quantitative finance physics phd

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Quantitative Finance MS and PhD  

Figure: Implied Volatility Surface

SPECIAL QUALITIES OF STONY BROOK QUANTITATIVE FINANCE PROGRAM

Most of the Applied Mathematics faculty teaching quantitative finance courses have extensive experience building quantitative trading systems on Wall Street. Because of their Wall Street backgrounds, our faculty are able to place many of their QF students in  internships  during the summer and the academic year at hedge funds and major investment companies. Few other QF programs offer internships. There is limited use of adjunct faculty who come to campus one or two evenings a week after work.

Figure: Merger Arbitrage Strategy

In the world of finance, the name 'Stony Brook' is famous for Renaissance Technologies, which is located a mile from the Stony Brook campus and headed by the former chairman of the Stony Brook Mathematics Department. Renaissance's flagship Medallion Fund has been the best performing hedge fund in the world for the past 20 years. One of the key creative minds at Renaissance, Robert Frey, Stony Brook Applied Mathematics PhD 1986, returned to Stony Brook in 2005 after early retirement at Renaissance to develop a Quantitative Finance program in the Stony Brook Applied Mathematics Department. Frey is chairman of the advisory committee to the University of Chicago's mathematical finance program, the country's best-ranked program in this area. 

The Stony Brook Quantitative Finance program is unique among mathematical sciences departments in its very practical focus on 'alpha generation', Wall Street term for trading strategies for making money. Courses are centered on projects where students use real tick data to analyze and predict the performance of individual stocks and commodities, market indices and derivatives. Also, Stony Brook is one of a small number of quantitative finance programs offering PhD as well as MS training. Our PhDs have taken positions both in Wall Street firms and in university quantitative finance programs. For more information about our quantitative finance courses and faculty, see  QF Courses  and  QF People .

Figure: New York Stock Exchange

Course Requirements for the Quantitative Finance Track

The standard program of study for the M.S. degree specializing in quantitative finance consists of : 

AMS 507   Introduction to Probability AMS 510   Analytical Methods for Applied Mathematics and Statistics AMS 511   Foundations of Quantitative Finance AMS 512   Portfolio Theory AMS 513   Financial Derivatives and Stochastic Calculus AMS 514   Computational Finance AMS 516   Statistical Methods in Finance AMS 517   Quantitative Risk Management AMS 518   Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization AMS 572   Data Analysis

Quantitative Finance Track Electives (students must take at least  2 elective courses  to achieve at least  36  graduate credits along with the required courses):  AMS 515 Case Studies in Machine Learning and Finance  AMS 520 Machine Learning in Quantitative Finance AMS 522 Bayesian Methods in Finance AMS 523 Mathematics of High Frequency Finance AMS 526 Numerical Analysis I  AMS 527 Numerical Analysis II  AMS 528 Numerical Analysis III  AMS 530 Principles of Parallel Computing  AMS 540 Linear Programming AMS 542 Analysis of Algorithms AMS 550 Stochastic Models AMS 553 Simulation and Modeling AMS 560 Big Data Systems, Algorithms and Networks AMS 561 Introduction to Computational and Data Science AMS 562 Introduction to Scientific Programming in C++ AMS 569 Probability Theory I AMS 570 Introduction to Mathematical Statistics AMS 578 Regression Theory AMS 580 Statistical Learning AMS 586  Time Series AMS 595   Fundamentals of Computing AMS 603   Risk Measures for Finance and Data Analysis

Elective courses in the QF program are split in five focus areas. Students can follow one of the following course sequences depending upon their interests.

(A) Typical course sequence:   Modelling and risk management in finance

• First Semester - AMS  507 ,  510 ,  511 , 572 ( or Electives: AMS 520 for those who have already taken an equivalent data analysis course before and have experience with Python)
• Second Semester - AMS  512 ,  513 ,  517 (Electives: AMS 515 , 522 , 523 , 603 )
• Third Semester - AMS  514 ,  516 , 518 (Electives: AMS 553 )

 (B) Typical course sequence:   Machine learning and big data

• First Semester - AMS  507 ,  510 ,  511 , 572 (or Elective AMS 520 for those who have already taken an equivalent data analysis course before and have experience with Python)
• Second Semester - AMS  512 ,  513 ,  517 (Electives: AMS 515 , 560 , 580 )
• Third Semester - AMS  514 ,  516 , 518 (Electives: AMS 586 )

 (C) Typical course sequence:   Statistics and data analytics

• First Semester - AMS  507 ,  510 ,  511 , 572 (or Elective AMS 520 for those who have already taken an equivalent data analysis course before and have experience with Python)
• Second Semester - AMS  512 ,  513 ,  517 (Electives: AMS 515 , 570 , 578 (with pre-requisite 572  )
• Third Semester - AMS  514 ,  516 , 518 (Electives: AMS 553 , 586 ) 

  (D) Typical course sequence:   Stochastic calculus, optimization, and   operation research

• Second Semester - AMS  512 ,  513 ,  517 (Electives: AMS 515 , 542 , 550 , 569 )
• Third Semester - AMS  514 ,  516 , 518 (Electives: AMS 540 , 553 )

(E) Typical course sequence: Computational methods and algorithms

• Second Semester - AMS  512 ,  513 ,  517 (Electives: AMS 515 , 527 , 528 , 561 )
• Third Semester - AMS  514 ,  516 , 518 (Electives: AMS 530 , 562 , 526 (co-requisite or pre-requisite 595 or 561 )

Note 1 : If you have poor programming skills take the following electives (instead of electives recommended in sequences) : AMS 595  Fundamentals of computing (Fall semester) or AMS 561 Introduction to computational and data science (Spring semester). Programming skills are critically important for industrial jobs. Note 2: If a 4th semester becomes necessary, a required course will be needed to continue.

For Ph.D. requirements please click here .

Quantitative Finance Opportunities for Applied Mathematics Graduate Students in Other Tracks Any strong student (3.5+ GPA in first-semester core courses) in another track may enroll in AMS 511 , Foundations in Quantitative Finance.  Selected students, with the permission of the Director of the Center for Quantitative Finance, may take additional quantitative finance courses. Students are eligible to earn an  Advanced Certificate in Quantitative Finance . You must formally apply for the secondary certificate program prior to taking the required courses. Only a maximum of six credits taken prior to enrolling in the certificate program may be used towards the requirements.  The 15-credit advanced certificate requires AMS 511 ,  AMS 512 ,  AMS 513 ,  one additional Quantitative Finance Graduate course  elective, and one additional Applied Mathematics course chosen with an advisor’s approval.

To apply, download the registration form, please click here .

For gainful employment disclosure information for our Quantitative Finance Program, please contact the AMS Department.

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An MPhil/PhD is an advanced postgraduate research degree that requires original research and the submission of a substantial dissertation of 60,000 to 100,000 words. At Birkbeck, you are initially registered on an MPhil and you upgrade to a PhD after satisfactory progress in the first year or two. You need to find a suitable academic supervisor at Birkbeck, who can offer the requisite expertise to guide and support you through your research. Find out more about undertaking a research degree at Birkbeck .

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Mathematics and statistics mphil/phd: 7 years part-time, on campus, starting 2024-25.

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Mathematics and Statistics MPhil/PhD: 7 years part-time or 4 years full-time, on campus, starting in academic year 2024-25

Academic year 2024–25, starting october 2024, january 2025, april 2025.

Part-time home students: £2,539 per year Full-time home students: £4,786 per year Part-time international students : £7,525 per year Full-time international students: £14,885 per year

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• Non-associative algebras and normal ideals
• Canonical auto and cross correlations of multivariate time series
• Dimension and measure defined by infinite Bernoulli convolutions
• Some Markov decision models for pest control
• Aspects of estimation in linear and non-linear time series models
• Inventory renewal time series and their ARMA equivalents
• Some statistical aspects of the estimation of fire losses
• The use of tensor algebras in population genetics.

Extensive computing facilities include PCs and UNIX platforms and generic courses and workshops are offered by the College.

Read more about  our vibrant research culture .

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Find out more about the research interests of our academic staff:

• Brad Baxter, BA, PhD : approximation theory; numerical analysis; mathematical finance; theory and algorithms of radial basis functions.
• Anthony Brooms, BSc, MSc, PhD : stochastic processes; stochastic order results; stochastic games, with applications to models of service systems; optimisation and control.
• Swati Chandna, BSc, MSc, PhD : statistical analysis of network data; time series in the frequency domain; speech signal processing; boot-strap methods for time series; saptio-temporal analysis.
• Simon Hubbert, PhD : approximation theory, optimisation and mathematical finance.
• Professor Steven Noble, MSc, DPhil : combinatorics, particularly graph polynomials.
• Professor Maura Paterson, BSc, PhD : information security; combinatorics.
• Ilaria Peri, PhD : mathematical finance; quantitative finance; risk and performance measures; financial risk; backtesting.
• Richard Pymar, MA, MMath, PhD : probability theory, particularly interacting particle systems, mixing times, random walks (in random environment).

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• Computer Science and Information Systems (MPhil/PhD)

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Quantitative finance for physicists

I am looking for good books to learn quantitative finance. As I have strong background in physics, I would appreciate introductions that do not hesitate to show the equations, but in the same time cover the finance rather comprehensively. Most of what I have seen up till now errs either a) in the direction of explaining elementary probabilistic concepts, or b) towards formal math/statistics, or c) giving just a gist of it.

• finance-mathematics
• statistical-finance

• 3 $\begingroup$ You may also want to check Wilmott’s quantitative finance, he takes a very PDE centric approach, which will be very easy to follow. But please check sample pages before buying! $\endgroup$ –  Magic is in the chain Commented Jun 2, 2020 at 19:15
• $\begingroup$ Did you study measures in calculus curriculum? Did you study measure theoretic probability? $\endgroup$ –  Aksakal almost surely binary Commented Jun 2, 2020 at 23:25
• 2 $\begingroup$ Perhaps Steven Steve 2 volumes of Stochastic calculus in Finance? If your background in dealing with Maths is strong, I think you can start with volume 2 directly, which deals with continuous time $\endgroup$ –  Idonknow Commented Jun 2, 2020 at 23:39
• 1 $\begingroup$ What's your goal? Generally, Baxter & Rennie is a good intro to the Black Scholes world. $\endgroup$ –  LazyCat Commented Jun 3, 2020 at 2:37
• $\begingroup$ @Aksakalalmostsurelybinary I don't have background in measures... but I am quite at ease with Langevin equations, Fokker-Planck and path integrals. $\endgroup$ –  Roger V. Commented Jun 3, 2020 at 4:30

5 Answers 5

Physicists typically know pdes but not stochastic calculus.

I have a masters in physics, so have a reasonable idea of the usual skillsets a physicist will know (at least at undergraduate level), and also then a masters in mathematical finance, so learnt the hard way the bits of maths physicists typically don't know but will need to know for quantitative finance.

Typically physicists are very strong with linear algebra, and PDEs, but the world we work in is largely deterministic (I'll overlook QM for now) and we rarely do much with distributions. If you are happy enough to have a 5-minute overview of Ito calculus and focus on the PDEs that appear in finance and options pricing, then it is possible to take a very PDE centered approach. A great book in this regard is the book The Mathematics of Financial Derivatives (1995) by Wilmott, Howison, and Dewynne.

If you want to know stochastics

If you take the PDE approach then much of quantitative finance will be inaccessible to you, as you can only go a small way before learning Ito calculus is required. A great resource I found for this was Introduction to stochastic calculus with applications by Klebaner . This will give you pretty much all the stochastic calculus skills you will need.

Some more advanced stochastics and control theory

At this point you will be able to go into much of quantitative finance (or at least have the core skills to). However there are some branches where I think you will need a fair chunk more of mathematics, and the biggest is likely control theory (and the HJB equations), for which there are only really graduate books, and the best I can think of is Stochastic Controls: Hamiltonian Systems and HJB Equations by Yong and Zhou .

So far all of this is largely focussed on financial modelling, but from a theory based perspective rather than from an empirical or statistical perspective. Of course a huge number of hedge funds (and investment banks) model financial behaviour through statistical trends, or even just through blackbox machine learning. A great book for time series and statistics is Introduction to Time Series and Forecasting by Brockwell and Davis, and the standard book (amongst several) for statistics and machine learning is The Elements of Statistical Learning by Friedman, Tibshirani, and Hastie .

At this point you can now cover the main items including: options pricing, fixed income, statistical arbitrage, time series modelling, numerical methods, optimal control, etc.

• $\begingroup$ Thank you for the recommendations! In fact, I am rather comfortable with Langevin equations, Fokker-Planck equation and have some inactive background in path integrals. However the mathematical underpinnings seem to be lacking, since measures and Ito calculus seem like things I might have to learn about. $\endgroup$ –  Roger V. Commented Jun 3, 2020 at 10:09
• 1 $\begingroup$ Langevin and Fokker-Planck are still just PDEs and they give you the evolution for distributions, but they are still very disjoint from SDEs, which is really the language of quantitative finance, for which you need to know Ito calculus. The best book for this that I found was Klebaner's. $\endgroup$ –  oliversm Commented Jun 3, 2020 at 10:18

It's not a great book, but Jan Dash. Quantitative Finance and Risk Management: A Physicist's Approach. World Scientific Publishing Company (2004) takes the approach that you might like - not too much formal math, and not too elementary.

Since you didn't study measure theoretic probability, that would be the first thing I recommend. In my opinion that's the main gap that many physicists on math side, because stochastic calculus is not in mainstream physics curriculum.

Whether you first study measure theory in calculus then take on probability, or jump right into measure theoretic probability is up to you. I took the first approach:

• I studied Kolmogorov's classical text in Russian. It's very clearly written, and surprisingly accessible to non mathematicians. I had one of my math professors help me digest the content.
• Then I took PhD course with Billingsley's text " probability and measure ," which covers both subjects at once. I think it's possible in principle to learn both following this book, but I had a feeling that everyone in the class room knew measure theory, sets etc.
• I also took a PhD seminar on continuous stochastic calculus and we used Shreve's text's second volume . Again, it is not impossible to start with this book, but it's written for mathematicians, unless you're a theoretical or math physicist it will not be a comfortable read.

If you want to follow this path then I recommend enrolling/auditing PhD classes on this subjects in a local university.

A completely different approach would be to start from the end, e.g. read Wilmott's three volume book , Hull's "options..." text or Neftci's stochastic calculus text. I've seen people going this route too. It depends on your background and how much time you allocate for this project.

Then you need to study finance itself. That's a whole different ball game. If you have funds and time, then maybe getting MBA or CFA Level 1 exam is the most comprehensive approach.

• $\begingroup$ I would recommend the second approach. $\endgroup$ –  Lisa Ann Commented Jun 3, 2020 at 19:23

As a long practicing plasma physicist who moved into quantdom (now retired), I suggest focusing on stochastic calculus and modeling. How deep you go down the rabbit hole of measure theory will depend on what you do. Simulation will be your friend and help you in many situations. To the excellent suggestions above, I add Paul Glasserman's Monte Carlo Methods in Financial Engineering. Build up a repertoire of solved derivative models as soon as you can. Have Fun, I did. ntgladd

As a former physicist you will certainly enjoy Jean-Philippe Bouchaud’s approach. Pragmatic and empirical with simple models that are sophisticated enough to be useful.

Check out “Theory of Financial Risk and Derivative Pricing: From Statistical Physics to Risk Management” and “Trades, Quotes and Prices: Financial Markets Under the Microscope” in that order.

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We have 35 quantitative finance PhD Projects, Programmes & Scholarships

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quantitative finance PhD Projects, Programmes & Scholarships

Phd mathematical sciences, funded phd programme (students worldwide).

Some or all of the PhD opportunities in this programme have funding attached. Applications for this programme are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full programme details for further information.

China PhD Programme

A Chinese PhD usually takes 3-4 years and often involves following a formal teaching plan (set by your supervisor) as well as carrying out your own original research. Your PhD thesis will be publicly examined in front of a panel of expert. Some international programmes are offered in English, but others will be taught in Mandarin Chinese.

Fully Funded PhD opportunities in Business, Economics and Finance Sciences

4 year phd programme.

4 Year PhD Programmes are extended PhD opportunities that involve more training and preparation. You will usually complete taught courses in your first year (sometimes equivalent to a Masters in your subject) before choosing and proposing your research project. You will then research and submit your thesis in the normal way.

PhD in Finance at Henley Business School

Business research programme.

Business Research Programmes present a range of research opportunities, shaped by a university’s particular expertise, facilities and resources. You will usually identify a suitable topic for your PhD and propose your own project. Additional training and development opportunities may also be offered as part of your programme.

A critical political economy of money, finance and finacialization

Phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Self-Funded PhD Students Only

This project does not have funding attached. You will need to have your own means of paying fees and living costs and / or seek separate funding from student finance, charities or trusts.

PhD in Business, Economics and Social Sciences – 55 doctoral positions

Germany phd programme.

A German PhD usually takes 3-4 years. Traditional programmes focus on independent research, but more structured PhDs involve additional training units (worth 180-240 ECTS credits) as well as placement opportunities. Both options require you to produce a thesis and present it for examination. Many programmes are delivered in English.

Various projects in theoretical ecology and modelling

Funded phd project (students worldwide).

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

PhD Studentship (3 years): Understanding Financial Wellbeing in broader inclusive context: conceptualisation, measurement and interventions

An investigation of quantum cognition in financial decision making, leveraging business models for investment due diligence in the venture capital, structural characterizatoin of cell signaling at the membrane interface, unraveling nuclear pka activity in neurons of mouse striatum during behavior, hijacking mechanism of dengue virus on human plasmin to enhance the permeability of mosquito midgut for infection., effects of urbanisation on insect biodiversity, cryo-em structure and function of membrane proteins involved in human diseases, control of human chromosome condensation in situ.

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Master Quantitative Finance

The specialized Master's degree programme of Science ETH UZH in Quantitative Finance is a highly international degree programme offered jointly by the University of Zurich (Department of Banking and Finance) and ETH Zurich (Department of Mathematics).

A distinguishing feature is its unique combination of economic theory and finance with mathe­matical methods (probability theory, statistics and econometrics, numerical analysis) for finance and insurance. Recent developments in the FinTech area are included in several lectures.

The curriculum consists of two semesters of coursework followed by the writing of a Master's thesis.

The programme qualifies through

• A very competitive selection of participants (around 15%-20% admitted each year)
• A close supervision by the programme director thanks to a small group of students
• Good opportunities for interaction with the Swiss financial services industry
• The international dimension - more than 60% of students come from outside of Switzerland

Graduates are prepared for excellent opportunities to work as specialists in quantitatively oriented areas of the financial services industry. Another career opportunity is to begin doctoral studies in a programme in (quantitative) finance, opening the door to an academic career.

The programme consists of core courses and elective courses. Each student needs to acquire 36 credits from the core courses, 24 credits from the elective courses and 30 credits with the Master’s thesis. Any course of the programme is part either of the field "Finance (FIN)" or of the field "Mathematical Methods for Finance (MF)".  

Language of instruction

Credits | duration.

90 ECTS | 1.5 years

Academic title

Master of Science UZH ETH in Quantitative Finance  

Qualifying disciplines

The MSc Quantitative Finance strictly requires a University Bachelor's degree with

• an economics
• and/or science background (mathematics, physics, engineering, data science, etc)

There is a special admission and application process for this Master's degree programme.

Detailed information

external page Master's degree programme Quantitative Finance (information on application, admission, selection process, deadline and curriculum)

• Phone phone +41 44 634 39 53

ETH Zurich Department of Mathematics Plattenstrasse 14 8032 Zurich Switzerland

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Your responsibilities will vary based on your location and team assignment. You'll build innovative solutions that make a difference for our customers, clients, and employees. You’ll create and improve the design, analytics, coding and testing for high-quality software and new products. You’ll own projects end-to-end, keeping teams and stakeholders up to speed on the progress of what’s being developed. The Software Engineer Program will allow you to explore agile software development methodologies, pair programming, resiliency patterns and chaos engineering, and more. The work will be varied.  You could be developing digital and mobile features that give our customers and clients more control over how they bank with us. You could be strategizing on how big data can make our trading systems quicker. You could help create the next innovation in payments for merchants. You could be engineering automated recovery solutions on a global scale. You could be supporting the integration of our private and public cloud platforms. Across all projects and businesses, you’ll have the opportunity to develop your coding skills, work with innovative technologies such as machine learning, and build solutions using agile methodologies and more. You’ll develop the skills to take your career in any direction and make a genuine contribution to our businesses from the start. Because we’re always looking for new ways to innovate, your ideas and contributions are welcome from the beginning.

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We’re looking for enthusiastic, capable and motivated Computer Science and/or Engineering majors who want to directly contribute to our businesses from day one. No matter your background, we’re looking for those with a strong interest in financial services and excellent coding skills. Our teams work collaboratively, so we’re looking for those who have excellent teamwork and demonstrated leadership abilities.

You should have excellent coding skills, be able to manage relationships with clients, and have exceptional problem solving and analytical thinking skills. We’re looking for those who are intellectually curious, collaborative and open to new challenges, as well as being able to take ownership of projects to bring them to fruition.

This full-time program will give you the opportunity to learn about our technology business and develop your career. You could work on projects that deliver real solutions for our customers, clients and businesses. No matter if you’re working on payment solutions or trading algorithms around the world, you’ll see tangible results from your work.

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Dive head first into creating innovative solutions that make a difference for our customers, clients and employees. Our program is designed to make sure you’re supported and learning new skills. You’ll have clear priorities and projects where you’ll be able to make a difference across our business and add value from the start.

You are encouraged to take time to explore, shadowing other teams and networking with various people. You'll be fully integrated into our technology community with the opportunity to attend social events, tech talks, interact with senior leaders, and more.

You’ll begin with a comprehensive induction program to learn about our businesses, build on your knowledge of development methodologies, and develop your professional skills. You'll join our agile Force for Good teams to create real-world technology solutions for non-profit organizations through a series of “side” projects that take place over several months. You’ll have access to continuous training both on-the-job and via courses to build your technical and business skills. We’ll cover topics ranging from cybersecurity to presentation skills to further your career development. Our teams are dedicated to your support and advocacy throughout the two years of the program.

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Once you’ve successfully completed the program, there will be opportunity for advancement in our Technology organization based on your performance. Our culture of continuous learning will help you to take your career further. As you grow, you’ll also be able to explore opportunities with many teams in various locations across our company.

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Physics + finance = career opportunities.

A joint venture between UNC’s Department of Physics and Astronomy and UNC Kenan-Flagler, the new major combines physics classes – such as mechanics and electromagnetism – with business and math courses – such as applied derivatives and differential equations.

Finance professor Alexander Arapoglou and physics professor Reyco Henning are collaborating to offer the new major.

“Physics is an intellectually interesting subject,” said Arapoglou. “This major creates an opportunity for students to prepare themselves for future employment in such areas as quantitative finance, risk analysis and management, and portfolio management.”

The numerical methods developed for solving problems in physics have direct applications in finance. Top financial institutions regularly employ students with math and physics backgrounds – known as “quants.”

There is strong demand for people who can understand complex mathematical models and solve hard quantitative business problems, according to Arapoglou. “This is a great time to enter the finance job market.”

A physics major lends itself well to a career in finance because students learn quantitative analysis and how to handle real world data and uncertainties, Henning said. “In finance, a lot of times there are hard problems for which there is no text book or class. In physics, students learn to handle real-world data and uncertainties.”

Henning said that many of his physicist colleagues now work in finance, and when Arapoglou (MBA ’80) worked at J.P. Morgan his colleagues often had PhDs in physics. Finance professors Jacob Sagi and Greg Brown studied physics before they embarked their academic careers – Brown worked for the Board of Governors of the Federal Reserve System.

Many analytical models used in financial engineering are derived from mathematical models used in physics, including the Nobel-Prize-winning Black-Scholes options pricing model, which is based on the heat equation in physics. Economists Fischer Black and Myron Scholes both studied physics prior to making their mark in finance.

The program was specifically designed as a BA rather than a BS to give students the freedom to choose the classes that interested them, Henning said. “It gives you flexibility. You can fill up your BA with as much math and science as you want or you can fill it up with business classes. This makes it appealing to a wide audience.”

The major is available for current and incoming students with no separate application. Henning and Arapoglou encourage students interested in combining math and problem solving with business to consider the degree.

“If you’re a student who likes to do quantitative problems and you like business, this is the degree for you,” said Henning.

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Why Study for a Mathematical Finance PhD?

I was emailed by a reader recently asking about mathematical finance PhD programs and the benefits of such a course. If you are considering gaining a PhD in mathematical finance, this article will be of interest to you.

If you are currently near the end of your undergraduate studies or are returning to study after some time in industry, you might consider starting a PhD in mathematical finance. This is an alternative to undertaking a Masters in Financial Engineering (MFE), which is another route into a quantitative role. This article will discuss exactly what you will be studying and what you are likely to get out of a PhD program. Clearly there will be differences between studying in the US, UK or elsewhere. I personally went to grad school in the UK, but I will discuss both UK and US programs.

Mathematical finance PhD programs exist because the techniques within the derivatives pricing industry are becoming more mathematical and rigourous with each passing year. In order to develop new exotic derivatives instruments, as well as price and hedge them, the financial industry has turned to academia. This has lead to the formation of mathematical finance research groups - academics who specialise in derivatives pricing models, risk analysis and quantitative trading.

Graduate school, for those unfamiliar with it, is a very different experience to undergraduate. The idea of grad school is to teach you how to effectively research a concept without any guidance and use that research as a basis for developing your own models. Grad school really consists of a transition from the "spoon fed" undergraduate lecture system to independent study and presentation of material. The taught component of grad school is smaller and the thesis component is far larger. In the US, it is not uncommon to have two years of taught courses before embarking on a thesis (and thus finding a supervisor). In the UK, a PhD program is generally 3-4 years long with either a year of taught courses, or none, and then 3 years of research.

A good mathematical finance PhD program will make extensive use of your undergraduate knowledge and put you through graduate level courses on stochastic analysis, statistical theory and financial engineering. It will also allow you to take courses on general finance, particularly on corporate finance and derivative securities. When you finish the program you will have gained a broad knowledge in most areas of mathematical finance, while specialising in one particular area for your thesis. This "broad and deep" level of knowledge is the hallmark of a good PhD program.

Mathematical Finance research groups study a wide variety of topics. Some of the more common areas include:

• Derivative Securities Pricing/Hedging: The technical term for this is "financial engineering", as "quantitative analysis" now encompasses a wide variety of financial areas. Some of the latest research topics include sophisticated models of options including stochastic volatility models, jump-diffusion models, asymptotic methods as well as investment strategies.
• Stochastic Calculus/Analysis: This is more of a theoretical area, where the basic motivation stems from the need to solve stochastic differential equations. Research groups may look at path-dependent PDEs, functional Ito calculus, measure theory and probability theory.
• Fixed Income Modeling: Research in this area centres on effectively modelling interest rates - such as multi-factor models, multi-curve term structure models as well as interest rate derivatives such as swaptions.
• Numerical Methods: Although not always strictly related to mathematical finance, there is a vast amount of university research carried out to try and develop more effective means of solving equations numerically (i.e. on the computer!). Recent developments include GPU-based Monte Carlo solvers, more efficient matrix solvers as well as Finite Differences on GPUs. These groups will almost certainly possess substantial programming expertise.
• Market Microstructure/High-Frequency Modeling: This type of research is extremely applied and highly valued by funds engaged in this activity. You will find many academics consulting, if not contracting, for specialised hedge funds. Research areas include creating limit order market models, high frequency data statistical modelling, market stability analysis and volatility analysis.
• Credit Risk: Credit risk was a huge concern in the 2007-2008 financial crisis and many research groups are engaged in determining such "counterparty risks". Credit derivatives are still a huge business and so a lot of research goes into collateralisation of securities as well as pricing of exotic credit derivatives.

These are only a fraction of the total areas that are studied within mathematical finance. The best place to find out more about research topics is to visit the websites of all the universities which have a mathematical finance research group, which is typically found within the mathematics, statistics or economics faculty.

The benefits of undertaking a PhD program are numerous:

• Employment Prospects: A PhD program sets you apart from candidates who only possess an undergraduate or Masters level ability. By successfully defending a thesis, you have shown independence in your research ability, a skill highly valued by numerate employers. Many funds (and to a lesser extent, banks) will only hire PhD level candidates for their mathematical finance positions, so in a pragmatic sense it is often a necessary "rubber stamp". In investment banks, this is not the case so much anymore, as programming ability is generally prized more. However, in funds, it is still often a requirement. Upon being hired you will likely be at "associate" level rather than "analyst" level, which is common of undergraduates. Your starting salary will reflect this too.
• Knowledge: You will spend a large amount of time becoming familiar with many aspects of mathematical finance and derivatives theory. This will give you a holistic view into the industry and a more transferable skill set than an undergraduate degree as you progress up the career ladder. In addition, you will have a great deal of time to learn how to program models effectively (without the day-to-day pressure to get something implemented any way possible!), so by the time you're employed, you will be "ahead of the game" and will know best practices. This aspect is down to you, however!
• Intellectual Prospects: You are far more likely to gain a position at a fund after completing a PhD than without one. Funds are often better environments to work in. There is usually less stress and a more relaxed "collegiate" environment. Compare this to working on a noisy trading floor, where research might be harder to carry out and be perceived as less important.

I would highly recommend a mathematical finance PhD, so long as you are extremely sure that a career in quantitative finance is for you. If you are still unsure of your potential career options, then a more general mathematics, physics or engineering PhD might be a better choice.

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• Career Advice

PhD in physics, first job as a quant

• Thread starter Thread starter feynmanjr
• Start date Start date 3/30/16

I am a PhD student in physics in US and I am going to graduate by end of this summer. Recently, I have decided to shift my career toward a quantitative job instead of going to a postdoc. I have read some basic books in finance and I am good at mathematics and I worked with C++ / python and mathematical softwares (Mathematica and Matlab), but it was mostly through self study in school. I want to know what my chance is for applying for getting a job as a quantitative analyst or developer (or similar jobs). Do I have to invest in getting a MFE or some similar degree before thinking of applying in that area? Also, since I am going to graduate in less than a year, is there anything that I can do to help my condition (finding headhunters or studying some special topics)? -thanks  

IntoDarkness

wow, quitting academia but still call urself feynman jr...  

Since you have a very short time frame, a useful materials for a PhD quant wannabe is: A Practical Guide To Quantitative Finance Interviews by Xinfeng Zhou (Author) 150 Most Frequently Asked Questions on Quant Interviews by Dan Stefanica Rados Radoicic (Author), Tai-Ho Wang (Author) Quant Job Interview Questions and Answers (Second Edition) by Mark Joshi (Author), Nicholas Denson (Author), Andrew Downes (Author)  

Thanks @rnavarro . I will definitely Start reading these books. Also, do you have any suggestions how to find headhunters or should I upload my CV directly in any online job ad that I see? @IntoDarkness : Feynman Jr was my nickname in more than a few years and I usually use it for my username.  

Networking is the key. You can also start with headhunter site such as selbyjennings, huxley, and efinancialcareers. Don't forget to network via LinkedIn. It is very tough to break into Quant Finance but it is worth it. Good Luck!  

ShowMeTheLight

The main thing I would stress is not to get too cocky regarding your math, C++ or quantitative finance skills. The top hedge funds will test all of these like nothing you ever experienced in grad school. They want to make sure you know everything in C++ inside out, upside down and backward. Honestly, after a Physics PhD, it might still take you 6-12 months to prepare for the interviews at the TOP places. I would pick up all of the recommended books, plus Elements of Programming Interviews, and work through the problems carefully.  

The suggestion I gave was more of a quick notice preparation for those graduating few months from now. I know several people taking their PhD in Physics either took for MS Statistics degree or get some Math Finance courses ( C++ programming included) the year before they graduate. In addition, experience with large data sets (such as using Machine Learning, Time Series, Market Microstructure) is desirable.  

I agree with both of you ( @ShowMeTheLight and @rnavarro ). Unfortunately, I have not prepared myself enough during my phd for such a job. If I had time, I would also consider a couple of internship in different places in previous summers). I know that probably I will not get an offer from any of top hedge funds/Investment banks, but I want to start my first job at this point. I know C++ /math/statistics, but I do not know them enough to pass any kind of tests. I have learnt them (mostly coding and statistics) by myself, so I emphasized on the parts that I needed. My advantage is that I am relatively good in all of them and I am willing to learn whatever I need to know. @ShowMeTheLight : I would not mind spending some time to learn these, but I am mostly concerned about not having any experience. Do you think I should forget about applying until I completed all different books that are in that area?  

feynmanjr said: I @ShowMeTheLight : I would not mind spending some time to learn these, but I am mostly concerned about not having any experience. Do you think I should forget about applying until I completed all different books that are in that area? Click to expand...

ShowMeTheLight said: Yes, definitiely do not apply until you have put in 6-12 months of study in C++ especially, as well as probability, statistics and even basic machine learning and databases (SQL, Hadoop, HBase). If you apply too early, you won't make it past the 30 minute phone interview. They will just cut you when you say you don't know how to release a unique_ptr or how to write a copy constructor, or overload the () operator. Click to expand...

@ShowMeTheLight : I am not going to apply if it says "strong C++ skills", since all my knowledge is based on my personal learning. However, I can write a program and I can easily find my way, if I face a problem that I have not seen before (probably not useful for interview). Also, my main strength is in math (mostly statistical mechanics and PDE/stochastic calculus) and I am hoping to get a job that requires both of these skill at the same time. I will follow your advice and try to become a master in those areas. @pingu : I agree that even if I fail at interview, it can help me for my next ones. I do not think I can be qualified as a person who works with extremely large databases.  

Finder of biased coins.

pingu said: I have a different opinion. Apply to all jobs and use it as experience or training. Think like you are practicing a sport and getting better at it. Click to expand...

feynmanjr said: @ShowMeTheLight : I am not going to apply if it says "strong C++ skills", since all my knowledge is based on my personal learning. However, I can write a program and I can easily find my way, if I face a problem that I have not seen before (probably not useful for interview). @pingu : I agree that even if I fail at interview, it can help me for my next ones. I do not think I can be qualified as a person who works with extremely large databases. Click to expand...

@ShowMeTheLight : I agree. I am not planning to act line I am a genius, so they should hire me. As you said, most of the mathematical modelings happened when Black-Scholes model came out(70s-90s). I can write any program in C++ and I know some other languages like python . The point is I have learned all of these by self study, so I may not know exactly what unique pointer or other expressions means. I have also worked with SQL databases when I was designing software and web pages a while ago, but it was also self study (I can write a code to do the job, but I may not know the official name for anything that I use). My plan is to do math and computer programming and I doubt if I can compete with someone whose major is computer science. As a person with no experience, I do not think I can get a job at somewhere like Goldman Sachs, so following your advice, I am going to learn more expressions in C++ and then start applying. Since I am going to graduate this summer, I am willing to get any job I can get (or maybe a part time job first). If I do not get a job, I may try to go to a postdoc and find a job after that.  

Daniel Duffy

C++ author, trainer.

My advantage is that I am relatively good in all of them. How did you determine this?  

feynmanjr said: @ShowMeTheLight : I can write any program in C++ and I know some other languages like python . Click to expand...

Here is a list of commonly asked C++ interview questions. This should keep you busy for sometime and you will not be blindsided in the interview: C++ Interview Questions and Answers - Test & Download http://www.cs.ucr.edu/~lyan/c++interviewquestions.pdf Google You also need to go through the book "Cracking the Coding Interview" Get it from Amazon.com  

I see. I put a bad wording by saying I can write anything. I definitely cannot. In fact I do not know if I know enough for a specific position or not. I meant I can write a program to find noise in a 1-milion line code, or I can sole a partial differential equation (or some simulations). I am definitely not an advanced programmer. @ShowMeTheLight : I understand that you are frustrated that physicists who say things like that. I did not want to look like a very arrogant person who think he knows anything. I am pretty sure that most of the things that I learned in school is not going to be useful in finance. @Daniel Duffy : I did not mean I am pretty advanced in C++ . I will definitely be more careful in future in my statements. In fact, that is exactly why I want to go to industry, so I can get a better understanding of how to deal with a real problem. @TraderJoe : thank you for your suggestions. I will read them before going to an interview.  

feynmanjr said: @ShowMeTheLight : I understand that you are frustrated that physicists who say things like that. I did not want to look like a very arrogant person who think he knows anything. Click to expand...

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College graduates who have some background in mathematics and statistics and want graduate school preparation in the mathematics and statistics of finance and financial engineering, or are interested in a career change, can follow a structured curriculum developed with the Departments of  Mathematics  and  Statistics .

The Quantitative Studies for Finance program includes:

Calculus courses (2 courses), calculus ii in person.

Methods of integration, applications of the integral, Taylor's theorem, infinite series.

Course Number

Prerequisite, section/call number, calculus iii in person.

Vectors in dimensions 2 and 3, complex numbers and the complex exponential function with applications to differential equations, Cramer's rule, vector-valued functions of one variable, scalar-valued functions of several variables, partial derivatives, gradients, surfaces, optimization, the method of Lagrange multipliers. 

Also recommended but not required:

Calculus iv in person.

Multiple integrals, Taylor's formula in several variables, line and surface integrals, calculus of vector fields, Fourier series.

Advanced Mathematics Courses (Choose 4 Courses)

Linear algebra in person.

Matrices, vector spaces, linear transformations, eigenvalues and eigenvectors, canonical forms, applications. 

Ordinary Differential Equations In Person

Special differential equations of order one. Linear differential equations with constant and variable coefficients. Systems of such equations. Transform and series solution techniques. Emphasis on applications.

Partial Differential Equations In Person

Topics of linear and non-linear partial differential equations of second order, with particular emphasis to Elliptic and Parabolic equations and modern approaches.

Analysis and Optimization In Person

Mathematical methods for economics. Quadratic forms, Hessian, implicit functions. Convex sets, convex functions. Optimization, constrained optimization, Kuhn-Tucker conditions. Elements of the calculus of variations and optimal control. 

Introduction to Modern Analysis I In Person

The second term of this course may not be taken without the first. Real numbers, metric spaces, elements of general topology. Continuous and differential functions. Implicit functions. Integration; change of variables. Function spaces.

Statistics Courses (Choose 1 Course)

Introduction to probability and statistics in person.

Introduction to Probability and Statistics

Probability Theory In Person

Probability is the foundation on which statistics is built. The purposes of this course are 1) to introduce you to probability and 2) prepare you to take a sequel course on statistical inference (Statistics 4204,5204).

We shall begin by covering the basic axioms of probability and using these in some simple settings. Then we will take up the idea of independence and conditional probability. Following we shall consider random variables, and the properties first of univariate discrete and continuous distributions. When we look at two or more variables, additional considerations arise, such as the relationship between the variables|conditional distributions and marginal distributions. Following these basics, we will then take up some ways of summarizing distributions, e.g., expectations and variances and, in summarizing relationships among variables, covariance and correlation. We then take up some of the more important distributions in statistics. In particular, for the discrete case, we will study the Bernoulli and binomial distributions and the generalization to the multinomial distribution, also the Poisson distribution. For continuous distributions, we take up the univariate and bivariate normal, the Gamma and the Beta distribution. In statistical applications, sums of independent random variables (for example, a sample average is such a sum, divided by sample size) are extremely important and characterizing the properties of these in large samples justifies many of the ways in which we make inferences in statistics. Thus, we take up the properties of these sums in large samples, focusing on stating laws of large numbers and also a simple central limit theorem.

Statistical Inference In Person

The aim of the course is to describe the two aspects of statistics {estimation and inference {in some details. The topics will include maximum likelihood estimation, Bayesian inference, confidence intervals, bootstrap methods, some nonparametric tests, statistical hypothesis testing, linear regression models, ANOVA, etc.

Computer Science Course

Programming in java in person.

This course is a foundation course for learning software programming using the Java language. The course will introduce the student to programming concepts, programming techniques, and other software development fundamentals. Students will learn the concepts of Object Oriented programming using Java. The course will present an extensive coverage of the Java programming language including how to write, compile and run Java applications.

The purpose of this course is to learn programming concept and Object Oriented fundamentals using Java. Students will receive a solid understanding of the Java language syntax and semantics including Java program structure, data types, program control flow, defining classes and instantiating objects, information hiding and encapsulations, inheritance, exception handling, input/output data streams, memory management, Applets and Swing window components.

Students are required to maintain an overall minimum GPA of 3.0 (B). Every course creditable toward the certificate must be taken for a letter grade. Courses with a grade of P or below a C will not count toward the completion of the certificate.

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Are Physics PhD going into a quant still a thing?

I'm a junior major in Physics and Minor in (Maths and CS). I'm interested in Physics and would like to pursue a PhD in it but I'm not sure if I want Academia to be my only option. I heard 20 years back, Physics/Math PhDs were a big thing for good quant roles. I'm already in a pretty reputed uni and can get into a top 10 uni for PhD. But I heard it's not that simple to get into quantitative researcher (and other quant positions) for just Physics PhDs anymore.

So my question is if I'm a Physics PhD from a top uni with programming experience (I've taken lot of CS courses) can I still get into a good quantitative researcher role (not some role which has mostly undergrads anyway). Or should I just take the safer option and do MS in CS/MFE/Data Science?

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Senior Quantitative Finance Analyst

Job Description:

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. Responsible Growth is how we run our company and how we deliver for our clients, teammates, communities and shareholders every day.

One of the keys to driving Responsible Growth is being a great place to work for our teammates around the world. We’re devoted to being a diverse and inclusive workplace for everyone. We hire individuals with a broad range of backgrounds and experiences and invest heavily in our teammates and their families by offering competitive benefits to support their physical, emotional, and financial well-being.

Bank of America believes both in the importance of working together and offering flexibility to our employees. We use a multi-faceted approach for flexibility, depending on the various roles in our organization.

Working at Bank of America will give you a great career with opportunities to learn, grow and make an impact, along with the power to make a difference. Join us!

Job Description: Enterprise Model Risk Management seeks a Senior Quantitative Finance Analyst to conduct independent review and testing of complex models based on artificial intelligence (AI) and machine learning (ML) techniques including natural language processing (NLP). These are high profile modelling areas in the bank, with continual senior management and regulatory focus. The Senior Quantitative Finance Analyst will be a key leader in Model Risk Management. The role is especially designed to provide both thought leadership and hands-on expertise in methodology, techniques, and processes in applying AI/ML models to manage the bank's large footprint in various line of businesses. The AI/ML model inventory cover areas of natural language processing, deep learning and ensemble learning. The model inventory is rapidly increasing especially with generative AI and large language models (LLMs). The position will be responsible for: • Performing model review activities including but not limited to independent model validation/challenge, annual model review, ongoing monitoring report review, required action item review, and peer review. • Conducting governance activities such as model identification, model approval and breach remediation reviews to manage model risk. • Providing hands-on leadership for projects pertaining to statistical modeling and AI/ML approaches; and providing methodological, analytical, and technical support to effectively challenge and influence the strategic direction and tactical approaches of these projects. • Communicating and working directly with relevant modeling teams and their corresponding Front Line Units; and if needed, communicating, and interacting with the third line of defense (e.g., internal audit) as well as external regulators. • Writing technical reports for distribution and presentation to model developers, senior management, audit, and banking regulators. • Acts as a senior leader and Subject Matter Expert (SME) to help management’s decision making and guide junior team members.

Minimum Education Requirement: Master’s degree in related field or equivalent work experience

Required Qualifications & Skills: • PhD or Masters in a quantitative field such as Mathematics, Physics, Engineering, Computer Science or Statistics. • Solid 3+ years of work experience at another financial service or technology firm in AI/ML, quantitative research, model development, and/or model validation. • Expert of AI/ML methodologies including NLP, e.g., methods in NLP used for text-to-text, speech-to-text/text-to-speech tasks. Familiarity with techniques in generative AI and LLMs. • Proficient in Python, and ideally experienced in AI/ML packages, e.g., scikit-learn, TensorFlow, XGBoost, PyTorch, spaCy. • Domain knowledge such as retail banking, technology, operations, financial market is a plus, including the use of analytics to perform automatic speech recognition, fraud, cyber risk monitoring, retail customer digital experience, ATM operations, and/or cash management. • Strong knowledge of financial, mathematical, and statistical theories and practices, and a deep understanding of modeling process, model performance measures, and model risk of AI/ML models. • Understanding of additional risks of AI/ML models in areas such as privacy and information security will be a plus. • Strong written and verbal communication skills and collaboration skills. This role involves communicating with various groups within the firm including stakeholders with non-technical background. • Critical thinking and ability to independently and proactively identify/suggest/resolve issues. • Motivated to continuously research and share state-of-the-art technologies, methodologies, and applications in the AI/ML field.

Hours Per Week:

Weekly Schedule:

Referral Bonus Amount:

Hours Per Week: 

Learn more about this role

JR-24034310

Manages People: No

Travel: Yes, 5% of the time

Jersey City pay range:

$125,000 - $210,000 annualized salary, offers to be determined based on experience, education and skill set.

Discretionary incentive eligible

This role is eligible to participate in the annual discretionary plan. Employees are eligible for an annual discretionary award based on their overall individual performance results and behaviors, the performance and contributions of their line of business and/or group; and the overall success of the Company.

This role is currently benefits eligible . We provide industry-leading benefits, access to paid time off, resources and support to our employees so they can make a genuine impact and contribute to the sustainable growth of our business and the communities we serve.

New York pay range:

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Quantitative Finance Graduate Certificate

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• Program of Interest * Choose a Program AAS Health Information Systems Application Software Support Specialist Certificate Artificial Intelligence in Business Graduate Certificate Associate of Applied Business in Accounting Technology Associate of Applied Business in Business Applications Technology Associate of Applied Business in Business Management Technology Associate of Applied Business in Financial Management Technology Associate of Applied Business in Healthcare Management Technology Associate of Applied Business in Information Technology Associate of Applied Business in Law & Paralegal Studies Associate of Applied Business in Marketing Management Technology Associate of Applied Business in Supply Chain Management Technology Associate of Applied Science - Cancer Registry Management Associate of Applied Science in Early Childhood Care & Education Associate of Arts in Business Administration (Pre) Associate of Arts in Communication Associate of Individualized Study in Interdisciplinary Studies Associate of Science in Public Health Autism Spectrum Disorders Graduate Certificate Bachelor of Arts in Communication Bachelor of Arts in Organizational Leadership Bachelor of Arts in Professional Studies Bachelor of Business Administration - Finance Bachelor of Business Administration - Marketing Bachelor of Health Information Management Bachelor of Science in Criminal Justice Bachelor of Science in Early Childhood Education Bachelor of Science in Health Sciences Bachelor of Science in Health Sciences - Pre-Occupational Therapy Bachelor of Science in Law & Paralegal Studies Bachelor of Science in Medical Laboratory Science Bachelor of Science in Public Health Bachelor of Science in Respiratory Therapy (RRT to BSRT) Bachelor of Science in Substance Abuse Counseling Bachelor of Social Work Bachelor of Technical and Applied Studies in Applied Administration Bachelor of Technical and Applied Studies in Applied Administration: Healthcare Leadership Business Foundations Graduate Certificate Certificate Blended Online Learning and Teaching (BOLT) Certificate in Insurance & Finance Management Certificate in Substance Abuse Counseling Clinical Trials Design and Research Graduate Certificate Corporate Taxation Graduate Certificate Cosmetic Science Graduate Certificate Data Analytics Graduate Certificate Data-Driven Cybersecurity Certificate Digital Marketing Graduate Certificate Doctorate of Clinical Laboratory Sciences Gifted Education Endorsement Global Regulatory Affairs in Drug Development Graduate Certificate Graduate Certificate in Analysis of Criminal Behavior Graduate Certificate in Correctional Rehabilitation Graduate Certificate in Crime Analysis and Prevention Health Care Administration Graduate Certificate Health Care Finance Graduate Certificate Health Care Policy & Regulation Graduate Certificate Hygienic Manufacturing of Cosmetic Products Graduate Certificate Individual Taxation Graduate Certificate Information Technology Certificate Information Technology Graduate Certificate Investment Management Graduate Certificate MEd Curriculum and Instruction MEd Educational Leadership MEd Instructional Design and Technology MEd Literacy and Second Language Studies MEd Special Education MEng in Electrical Engineering MEng in Mechanical Engineering MEng in Robotics & Intelligent Autonomous Systems MS Pharm Sci: Drug Development MS Pharm Sci: Health Outcomes & Pharmacoeconomics MS Pharm Sci: Healthcare Operational Excellence MS Pharm Sci: Pharmacogenomics and Personalized Healthcare MSN Adult-Gerontology Primary Care Nurse Practitioner MSN Family Nurse Practitioner MSN Nurse-Midwifery MSN Nursing Education MSN Women's Health Nurse Practitioner Marketing Graduate Certificate Master of Arts in Teaching - Special Education Master of Business Administration - MBA Master of Education in Foundations in Behavior Analysis Master of Health Administration (Executive) - MHA Master of Health Informatics - MHI Master of Legal Studies Master of Science in Business Analytics Master of Science in Community Health & Prevention Science Master of Science in Cosmetic Science Master of Science in Criminal Justice Master of Science in Finance Master of Science in Geographic Information Systems Master of Science in Health and Wellness Management Master of Science in Information Systems Master of Science in Information Technology Master of Science in Marketing Master of Science in Medical Laboratory Science Leadership Master of Science in Pharmacy Leadership Master of Science in Respiratory Therapy Master of Science in Sport Administration Master of Science in Taxation Medical Coder Certificate Online Cancer Registry Certification Pharmacogenomics and Personalized Healthcare Graduate Certificate Pharmacy Leadership Graduate Certificate Physician Practice Management Certificate Post-Associate Certificate in Law and Paralegal Studies Post-Baccalaureate Certificate in Law & Paralegal Studies Post-Master's Family Nurse Practitioner Post-Master's Psychiatric-Mental Health Nurse Practitioner PreK-12 Reading Instruction Endorsement Principal Licensure Endorsement RN to BSN Online Revenue Cycle Management Certificate Special Education Leadership Graduate Certificate Teaching English to Speakers of Other Languages Endorsement Transition to Work Endorsement Undecided
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• Desired Start Date * Please Select an Option Fall 2024 Spring 2025 Summer 2025 Fall 2025
• *This program is now enrolling for Summer 2025.
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• Program Overview
• Academic Calendar
• Admission Requirements

Harness the Power of Finance and Technology

The financial landscape is evolving rapidly, driven by groundbreaking technological advancements. As quantitative tools become more sophisticated, the industry is increasingly turning to technology to inform strategic decision-making.

Our certificate program equips you with the essential skills to bridge the gap between finance and technology. Through a carefully curated curriculum, you’ll learn to:

• Master quantitative tools: Gain proficiency in advanced analytical techniques.
• Leverage technological solutions: Explore cutting-edge applications that drive innovation in finance.
• Apply your knowledge in practice: Collaborate with peers in real-world scenarios to solidify your understanding.

By completing this certificate, you’ll position yourself as a sought-after professional who can effectively harness the power of finance and technology to drive success in today’s competitive market.

• Academic Calendar Start dates, deadlines, and more
• Admission Requirements Prerequisites and requirements
• Curriculum Course offerings and samples
• Tuition Tuition calculator and cost breakdown

Most colleges allow you to order copies of transcripts online, by mail, or in person via the office of admissions or registrar’s office. You’ll complete a transcript request form, you may pay a small fee, and have to provide proof of identification.

If you don’t know where to start when requesting transcripts, usually, the easiest way to locate your previous college transcripts is to conduct an online search for “(your previous school name here) transcript request.”

No. You must choose one program to apply for in any given semester.

Yes! You can earn both an MS in Finance and an MBA online!

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