The banking world is slowly getting to grips with the idea that data scientists are a key element to fully understanding their fields and becoming the best in the business. If you’re looking for a job as a data scientist in the financial sphere, here’s the basics of what you need to know.
There are two parts to any analysis done by a data scientist. The first is data analysis, which is a bit ‘does what it says on the tin’ – you analyse data and then give advice about your findings in a financial sense.
The second part is predictive analysis, which is more to do with forecasting the markets due to trends and history, correlating things that have happened in the past with things that are happening now.
If you’re looking at this industry as a profession, you need to have a very specific skillset, which includes programming languages such as C++, Java and Python and the ability to compile databases from sets of data which aren’t necessarily related.
On top of the computer stuff though, you’ll need to be good with statistical analysis, be able to model effectively and be comfortable with complex calculus.
Once you come out of Uni, there are very few people who have all the tools needed to be financial data scientists straight away. There are however, plenty of courses who will equip appropriate candidates with the skills they will need to succeed in the industry, and these are often the key to getting your foot in the door.
As an expanding and relatively new profession, it’s a good time to be considering financial data science as a career if you feel you have the knowledge and capability to do so.
For years I have studied American finance regulations. All the information in this blog is sourced from official or contrasted sources from reliable sites.
Salesforce Certified SALES & SERVICE Cloud Consultant in February 2020, Salesforce Certified Administrator (ADM-201), and Master degree in “Business Analytics & Big Data Strategy” with more than 13 years of experience in IT consulting.