How many programming languages one needs to know to be an effective analyst in the data science profession? The answers to this question depend on whom that you asked?  However, there is one general rule to be competitive in any profession.  The more languages that one knows, the more competitive she or he will be, right? Learning programming language not only depends on one’s talent, passion, willingness to learn new stuff/programming language, and how much resources that one has, but also to the organization where the analyst is working and the ability to see what is coming, the trends.  Some institutions take the short-cut for deploying canned analytics software, with it unique limitation.

Organizations may have their own legacy system where heavy investments have been done in the past. This situation creates a unique condition where an analyst with only expert in one programming language is the best fit for the position.  The Association thinks that at least one needs to master and being an expert in one language in his work place—knowing it very well and in-depth will make she or he the best asset for the organization.  However, to be able to stay competitive she or he needs to add her or his core competent.  With this in mind, the Association has added several sources such as C++, R or Python and  lists of canned analytics software in its site to help members to be more competitive in the job market.

Nowadays, be able to code and to share the statistical analysis results which may impact the institution’s bottom line are not the only core competent that one needs to have.  It is imperative to understand other aspects such as the industry, the economic condition, the competitors, and output and input markets in the industry where he or she is in.  For example, what will happen to the company when the DOW/stock market dips due to geo-politics?  This interruption may require the analyst to be able capture it and stochastically rerun different scenarios.