Abstract
It is always my firm belief that an ambitious finance-major student should master at least one computer language. This is especially true for students from quantitative-finance, MSF, business analytics or financial engineering programs. Among many good languages, R and Python are the two best ones. Based on my 6-year experience of teaching R to finance students at 3 schools, the following 7 factors are critical for designing and teaching such a course: strong motivation, a good textbook, a hands-on environment, data intensive, a challenging term project, tons of supporting R data sets and an easy way to upload those R data sets.
Highlights
Nowadays, we are overwhelmed by large amounts of information (e.g., see Shi et al (2017), Fang and Zhang (2016)), the catchphrase being “big data.” defining it is still controversial, since many explanations are available
R/Python, on the other hand are better options for start-ups and Conclusions This brief paper shows potential finance instructors how to design a course for teaching finance-major students a programming language such as R and apply it to finance
For a finance major student, programming skills will become a necessity in the foreseeable future
Summary
We are overwhelmed by large amounts of information (e.g., see Shi et al (2017), Fang and Zhang (2016)), the catchphrase being “big data.” defining it is still controversial, since many explanations are available. We are overwhelmed by large amounts of information (e.g., see Shi et al (2017), Fang and Zhang (2016)), the catchphrase being “big data.”. College students should have one or two memory sticks, each with a capacity of 4 GB, and the simple answer is that if the student can generate and process 4 GB of data, he/she has the ability of dealing with “big data.” Obviously, this is not an accurate definition. It is a start and a practical one at that.
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