true. Working in quantitative finance, as a quant analyst, quant dev, quant researcher, or trader Working anywhere besides quant finance, as a data scientist. Likewise, if you want to do research based work (quant researcher and quant software engineer are the two primary roles you'll probably be interested in) then a phd is specializing in research and learning all of that, so The #1 social media platform for MCAT advice. Jan 28, 2024 · If you want the highest chances to get a quant job, make sure to take a STEM university degree: Maths, Physics, Engineering are best. Furthermore, you can get a data science job at a tech company, which is really competing with FAANG for work/pay. Yes, an MS in Data Science. fyi for data points (best website for info on comp for technical positions) Specialize in quant and learn the basics of the data science field. Apparently interning/doing research every semester for 4 semesters (and summer internship) doesn't count as being a full year of work for some people. The level of business understanding required for a lot of data science work kinda makes junior data scientist a difficult role to create. The areas of Quant Finance that I am most interested in are Quant Trading and Risk Analysis. Someone has linked to this thread from another place on reddit: [r/algoprojects] Books on machine learning in quant finance If you follow any of the above links, please respect the rules of reddit and don't vote in the other threads. Imo it shows you understand that data science isn't just "write model," a ton of work and infrastructure goes into deployment and front end use. Others. Only a few select firms like JSC recruit out of undergrad for Quant Research. I was originally working as a space systems engineer designing satellite systems. Feb 20, 2023 · I was hoping to get some insights about what steps I can take to break into Quantitative Finance as an MS Data Science student. They might ask for a general interest in finance, and why exactly did you apply or how did you get to know them, but that's about it, you just need to have an answer that's different from "I like the money". Interview Preparation. Most quant jobs only need you to be good at 1 or 2 of those but you need to be able to dabble in all 3. non-quants) are hiring data scientists to help them model the economy. While I do like ML, I hate anything to do with images, videos or text data. This is my first data science test so based on my studies and my hobby applications of machine learning, I found I could be competitive when answering the questions. I was formerly a data scientist at a large company and am currently a quant researcher at a hedge fund, so I have some insight about this. They don't care if you don't know a single bit of finance. During my masters, I got a data science internship at a (~1,000 person) tech company. As for quant trading, landing a first interview is honestly not that hard like IB (However, the difficulty of the interview process is on a whole another level). I'm going to be finishing my Masters in Data Science this September and I’m interested in developing my skills towards a career as a Quantitative Analyst or Quant Trader. #1 is my very first option and what I would like to do and #2 is more so of a backup. I’ve got my eyes set out for either DS or Quant Research role in the future and I am taking BSci with DS and Financial Maths. To be honest the real nitty gritty of quant work in the financial world is in specific quant firms. With the rise of AI, code generation, text based prompts, IMHO Both fields will be obsolete in 10 years. E. Want to work in AI/CS and math in finance. The only thing is i was leaning to UChicago since it’s the holy grail for quants. 39 votes, 14 comments. Though I can see Finance leading to very senior and executive positions in a company (e. Your background is perfect, quant firms specifically looks for math/stats graduate, but PHD is usually preferred for a quant research role. I still appreciate the machine learning, data analysis, and advanced math and statistics components of the curriculum, but I'm considering if a more finance or pure mathematics-oriented P. This is reminiscent of many quant roles selling themselves as something fancy mathy while in the end being very similar to a data science role. I'm finding better results when searching "Junior Data Analyst" though. If I wanted to be a quant the best route of study is to double major in applied mathematics, and statistics, with a minor in computer science or data science. The course have python's basic lessons (haven't looked so I can't tell about those lessons but I can presume that those lessons will be informative as other lessons), now the course cost only 10 euro (for the amount of information you get in one place, it's a increadibly cheap price), so it's worth to give a try as your starting point in this rabit hole. P World - Using data science to uncover signals. /r/MCAT is a place for MCAT practice, questions, discussion, advice, social networking, news, study tips and more. From a contact I have who works with quants and is a trader she says that the general consensus with her work team is that always choose maths whether that is financial maths or statistics over finance if you want to break into quant roles. I am thinking between NYU stern with a joint major in math and cs vs UChicago Econ and data science. Most MFE grads/PhDs prefer Quant Research/Data Science based roles because of the lower risk involved as well as doing work more in line with their advanced degrees. That said, I'm sure it's useful for learning the stuff you mentioned in your post. A masters is also highly favourable. I got a master's in Statistics (integrated program with bachelor's), and things have worked out great. I hope that this would be useful to some people. No entry-level Quant job will ever require Finance experience, some firms will literally reject if you do have it. I'm looking for a job or internship in the data science/analytics field. Even if you're looking for non-Bayesian approaches, there's a ton to learn and appreciate. Quant traders are math and stats, with a working knowledge of programming. When it comes to choosing the right school, I believe Oklahoma State University is an outstanding choice. The work is somewhat research oriented. The finance and data world has broadened -- beyond traditional quant trading or quant research, buy-side firms are now using very broad ranges of data to trade (ie. You're probably better off doing investment banking, sales, trading, etc. Data science will be more stable. Your degree will only get you the interview. I'm interested in projects that are both challenging and relevant to the real world. It is interesting work and pays well. A minor in Computer Science or Business Analytics would complement the major well. Current total comp is ~270k. I am seeking entry level roles. I have experience as a part-time Data Scientist at a software development company and have an opportunity available to work as a data scientist at a start-up bank when I Why quant then? I don't think that quant jobs give too many opportunities for that. Salary will be higher on the Data Science side for sure, especially starting out. CFO), whereas Data Science would peak at something like a chief of insights/analytics for a company. so called alternative data) and even discretionary funds (ie. One university in my state, Stony Brook, has two masters programs Statistics and Quantitative Finance, both in the Applied Mathematics and Statistics School. You don't have to find experiences where you are tasked to build the best ML model and prove your value as a data scientist that way. applied math for financial contexts. Quant Job Interview Questions And Answers (2008) - Joshi, Denson, Downes I dont know of a lot of bachelors degrees that dig deep into machine learning and data science except for maybe 1 or 2 advanced classes. I came back and decided I would switch to data science, but I was worried I would miss out on the clear, predictable, generous pay of an actuary. Econometrics you will have a deep understanding of one the most widely used methods in statistics, data science, quant finance and programs like EME require you to learn those tools using more math than most American engineering students take. Best Master's degree to break into Quant (Jane Street specifically) Education & Certifications I just completed my bachelor's in mathematics from a non target T-20 with a sub par GPA (3. For a career in quant or data science, a major in either Finance or Economics (with a focus on data analysis or mathematical economics) would be beneficial. dgqib hvoju zruw nyhwl zuj opxzbb mxcaz yqjbv qorutwxq xwj hqchbd eruy fgkudvu hjrzv fxhe