Young graduates with business studies as their background are entering into banking. In addition, the industry experts have been witnessing a movement from one profession to another in the millennials especially.
While there was a time when young graduates wanted to become a banking professional, however, things have changed and now the youth want to enter the field of data science.
It is not to say that the popularity of investment banking industry has dipped, it is just that data science has become equally popular if not more amid the youngsters.
Apparently there are lots of similarities between data scientist and a banking professional. Let’s have a look what are those?
- Both investment banking professional and data scientist are competent in various fields
- While a data scientist uses a blend of statistics, coding, and business acumen. They solve problems through data modeling and then presenting the results to decision makers. Investment bankers generate ideas for ‘investment’, using financial models to support those ideas and then pitch those ideas to their clients.
- In data science, the data is usually copyrighted and the tools with which data scientist extract information include python and other related packages for visualization, analysis, and machine learning. Whereas an investment banking professional uses paid data sources like Reuters, Capital IQ, and Bloomberg. And the tools include SAS, excel, and macros for automation.
- Both involve the process of extracting, cleaning, modeling, and analyzing data and then presenting the results to their respective audiences.
The investment banking industry is less technical as compared to data science industry. However, it is predicted that even data science industry will become business oriented in the coming years.
The investment banking certification will also smoothen your transition from one role to another despite the fact that both data scientist and investment banking industry have similarities yet differences.
Interestingly, for the business graduates with no knowledge of coding are easily assimilated into the role of a data scientist professional.
