Excel to Spotfire: Targeting Missing Values
In Data Science, prior to using any analytics we always face the problem of having missing values. Deciding when to delete cases or fill missing values is totally dependent on …
The Art of Data Simulation
Most problems in the scientific world are about understanding different phenomena. We want to learn the characteristics and patterns of the systems we study to be able to preview and …
Spatial Objects Using TERR
A key part of analytics in the oil and gas industry is evaluating opportunities at different locations. Space is always present when looking for profitable development projects. We usually look …
Linear Regression, the simplest Machine Learning Model
Linear Regression models are the simplest linear models available in statistical literature. While the assumptions of linearity and normality seem to restrict the practical use of this model, it is …
Missing Value Imputation with Data Augmentation in R
Incomplete data is a problem that Data Scientists face every day. Most common practices vary from complete deletion of the observations with missing values, substitution by a fixed value, or …
Using the “spTimer” Package to Model Spatio-Temporal Data in R
The “spTimer” package uses three Bayesian models to fit Spatio-Temporal Data. The data may be given at sparse spatial stations, where observations at each station are considered time series. The …