Fintech guru Michael Zetser reveals the data science skills that will prove to be instrumental for the fintech sector going forward. Global payment systems and financial transactions can only advance with the help of data scientists and other data professionals, as most technologies that are changing the financial sector are data-centric. Cloud computing, blockchain and artificial intelligence are just some of the technologies that are being used to upend traditional finance and none of them can operate without data.
Therefore, anyone looking for a career in the fintech industry will have to have some data science course in hyderabad skills. But, which skills will be useful? Michael Zetser has revealed some of them below.
Machine learning
Data scientists can identify patterns in data sets and make useful predictions accordingly when they are well-versed in machine learning techniques. There is no doubt that risk factors can be reduced in the fintech industry with the help of machine learning.
Financial modeling
Decision making tools are used in every industry and this is no different for the fintech sector. According to Michael Zetser, financial modeling is one such tool that be extremely useful in the industry and this particular skill will be helpful for anyone who wants to make a mark in it. Essentially, it involves representing the past operations of a company in numbers and using it to forecast the future numbers. They can be used for making cost and profit estimates of potential projects to identify the most feasible ones.
Time series analysis
Time series data is commonly used by fintech firms, such as transaction volumes, interest rates and stock prices. Therefore, Michael Zetser suggests that data scientists who want to be part of the fintech industry should master time-series analysis techniques. These include Auto-regressive Integrated Moving Average (ARIMA), Generalized Auto-regressive Conditional Heterokedasticity (GARCH) and Vector auto-regressive (VAR).
Deep learning
There are a number of purposes for which deep learning techniques are leveraged by fintech companies these days. They are helpful in building predictive models that can assist with investment recommendations, fraud detection and credit risk. Thus, a data scientist who wants to advance in the world of fintech should master various deep learning algorithms, which include Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs).
Programming Languages: Python and R
Michael Zetser states that the core of data science programming is statistics, which means that learning and developing expertise in languages like R and Python is a must. To become part of the fintech industry, these programming languages should be mastered on a big data landscape, such as Oracle, Hadoop or SAP HANA. This empowers professionals in creating industry use-cases related to marketing, customer and workforce analytics.
Natural Language Processing (NLP)
NLP techniques are also handy for fintech firms when it comes to analyzing data on social media platforms, news articles as well as customer feedback. These can be useful for developing an in-depth understanding of customer behavior, market sentiment and trends. A data scientist in the fintech sector would need to be an expert in NLP techniques, which include entity recognition, text classification and sentiment analysis.
Cloud computing
Since he is a fintech expert, Michael Zetser is aware that during the course of their operations, fintech firms have to handle massive, not to mention complex, datasets. To manage this data, they need to have scalable computing infrastructure. Hence, cloud computing experience is one of the top data science skills that one should possess when they want to forge a career in the fintech industry. They need to be familiar with cloud computing platforms, such as Google Cloud Platform (GCP), Microsoft Azure and Amazon Web Services (AWS).
Data visualization
Not everyone can understand data-driven insights right away due to which it is also essential for a data scientist to be able to communicate these in a visually-effective manner. The findings should be presented in a manner that both non-technical and technical audiences are able to comprehend them.
This is where data scientists should have in-depth knowledge of the different data visualization tools comes in. These tools are designed to present insights in a way that can be easily understood by everyone. There are a number of such tools that exist Michael Zetser suggests that those like ggplot2, D3.js and Tableau can be the most effective in presenting data-driven insights in ways that are easily understandable.
With the right data science skills, it is possible for fintech companies and organizations to leverage the huge amounts of data to make better and well-informed decisions. Those who want to join the fintech bandwagon should focus on mastering one or several of these data science skills to establish a career in the field and take advantage of the opportunities that exist today and will grow in the future.
