How to become a quant to data science reddit.

How to become a quant to data science reddit It really depends on what you want to do as a quant. When data science was just starting out as a separate entity with the new technology, physicists were the go-to group to get in your open positions. This is a cut throat field since there isn't a universal standard for being called "quant". Then it even gives you a list of quant shops and prop shops. Both Econ and finance have econometrics and reach topology in mathematics, both have big data with r programming languages yet finance also has python for finance, mathematics applied to finance, financial engineering subjects and al the risk prediction that comes with finance. This therefore puts a much greater premium on software engineering skills. What most data science roles demand is the ability to communicate with the investment business, ie something akin to a L1. Quant work is like being a surgeon with numbers. I work in London fyi. I got my undergraduate in math and a masters in business and data analytics (switched from the actuary track). Some employers ex. MIT’s MFin program can be quantitative as MFEs if you want to (just half the people don’t want to be quants). ” 1. Hey everyone, I’m (33) currently a quantitative analyst on track to become a data scientist. D. Dec 6, 2023 · Data analysts can acquire this knowledge through coursework, self-study, or experience in the financial industry. I am a freshman so I do not have much knowledge what specific skills are required and trending nowadays for quants. As a quant with around 14yoe, I tend to agree and disagree with the some of the comments here. Target, Starbucks offer education benefits you can use to complete courses and earn a degree. I’m applying to all kinds of jobs: data science, Swe, quant, insert role with math + stats + coding And out of all of them the most draining and grueling process has been prepping for quant roles. I’ve seen thoughts such as “a CS major may pidgeonhole you into dev jobs, and a compfi minor may never be put to use. Grad school does cost a lot, but a 125k starting salary makes that more affordable. As a computer science major, this path is sort of more clear and feasible. C/C++ is amazing and fast. Interesting. 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. Apparently neither of you read my comment, because it's explicit in it that the author is claiming python is better to LEARN data science, not do data science. Companies typically take students who are already/have taken grad school classes more seriously. Incredibly difficult I imagine. In order to perform all these grandiose data science tricks, you need to be able to ask the right questions, and you can only ask the right questions if you understand all I would like to become a quant developer at some point and was wondering if I anyone has some tips in making the transition coming from a standard software developer role. All require strong knowledge of a) statistics b) data science c) mathematics and they produce strong analytical, research, and modelling skills. It’s not AI, but more focused on making sense of new massive data availability. You try setting beta to some low, nonzero value. I originally thought a compfi minor would be helpful for breaking into the industry, but I’ve done some research, and I’ve seen that ML/statistics/data science/math experience might be more appropriate. 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. If you can't be detail oriented enough to read my comment, I wouldn't want you handling my data. #1 is my very first option and what I would like to do and #2 is more so of a backup. becoming a quant, especially on the buyside, provides you with the opportunity to advance to senior quant, sub pm, portfolio manager, or even to run your own fund one day, or just retire a This summer, I set an ambitious goal for myself: develop a trading model in Python by summer's end. This is probably quite a common question in this thread but I feel my situation is a little nuanced. just look into the quant positions job feeds, most of the requirements are similar to a data scientist specialized in finance. There are no other steps. Id say doing masters of financial mathematics UNSW, masters of mathematical science USYD or ANU would be your best bet and changing your IT bachelors to computer science. I had mathematics, statistics, machine learning, and a little computer science/programming, and I wanted a job where I could use all of that. I am capable of moving towards pure data science, which I may in the future. Odds are, you won’t get any solid bites for a quant internship this summer. Kaggle Datasets Grandmaster here, I'm also a Data Science Consultant at a Data & AI consulting firm. You won't be the negotiator. I would rather go for statistics, econometrics or actuarian science, or data analytics / data science degrees, or vocational degrees such as financial data science, marketing data science etc. What companies want is Data Science to deliver value and this means putting models in production to drive real impact. What matters is your course content and curriculum. Honestly, it doesn't really matter the major name. I’ll second everyone here and recommend Python. Allow yourself to be open to other pathways such as data science, finance, non quant trading, markets analysis etc have part time jobs that are relevant to building skills - programming, data analysis, math tutoring, customer service (for social skills) Deep learning, for example. That said, I'm sure it's useful for learning the stuff you mentioned in your post. But I don’t know what’s the « path ». in IB at risk management vs. It's not unusual for top quants to have a PhD in math. No entry-level Quant job will ever require Finance experience, some firms will literally reject if you do have it. for most people, quant IS the end goal, the dream job. but even without that should be no Quantitative finance is a lot like Data Science, but with more programming and CS skills. use them properly. I love finance and I want to do quantitative analysis. It’s better to have a broad knowledge base and know a little bit about a lot of different Jan 10, 2025 · Undergrad at Georgia State with sub 3. Quant Research rarely hires undergrads. First, let’s talk about the general skillset for becoming a quant. Industry Overview: Quantitative Finance and Analysis. But you'll also always be locked in as the quant. My 2 cents: given the raise of AI go for some applied statistics like data science. The narrative about how sophisticated the math is in any quant role is 50% outdated info from when option pricing was a large portion of the industry, and 50% probably deliberate deception to help with recruiting and raising capital. That being said, MFE grads have an opening for quant trading roles in the following ways: Starting out at firms where quant trading is effectively quant trading+quant research, and transitioning to a pure quant trading kind of role at a different firm. Physicists have the versatility to fit into any new tech role, data science included. It deals with systems of governance and power, and the analysis of political activities, political thought, political behavior, and associated constitutions and laws. social media sentiment traders outperformed old-school traders in the last few years. Now that data is becoming bigger and more intricate, the added value to analysing the data without being able to process raw data into actionable data is becoming negligible. Options for minor: Computer science, Business analytics, Economics, MIS. Personally for trading I prefer data science students over statistics. Start with QR and become a PM at a HF. It's also possible to start from trading or data science in finance and transition to quant roles in a few years once you've gained better quantitative skills and knowledge of financial This might be outdated, but from my experience working at a bank/brokerage, senior traders that got into research got picked up to have their own book, this curiously worked only for equities/money market, can't remember researchers/analysts being considered, also for institutional clients/funds client relationships were more important, so a sales person usually was the portfolio "manager" but Hello redditors, I have a Bachelor degree in commerce and I currently work in a non-finance company full time and day trade part time. If a data scientist has an advanced degree in a related field, they may need to consider additional coursework or certifications in finance. I wanted to get into Data Science but my friend suggested since you have commerce background and day-trade already so get into quant finance and become full time trader. I am a Data Analyst for a reputable Wealth Management firm currently in my late 20s, with a background in Wealth, Asset Management & PE Consulting from a small unknown consulting firm but worked with several blue chip clients in the industry. I study Econ but i am considering studying finance. Then apply to internships. It's more niche, requires a physics PhD, paid way more than data scientist, less crowded than data science. This is where time series/GLM comes into play Sounds like the second choice is up your alley. Competitions are mostly in Image and NLP sub themed. These are a few things that personally attracted me to this career: Quant trading is a career that you may be interested in if you are a very quantitative, academic person. I'd also learn about and explore ways to over time and over several years improve upon your skills related to Computer Science, Statistics, Math, Data Science, Finance, and other relevant topics and fields of study. Quant != data science. 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 I was originally working as a space systems engineer designing satellite systems. "Data science" has been a big buzzword the past few years and the field is only going to exponentiate throughout the decade. The rest is coding and engineering skills (write clear code and not screw up the system. Data science just wasn’t cutting it, so I interviewed and got an offer. CDOs are completely different disciplines. g. You (1) perform research on historical data to identify trading opportunities/ predictive signal, (2) back test your trading ideas by programming them into existence and then simulating them over some statistically significant period of time, and (3) you deploy them to a server that communicates with the exchange and 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 had to move into data science due to financial reasons. Working as a "quant" in HFT vs. P World - Using data science to uncover signals. I currently work as a data engineer for a quant team, my official title is quant data engineer. Also the demand is likely to become higher as the sector becomes more data-oriented. The techniques are quite different from those in derivatives pricing. This summer I plan to take the data analytics course by google. During this time you should be able to refine your soft skills and get a solid understanding of the non-data aspects of the business and how these connect to the technical skills you have, and how to drive value to the business through the application of technology and data science. So far my idea has been for a long time to study Data Science, but recently I've been reading about Quantitative Analysis, and I've started to become more interested on that. As in the quants were responsible for the ideas/theories for alpha generation, and the developers did all the programming. I’ve always liked math and statistics especially and have been thinking about graduate school first, but long term I don’t think I won’t to go back to an industry data science job, but rather I want to break into quant research or trading. I think lot of people got the impression of a quant from reading Emanuel Derman's "My Life As a Quant". If you want to become a really top level quant, like the ones who get paid a shitton of money, some amount of graduate school is probably required. Jul 1, 2024 · In this article, we explain all these points and answer how you can become a Quant. Working in quantitative finance, as a quant analyst, quant dev, quant researcher, or trader Working anywhere besides quant finance, as a data scientist. Postings about current events are fine, as long as there is a political science angle. For quant developer roles, a strong programmer (think competitive coding with C++ as main language but very good command over Python and relevant libraries too - pandas, numpy, scipy, vectorized coding, etc. People don't want to hear this, but you just have to be smart, and have good intuition for probability, game theory, and maybe some mental math. Prestigious, respected. Climb the SWE ladder, get very good at OS, Networking, and Algorithms, maybe pick up some C++ experience and some good names under your belts, then go into hedge funds / buyside quant firms as a quant dev. Maintain a high gpa, do a lot of leetcode and greenbook, get an internship at FAANG, then get an internship at a quant firm. While I do like ML, I hate anything to do with images, videos or text data. That will be ur best bet. It used to skew towards/cater to PhD hopefuls (will be starting a PhD next year) but the school has a ton of resources for recruiting and quant stuff (I started a quant club for example), although you have to be a bit proactive to a. It involves grasping the time value of money and mastering present value 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. I just hired a guy with a physics undergrad. For bioinformatics, you will certainly need a masters or PhD. , cython too if you can) with a solid command over probability and Specialize in quant and learn the basics of the data science field. It would take too long to describe even my limited understanding of the main differences between those, but as a teaser: qfin is more stressful, longer hours, quite likely would Jun 9, 2021 · Winning top ranks in competitions sponsored by quant firms could also help you land interviews (e. The work is somewhat research oriented. Hi everyone, I am a final year data science and analytics student and am dead set on becoming a quant trader. The level of business understanding required for a lot of data science work kinda makes junior data scientist a difficult role to create. Pick one that interests you more and build 1/ a yield curve or vol curve construction model and 2/ systematic strategies to test with portfolio construction, signal generation, risk management etc. One university in my state, Stony Brook, has two masters programs Statistics and Quantitative Finance, both in the Applied Mathematics and Statistics School. ) some quants also spend quite a bit of time on data management. As for tools that might be useful in quant and not data science, I can think of stochastic calculus, some advanced stats (e. My career path so far has essentially been data scientist -> actuarial analyst -> quant trader -> quant research. Data science is increasingly being used in the finance industry for tasks such as risk management, fraud detection, algorithmic trading, and customer analytics. Hi guys I'm a finance accounting major with no experience in Maths only at a high school level, wanting to become a Quant one day, what's the typical pathway for a Quant? I. Learn from the best datasets and notebooks available on the platform. Read good quant books, papers and implement strategies. Dec 23, 2024 · The one advantage of quant stuff, as the work is very technical; the pay can scale very high. Some data science could help too. You have risk quants, quant developers, quant traders, quant analysts, etc. In general, a QR will build models modelling the market, economics, individual assets, trading strategies and pricing derivatives etc. true. a) There are several good services nowdays which gives you infrastructure with data/historical data and API for order execution. ) in a quantitative field such as Mathematics, Physics, Engineering, Computer Science, Financial Engineering, or Quantitative Finance. (4. This is assuming you want to focus more on quant and less data science. A space for data science professionals to engage in discussions and debates on the subject of data science. To learn data science for a finance career, I recommend enrolling in courses at TutorT Academy. You are confusing Quant Trader with a Quant Researcher. 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". I have identified doing a masters degree in quantitative finance as a good next step along the path towards becoming one. The first option is to declare an additional major of Computer Science and the second option is to declare an additional major of Data Science and Technology. You have to understand that the MBA grads from top schools like Harvard, Stanford and Wharton usually go into banking, consulting or tech, but primarily PM or strategy roles within tech. 5 hours of LC, practicing probability brain teasers, worshipping Elements of statistical learning, and UChicago has a fantastic mathematics and applied mathematics (they call it CAAM) program (source: went there) . ) Im considering a career change out of Pharma drug development, and actuary is in my top 3 interests. You should have no problem finding a quant job. 39 votes, 14 comments. There is a lot of overlap between quant trader and quant researcher though, and where exactly the roles differ changes a bit from firm to firm. For instance, I've heard many say that in order to be a good Data Scientist one needs to not only be good at the math/stats/programming, but to also have a strong domain knowledge about the field in which they work (pharma, finance, sales, etc. However, pretty much all the information I've got about it comes from people in the US (also it feels everyone over at r/quant works in the US). Current program: MS Data Science at Vanderbilt 100% agree that it’s more efficient to recruit from specific schools, my firm does not give a test to all applicants because there are a large amount of applicants and the number of people who pass the test is way more than the number of people we would actually recruit (this would make the actual in-person interviews way too competitive/selective and makes it harder for the interviewers as That confidence will only come from knowing you can be a good quant. A subreddit to discuss political science. Only a few select firms like JSC recruit out of undergrad for Quant Research. Take one or two courses in quant finance to show interest, maybe do a project in the space, and try get an internship/entry level role at a quant shop and you'll be fine to progress into quant research if that's what you want to do In data science, MBA isn't really worth it imo. Analytics director here with 15 years experience in the field and the last 5 in leadership. Not quant in general just Jane Street/Citadel/etc. They often have Ph. 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% Masters isn't required for data science, but without relevant internship experience or a portfolio, it could be tough. A lot of posts say ‘learn stochastic calc, learn derivatives pricing’ (i. Dec 29, 2006 · More often than most people would admit. Mar 4, 2025 · A more typical career path is starting out as a data research analyst and becoming a quant after a few years. 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. To become an actual quant (as in to get the higher level analyst/researcher jobs) you'll need AT LEAST a master's degree, so many choose to continue their studies (NUS has its own MSc degree in QF too). QTs take this Actuarial science is essentially financial maths and statistics applied to insurance. Any combination of A, B and C, which may prove to be very useful if I'm trying to get a quant role 4. The exposure of negotiations can lead to more interesting human work down the line. If you aren't at a top school or if you aren't exceptional (or very lucky) getting a quant job right out the gate will be hard, so you first need to show that you are actually good on paper. 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'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. at the end of the day modern quant trading is more into data science rather than the old school of complex maths. They are not trying to become data science managers Currently majoring in Computer science and am looking at masters programs in my state that lead me towards careers in tech or trading. Yes, the interview process is especially brutal, since for some reason, you're basically required to be an expert in three disciplines (math, computer science, finance), and the positions tend be much sparser than say, a fundamental investment role on the sell side (as a strategist) or the buy side 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. Depends on the job, sure! But Data Science =/= Quant roles. 1. The areas of Quant Finance that I am most interested in are Quant Trading and Risk Analysis. It is important to distinguish between financial skills and data science skills. Data Science and Analytics (what is the difference between B and C omg) D. Like when I will graduate by may2024 what should I do? Thank you The whole idea that quant is the most intellectually stimulating role in the world, is also bogus, from the standpoint that I’ve also talked to real data scientists, (who this subreddit likes to clown repeatedly), where their work is actually data science, and they do more modeling than some of the quants I’ve talked to. Data analysts, software developers, actuary, etc are just as good a job. I graduated with a first in Business Studies; not only is it not essential at entry level to have higher education in the field, a big part of being a successful analyst is actually having the commercial acumen to understand what a business user needs. Don’t give up, quant is competitive. My initial interest in switching to a data analyst/data science/data career sort of revolved around sports analytics. Algorithmic trading is programming and research. I've met quant analysts in hedge funds and for one particular HF, the roles of the quant analysts and programmers were seperate. We have a market data team but they handle things on a firm wide level, most desks have specific needs and that means at least one person has to be fairly involved with the data. So get data, acquire the technical + practical skills, and build quant models in your free time. Creating values with quantitative methods then you’re in My current plan is do a data science bootcamp (I know they are a rip off), and am applying for quantitative finance masters for the following year. I don’t do any of this though, and it’s more of a peripheral responsibility. . And quant trading is pretty broad and on top of that most firms are secretive. You get the unit test running the appropriate data, and it does seem to be taking a long time to run. Here’s what I see, actuaries have to study for 8 years (avg) after undergrad anyway, why not just become a quant. Data mining and deep learning would be extremely useful in data science but not so much in quant. Quant trading requires a large set of skills within various disciplines, drawing most from Statistics, Math, Computer Science, Data Science, and Machine Learning. as far as I had learned from various sources - C++, DSA, a secondary Programming Language (Im learning python and doing my leet codes in python) and various concepts including Move Semantics, Designing Data Intensive applications and so on. 95% of people in the discipline provide a level of The "true quants" will dive into the available datasets, potentially creating new ones in the process, transform the data into something useful alongside data engineers and data scientists, and then start modeling the new strategies the traders have described. Another friend went tech -> qdev -> quant (in his 30s): had a math phd, went tech route first, was a bit of a mess/didn't build a good career so flipped to quant to start afresh. Machine Learning and Data Science Skills: Quants often use advanced machine learning algorithms and data science techniques. Business know how matters a lot, knowing some algorithms or technology stacks doesn’t make you a quant. Although the course is inclined more towards data science, I've made it to part 3, specifically the "transform and reshape data" lesson. I am a bit of confused whether I should pursue Data Scientist or Quantitative Analyst as my future career plan. Oct 16, 2012 · I currently work in data science/machine learning at a tech unicorn, so have tried both the tech data science and finance jobs. Q: Is the X programme at Y college good enough to get a quant job? A: Yes. 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. For tabular I would recommend other similar platforms. In Europe, it seems to me 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. Had I been in your place, I would have tried for quant developer role. Dec 16, 2023 · Month 1: Fixed Income Concepts. To be a quant trader wasn’t massively difficult, to become a quant researcher was. dont set your eyes on only quant - even the best get knocked back. 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 don’t want to intimidate u/Expensive-Republic-2 with a huge list. It’s 100% more academic. This means there isn't any classes you can take that will teach you the more important bits. All of this does not involve any finance. Data Science and Economics C. To become a quant developer you don't need to have done math heavy stuff as much as you'd need to be a very strong coder. 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). CFO), whereas Data Science would peak at something like a chief of insights/analytics for a company. Data Open, trading competitions, quant hackathons). Quant research roles are primarily for advanced degrees like Masters and PhD’s. 25 votes, 10 comments. There probably big difference between actively trading hedge funds, and slow investment funds which rebalance portfolio once in a while. quants don't know and don't need "econometrics" (unless that's your euphemism for basic stats): instrumental variables or regression discontinuity designs or almost all econometrics economists use isn't super widely quant used. Ds in computer science or statistics, etc. e. Another guy I vaguely knew, Brown grad, worked in data sci for a while seemed to have been doing pretty well then switched to a very top quant firm late 20s/early 30s. That may change over time as data science degrees mature, but so far I haven't been impressed with those. Edit: forgive my tired and unecessarily rude comment. As for changing, I would ask someone from your school. Data science will be more stable. It's better to spend your time practicing interviews and make sure you get a quant job rather than trying to convince companies that you're already a quant. Preference: Master or more in Math, Statistics, Econometrics, Finance, (edge profile) Computer Science, (edge profile) Engineering Statistical arbitrage quant Statistical arbitrage quant, works on finding patterns in data to suggest automated trades. Data science can be anything such as an internship I did in DS last year for a games company finding conclusions about the difficulty in singleplayer levels/puzzles. Read it, work out the practice problems. I took a dive into Python with the Data Scientist path on Dataquest. Where I am studying, quantitative specialization of business degree - Business Intelligence, Business Analytics, econometrics or Data science are all viable options even on job listings. Let me add, each quant job requires a subset of this knowledge, rarely Will a job use it all. 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. this is WAAAY too much for entry quant role. 5 years for undergrad is really quick, I bet you could crush a masters in a year with some planning. But there's so few jobs, where they pay you so little, and who knows if you even have a voice in these organizations. Eliminate factors such as institutional prestige, cost or alumni network, and simply look at statistics vs. One of our directors is a physics PhD. I also wasn’t deliberately making the transition. I have seen a lot of people who became data analysts with business degree, since for most positions its enough to know stats until regressions, R+SQL. Nov 4, 2024 · You can go study real analysis, abstract algebra and measure theory. In your situation, it’s the best bang for your buck: 1) you are new to programming and Python is a good first language, 2) Python has a lot of libraries for data science and machine learning, and 3) Python is widely used in quant research. I currently run the quant department at my firm and am constantly training hires from top graduate programs in ML and data science (as it relates to finance). The only commonality is that you’re developing models to understand data, there’s a lot more that goes into being a good quant Physics is data science. Like I’m talking 2-3 hours of zetamac, 1. Step 2. I interned in quant research for a bit. Topic 1 : Bond Pricing Understanding the fundamentals of bond pricing is crucial. ). Some new tenders are nearly all data science related. Members Online I have a Masters in Data Science but I feel behind on some basics. The field is asking for more education and PhDs are slowly becoming a necessary requirement versus just a preferred requirement. Definitely interested. 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. Reinforced learning, autonomous car driving research, facebook's core data science group, etc belong to this category. For my dream job, I definitely would prefer quantitative-heavy positions such as machine learning engineer or quantitative analyst as opposed to BI developer or data engineer. The software engineering is incredibly easy compared to the real value brought by building data products and getting an edge on the market through data science. We would like to show you a description here but the site won’t allow us. Okay, the pro is my life horizon will be greatly expanded, where I could network with different types of either tech or non-tech elite or excellent ppl. Most quantitative analyst have a PhD but a good percentage worked their way into the role. I’d imagine it’d be the same for Internships as well. For example some quants will be very focussed on their economic area and barely even touch regression and their knowledge comes from actual trading knowledge. b) I think it depends on which area you want to work on. My old firm masked the data from me so I had no idea what I was predicting - and our models were hidden from the data engineers. I wanted to be a quant developer but since I couldn't opt for physics I chose to become a data scientist. The thing about being a quant is the industry is competitive, so findings are not published like other industries. Some career transitions out of a math phd are: software development, data science, quant finance (much fewer roles compared to the previous two), "applied research". Happy hunting, with a PhD in physics. Over the past couple of months, I have been researching quantitative finance, and I'm very interested! If I wanted to pivot into finance and become a Quant Researcher (particularly at a hedge fund), could I? Think it’ll be difficult for you to get a job in australia as an international student especially in quant finance. Develop Your Technical Skills . I’m following the path that other quantitative analyst (who only have a masters degree) have taken. 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. /r/MCAT is a place for MCAT practice, questions, discussion, advice, social networking, news, study tips and more. All of them have a masters. 0 GPA means quant trading is pretty much impossible to recruit for. With an advanced degree you are looking at 5-6 more focused classes and likely research in a relevant area. Do all of this while having a 9-5 job related to quant (data analyst/ data science, software engineer etc). The third level is the people who can be called either data scientists or machine learning engineering who research and develop new algorithms. I've studied computer science (MEng) and have been working at an investment bank for about 2 years now. Putting the brand names aside, I want to know which field has a better long-term situation, I have heard people talking about DS going downward as AI blooms and Quant has higher salaries (maybe these infos are not accurate). 2. If you know quant trading then learn about rates (read Eurodollars by Burghardt) and options/ vol (read Natenberg). In many ways the jobs are more similar than I thought. I am seeking entry level roles. I’m pretty familiar with the MIT MFin program (went there for undergrad) and have a close friend who went to Princeton MFin so can only speak on those. Jan 28, 2024 · Skills needed to become a Quant. Quant will be great, but volatile. Just to specify, I am looking at Low Latency/ High Performance programming roles. I want to become a quant. majors extracurriculars and work experience? Is it possible to break into Quantitative finance if I learn from books or do employers want a certification of knowledge? Dec 6, 2023 · Education: A quant typically holds an advanced degree (Master’s or Ph. So which universities are best at which to study quantitative finance? Salary will be higher on the Data Science side for sure, especially starting out. The biggest advice I can give anyone trying to break into the industry is "practice, practice, and grind". Current total comp is ~270k. Data analysts should focus on developing these skills. If I'm understanding correctly, it seems to be similar to the dynamic in the Data Science field. Probably want to take some math courses, specifically probability and statistics. Anyway, to each their own. 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). Kaggle is crucial for beginners and those entering this domain. Your degree will only get you the interview. Some are physics PHDs that wanted to be in finance, other are computer science, others are financial engineers/mathematics guys with applied emphasis on Dec 26, 2024 · No offense but Google will give you a fairly good idea. 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 I'm on track to complete my bachelor's in mechanical engineering with a minor in math and CS (from a non-target US school). you need to know python/numpy/pandas basics, no mysterious "stat analysis libraries". data This is just my perspective based on what I'm seeing but Data Science seems to be becoming more of an engineering specialty as time goes by. Below are some details about my background. The projects in my portfolio involves a lot of data scraping, data cleaning, automated financial analysis, basic ML that tries to predict things and recognize patterns and images, chatbots using Llama It is extremely unlikely that you will be employed as a quant with only a data science undergrad, and being excellent at whatever math you are taught will be highly beneficial to transition from data science to quant work via an MFE or PhD. A Quantitative Finance Analyst or simply “Quant” uses and analyzes large data sets through the use of novel or applied mathematical models to analyze financial markets and securities (stocks, bonds I'm thinking about trying to switch from data science to quantitative research. My salary is about the same as the quant developers (a little less but not much) I spend my time creating data pipelines for alternative data sources to improve forecasts, creating data warehouses for analysis, cloud security and architecture. Apart from the classical spreadsheets and office and BI tools, my tech stack includes python (with data science and data viz library) and SQL. copulae), and optimization techniques. Government funding/contracting for all kinds of things is starting to include large data science components. Don’t bother with MATLAB, and probably not R. Idk if i should major undergrad in data science or comp sci if i wanna become a data scientist. if you already have serious cs&coding under your belt and do the kind of physics that involves a lot of ML/big data/nontrivial statistics (I think some of the work with collider data or astrophysics is like that?) then you're likely to easily find very beneficial quant exits. So keep that in mind. the very useful and thorough but now old Mark Joshi stuff) then a lot of more modern posts say ‘none of that is used’ but are way less comprehensive about what to actually do. Political science is the scientific study of politics. I have also realized that without any kind of domain knowledge, I am absolutely useless as a data scientist. Just wanted to feel out your opinions of the current state of the field and if you would make the same choice given the opportunity to start over. In both my quant group and DS group, I collect data, build models using statistics and machine learning, and write production software. It wasn’t particularly difficult for me, depending on your definition of quant. Quantitative Finance B. The master's in data science vs master's in CS is a stupid debate that people have on here because a lot of people feel threatened or feel territorial. physics phds from good schools who want to become quants can do it just fine. Am a maths grad working in tech in a large bank but quant trading fascinates me. I wanted to work on interesting problems and to use a wide variety of my skill set. Dead useful for a lot of quant work. I'm okay to stay at NYC or jump to west coast. Data science is growing outside of tech. There’s very important differences in the skill sets. You mess about for a while turning the data into a unit test - at first the data won't load, then you realise you were using the wrong loader configuration. Anyways, here are my options: Options for major: Finance, MIS, Economics (data analysis), Economics (mathematical economics). Dive deep into finance industry, and try to become quant. Hello Reddit, I’m currently a junior in computer science in a Canadian university. Yes, you can pursue a data science career in finance. College Of Humanity and Science, various majors A. Data science especially requires a lot of math, companies actually hiring for data scientists and not those who just do "data analysis" all day and use predefined models focus on hiring people from top colleges with math/stats background. 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 I am an incoming MS student deciding between programs. You don't become a quant with a degree, you become a quant while practicing. All tend to have blended backgrounds and varying competency in Mathematics, statistics, programming and finance. Bro, stakeholder management and business understanding is the most underrated skill in data science. Economics is very good as bachelor’s degree, but it is not enough on the master’s level for data science. learn about them and b. All of these are incredibly important to quant finance regardless of the application. Going to college next year, would like some opinions on the benefits of becoming an actuary vs a quant, and vice versa. if you're going into quant for the exit opportunities you're probably doing it for the wrong reasons. I am just a humble business analyst, but I am proud of it. Dec 26, 2024 · No offense but Google will give you a fairly good idea. I broke into the role by actively pursing openings and interviewing via recruiters. EDIT: THANK YOU FOR THE ADVICE AND MOTIVATION, I completed Coursera's Intro to data science course since this post and am motivated, data science seems more fun than straight maths! The #1 social media platform for MCAT advice. or computer science. Furthermore, you can get a data science job at a tech company, which is really competing with FAANG for work/pay. Though I can see Finance leading to very senior and executive positions in a company (e. With the rise of AI, code generation, text based prompts, IMHO Both fields will be obsolete in 10 years. You don't need to take specific classes or a major to become a Quant Trader, you don't even need to know anything about finance. It's been a long day. You should also look at BB’s they have some really good macro quant trading desk, & QR jobs that have so much more actionable data than those shops. kjyuqz qhyggd brkbfee faotvx rbnhc swqanerw uuc oplbigs vhgef qcpz wyrg glnce ulnzazj yqgji oluu