Gaining Experience as Financial Data Scientist
Obtaining hands-on experience is the most important thing and implies that financial data scientists should get colloquial in their craft and demonstrate that they can cope with the real-world situations.
1. Internships
Lots of companies such as banks, consultancies and technology, have created internships aimed at the data science students. Learners of this field get to handle real world cases, data, tools and methods of financial analysis and modeling through internships.
Via internships students can get practical experience, can communicate with the professionals from the financial sector and finally can look issues and possibilities in the financial market from different sides.
2. Personal Projects
With personal projects, students who want to become financial data scientists, have the opportunity to put their knowledge and creative thinking into action to solve real-world data problems that they are interested in. As in here, working on personal projects lets people go deep into the field of finance, create innovative projects, as well as compile a portfolio of their accomplishments to showcase them to the possible employers.
3. Kaggle Competitions
Contesting against others in this Kaggle competitions will be a great incubator for learners, as they will be able to see how the master does it, work with peers and see how their skills stack up against others. Kaggle supports a lot of competitions on finance spanning from forecasting stock prices over credit risk assessments and algorithmic trading strategies as well.
Participating in Kaggle competitions allows individuals to utilize prominent data sets, run different resampling algorithms and then learn what is being done by the other winning teams.
Shortly, internships, and projects with Kaggle competitions significantly help financial data scientists to acquire practical skills, cumulate their accomplishments in the portfolio and stand out from their colleagues.
How to Become a Financial Data Scientist?
Knowing how to use advanced programming languages like Python and R is important. These languages help people work with data, make graphs, and do detailed statistical analysis, even using fancy techniques.
In financial data science, it’s helpful to like the finance world and be good with technology. This helps you understand the tricky parts of the financial market and find useful insights from lots of financial data.