## Data Science in Banking and Finance

Data Science in Banking and Finance, When was the last time you went to the bank or wrote a cheque? Although the classic chequebook still has its uses, your debit card and mobile banking...

Skip to content
## finnstats Blog

##
Data Science in Banking and Finance

##
How To Become A Quantitative Analyst

##
Exponential Smoothing Forecast in Time Series

##
Matthews Correlation Coefficient in R

##
Replace the first non-missing value in R

##
ave for average calculation in R

##
When to Use plotly?

##
PCA for Categorical Variables in R

##
How to combine Multiple Plots in R

##
Missing Value Imputation in R

##
Principal Component Analysis Advantages

Data Science in Banking and Finance, When was the last time you went to the bank or wrote a cheque? Although the classic chequebook still has its uses, your debit card and mobile banking...

How To Become A Quantitative Analyst?, The financial sector must also deal with a significant increase in unpredictability in order to adapt to the needs of today’s globally integrated economies. To operate in today’s...

Exponential Smoothing Forecast in Time Series, A forecasting technique for univariate time series data is exponential smoothing. With this strategy, forecasts are weighted averages of historical observations, with the weights of older observations decreasing...

Matthews Correlation Coefficient in R, We can evaluate a classification model’s effectiveness using a metric called the Matthews correlation coefficient (MCC). How to perform Rolling Correlation in R ยป It is determined by: MCC =...

Replace the first non-missing value in R, to retrieve the first non-missing value in each place of one or more vectors, use the coalesce() function from the dplyr package in R. There are two...

ave for average calculation in R, In this tutorial, the R programming language’s ave function is used to calculate averages. The article will include two instances of the ave function in use. The tutorial...

When to Use plotly?, as you can see, has a number of features that make it exciting and fun to use. There are numerous situations where ggplot or plotly could be used, but the...

PCA for Categorical Variables in R, Using Principal Component Analysis to minimize the dimensionality of your data frame may have crossed your mind (PCA). However, can PCA be applied to a data set with...

How to combine Multiple Plots in R, recently came across Thomas Lin Pedersen’s patchwork program, and how simple it is to use this package to integrate numerous ggplot2 plots into a single plot composition....

Missing Value Imputation in R, Every data user is aware of the problem: Nearly all data sets contain some missing data, which can cause major issues like skewed estimations or decreased efficiency owing to...

Principal Component Analysis Advantages, with the help of Principal Component Analysis (PCA), a statistical technique, we are able to reduce the number of features in our data from a large number to just a...