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Financial Database Management with R


In this post, we will be decoding terms: Financial Database Management and R before we begin with financial analysis using quantitative modeling in R. 


What is data?

If you ever google data (which by the way isn’t recommended by the writer of this post), you are going to be stumped by the numerous definitions, each varying slightly from one another. 

Don’t worry! We aren’t here to add another definition to this ever growing list.
The only thing that you need to keep in mind whenever you deal with ‘data’ is that it is a qualitative or quantitative fact which is collected and examined for the purpose of making informed decisions and then you are good to go.

Data has become the “it” word of this century and the credit goes to the….


Rise of Big Data

With the unprecedented rise in technology in the recent decades, the world has witnessed the emergence of Big Data. It refers to the massive amounts of data that is being collected about many aspects of the world and grows exponentially with time

To understand big data, you need to familiarise yourself with the 3Vs of the big data:

  1. Volume

  2. Velocity 

  3. Variety












Qualities that characterize big data

Rise of Big Data has helped us to answer questions which seemed to be impossible a few decades back. You can always visit GitHub to check out some amazing projects done by people all around the world in R and other programming languages.


What is Database and Database Management?

A database is a systematic collection of data.










An example of a database


Unfortunately, this is rarely how data is actually available. The data sets we commonly encounter are much messier which makes the job of extracting useful information into a herculean task!

Thus, with the rise of big data, we also witnessed a rise in messy data which has consequently made Database Management Softwares like MySQL and Oracle Database a popular source of organised data. Yahoo! Finance and Google Finance are good sources of free Financial databases.


Database system’s efficiency in storing big data is a bit questionable as Big Data is ever changing but database softwares has been developed to fill this gap.

What is R? 

R is both a programming language and an environment, focused mainly on statistical analysis and graphics. R can be downloaded for free from the Comprehensive R Archive Network or CRAN

Why should you use R?

1) Its popularity

R is quickly becoming the standard language for statistical analysis. This makes R a great language as more people join this community, the more powerful it becomes, and the better the support there is! 

2) Its cost


This one is pretty self-explanatory - every aspect of R is free to use. There is no cost barrier to using R!


3) Its extensive functionality

R is a very versatile language -  Not only can it be used for statistics and in graphing, but its use can be expanded to many different functions - from making Websites, maps to  analysing language, it has a lot to offer!


Getting Help

For beginners, one of the most overwhelming moments  can be when you type out a command and all you get are lines and lines of angry red text telling you that you did something wrong. But taking a second to check over your command for typos and then carefully reading the error message solves the problem in nearly all of the cases. 

However, if the problem still persists you can take help of forums like StackOverflow and CrossValidated which are frequently visited by pros and beginners alike. It can be a great source for you to learn R and other programming languages.


How to Start?

There are a number of short- term courses on data analysis and R that are being provided by reputed universities across the globe. In case you don’t wish to enrol yourself in these courses, you can find a large number of videos on the internet to get you started. From video giving you a step- wise process of downloading R to complex quantitative analysis using R, you can find a video on every aspect of R.


So, what are you waiting for? Begin your journey now!

Sources- R Programming for Data Science by Roger Peng