Modern Investment Finance is hugely dependent on the implementations of the theories and techniques of financial engineering. Financial Engineering, or Quantitative Finance as it is alternately known, is a multidisciplinary field involving the application of theories from financial economics, physics, mathematics, probability, statistics, operations research and econometrics using the methods and tools of engineering and the practice of computer programming to solve the problems of Investment Finance.

Generally the language of choice for Quant implementations traditionally has been C++ along with tools like Matlab, Mathematica, Stata, etc. However, of late the Python language has become more popular.

Financial Engineering has emerged as a very prospective career prospect for people with a strong mathematical background like those coming from engineering, mathematics, statistics, physics or econometrics background. The best of the global financial institutions like Investment Banks, Hedge Funds, etc. hire people having strong quantitative skills for “Quant” jobs. This is also a very rewarding and exciting career option for such people as there is ample scope for applying their numerical and creative skills to design new things, be it devising new investment strategies or be it structuring new financial instruments or be it finding methods to value them. They are continuously competing with their peers and some of the best minds in the market and have to out-perform them to generate superior returns, which is intellectually a very challenging work, and this makes it all the more thrilling.

Modern Investment Management has become very much mathematical and statistical in nature, it is now much more of science than arts, specially where investments in complex financial instruments and complex trading/investment strategies are concerned. That is the reason that high-end investment firms that invest in derivatives are opting for people who have strong quantitative skills for structuring or valuation of complex financial instruments and for devicing superior investment strategies.

This has opened up very exciting and rewarding career opportunities in the field of Quantitative Investment Management for candidates who come with academic background in engineering, mathematics, and other numerical specializations. Needless to say, that apart from the stimulating intectual challenges that careers in this field offer to the mathematically talented individuals, the compensations are quite handsome indeed.

The course prepares candidates for careers as quantitative investment managers or quantitative analysts with financial institutions like investment banks, hedge funds, private equity firms, large broking houses, investment research and analytics firms, etc.

Candidates having a strong numerical background have a very bright chance of making a very rewarding career in this field with the largest of investment banks and other financial institutions. Salaries of quantitative analysts vary depending on their experience and background. In India presently, salaries for this profile may range from 8 Lacs p.a. for freshers to 30 Lacs p.a. for candidates with a few years of experience.

Students completing Certificate Program in Machine Learning Finance online courses will get placement assistance subject to the fulfilment of applicable conditions. Students completing this Machine Learning for Finance course will get placement assistance subject to the fulfilment of applicable conditions.