The BFSI to Data Science Journey: How to Make the Leap?

Okay, now that we have briefly covered the key characteristics of the industry, let’s move on to the main focus: how can you, as a BFSI professional, make that transition? The shift from a BFSI professional to a data scientist often requires some formal training. The actions you need to take are as follows:

1. Figure out how you can apply data science in your own domain

If you fully intend to leave your days in banking and finance behind and transition into a new career, that might not be a good idea. Not only will you need to master a new skill like data science, but you’ll also need to integrate your new role into your current workload. It is usually much preferable to stick to your niche and use data science approaches within the parameters of what you are familiar with. Numerous industries, including finance, banking, and insurance, allow you to use your data science talents. If you switch to data science within the same industry, you’ll have a better understanding of the data and the industry, which can be a significant advantage in the hiring process.

Listed below are a few data science roles that correspond to your prior identity as a BFSI professional.

Data scientists in finance analyse large data sets to find ways to generate profit and improve investment processes. They work with investment teams and develop tools and dashboards to support their organization’s goals.

• When it comes to banking Data science and AI/machine learning help banks analyse customer data to detect fraudulent activity and improve record accuracy.

• Data science can be used in the insurance industry to forecast customer demand, create tailored offers, cluster customers for targeted sales, and calculate customer lifetime value

Listed above are just a few generic roles that a data scientist plays in the domain of BFSI, but don’t sell yourself short and think that’s all there is to it; there are a lot of ways you can incorporate data science into your finance and banking careers. Just remember this: wherever there are large sets of available data, there will also be a data science team to make accurate predictions based on that data.

2. Learn the necessary skills to build your arsenal.

It’s important to learn the skills necessary to succeed in data science. This includes programming skills as well. Few of the frontline languages that you will come across in data science are.

• Python
• R
• SQL
• Julia
• Java

If you are just starting out with zero experience with programming, then we recommend kicking your journey off with Python as your best friend. Not only does it have a faster learning curve compared to other languages because of its simplicity, it is also extensively used in the field of data science.

Once you’ve built a solid foundation in your languages, it’s time to equip your arsenal with some more powerful tools that are going to be very important in your journey. These include

• Data Visualization
• Machine Learning
• Big Data

These are a few of the core concepts that you will come across in your data science journey and that you need to master to be a professional.

3. Enhance Your Skills with a Data Science Certification

Now that you are familiar with the major concepts of data science, it is time for you to get a data science certification. Employers generally like to see some academic credentials to ensure you have the know-how to tackle a data science job, though it’s not always required; it will also boost your confidence while job hunting. There are many online and offline resources out there that can help you achieve this.

Few of them that you might want to check out are listed below:-

• 1stepGrow
• Coursera
• edX
• DataCamp

They provide a select number of the greatest training programmes and priceless certifications that may help you establish roots in the field of data science while also honing your abilities. These courses offer industry-recognized certificates, so holding one will give you a competitive advantage over others.

4. Gain Hands-on Experience through Data Science Projects

“Why did the data scientist go to the beach? To do some regression analysis on the waves!”

I hope that brought a smile to your face. While obtaining a certification can be a valuable step in your career as a data scientist, it’s important to remember that real-world experience and the ability to apply your knowledge to actual projects is just as, if not more, important. So don’t forget to get your hands dirty and work on some projects to really demonstrate your skills and build your confidence as a data science professional.

If you’re unsure of where to begin, visit 1stepGrow; they have a section devoted to data science projects that will help you become job-ready with their capstone projects.

5. Make a strong impression with a portfolio of your work

It’s time to put your skills on display now that you have all the tools you need to rule the area of data science. To do that, you must create a compelling CV or portfolio that will make you stand out. However, if you have followed the above instructions, there won’t be any issues, and you may add your credentials, experience, and projects to ensure that you acquire a position.

6. Lastly, but certainly not least: Prepare for an interview.

There is one more obstacle you must go past before you can land your ideal job, and while the knowledge and experience you have gained thus far will undoubtedly be valuable, here are some tips on how to get ready in the context of the BFSI domain.

• Review common BFSI-related data science topics which include fraud detection, risk analysis, customer segmentation, and churn prediction. 
• Practice working with BFSI data.
• Understand the business context.

Understanding how data science is used to support business decision-making in the BFSI industry is very crucial and be prepared to discuss how you would apply your skills to solve real-world problems in this setting.

• Prepare for technical questions.

As a data scientist, you can expect to be asked about your technical knowledge, which includes questions about programming, statistical analysis, and machine learning.

• Practice your communication skills