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Data Analysis in Financial Market – Where to Begin?

  • Last Updated : 10 May, 2020
Geek Week

In recent years, technology has spurred innovation in the field of finance. Technology has become a major facet for any financial institution, offering strategic advantages as well as disadvantages. The availability of data has caused the processes of financial institutions to react in real-time. The ever-increasing speed and volume in stock trading have demanded reinforcement of faster, cleaner, and better-performing tools, which brings us to using programming languages such as R or Python.

Before moving to R or Python, let’s first define data analysis and its role in the financial market

Data analysis is a detailed examination of the given set of data. It can scrutinize and evaluate every aspect of the historical data when fed with suitable criteria, giving optimum results. Data analysis is reliable because it can eliminate the scope of human error, save time, and give an accurate outcome. In the financial domain, Data analysis can examine and identify paradigms, it also points out factors that influence customer behavior and purchasing decisions. These decisions can help businesses and companies make informed and important decisions about their product design, pricing strategy, distribution strategy, marketing, and promotion strategy.

When we talk about Data analysis tools, there are many tools like age-old Excel and the trending BI (Business Intelligence), but the roots begin at programming languages such as R and Python.

Why R or Python?

Both languages can be credited for being able to change the dynamics of the financial markets. They have a varied range of applications, beginning from data CMR (cleaning, modeling, and reduction) to hypothesis testing and modeling. While R is specifically designed for data analysis, it does not enjoy the same exclusivity in the case of Python. Data Analysis is just one of the many branches in Python. Although Python is catching up with R in the Data Analysis using AI (Artificial Intelligence) and Machine learning, R still maintains an edge in the field of statistics.

R vs Python – What to choose?

If you are a beginner, starting with Python would be easier for you. Python replicates many models of R. Both are easy to learn, and challenges can be tackled with online resources, however, choose to learn only one language at a time because learning both of them together can be confusing as they share many similar aspects.

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