It is common knowledge that roles in Quantitative Finance require expertise in several quantitative domains: mathematics, one of several programming languages (e.g. R or Python), statistics, and finance. As such, the question often arises in the minds of aspiring quantitative analysts: what is the best way to learn Quantitative Finance? Such a broad array of disciplines is not easily found in a silver bullet course teaching all the essentials in an easily accessible manner. As a result, the task of learning quantitative finance becomes slightly more challenging in piecing together the knowledge of the different disciplines.

It is important to emphasise at the start there is no single path to learning quantitative finance. Given the disciplines involved, it is inevitable some will be more intuitive to some students and therefore require less attention when studying. Having said that, the most logical starting place is a strong mathematics related degree to give a student a firm grounding in a quantitative mindset which will form the foundation for the rest of the learning journey. Several bachelors programs facilitate such a firm grounding which aren’t limited to going for a finance bachelors. Statistics, engineering, computer science, and to an extent economics all equip students (assuming a high quality course at a top university) with the quantitative grounding in addition to the specific contours of the academic discipline.

After such a grounding then the relevant skillset needs to be built in programming i.e. taking the time to learn programming languages relevant to finance, statistics (i.e. stochastic analysis, time-series analysis, probability theory etc), and ensuring a strong understanding of finance itself to guide the application of the quantities elements. Such a skillset is built after considerable exposure to financial models; their application, construction, and limitations. Such exposure takes time to build up and students often pursue a masters to help in this process or in some cases a doctorate to get more hands on exposure in the research design process end-to-end and then testing the hypothesis.

And while this is one way to go about building up exposure, often students from technical backgrounds realise their interest in quantitative finance during undergrad or even postgrad study. As such, in conjunction with their already technical field it is also common to undertake a professional qualifications to help learn quantitative finance given the academic position they are starting from i.e. strong technical foundations that need to be built upon. In this regard there are several options depending on where a student is at and how intense they want the process to be (a key consideration when balancing other commitments).