Data Science

Stay ahead of the curve with human, not just artificial intelligence.

VO provides a comprehensive set of analysis services based both on statistical and machine learning methods. A problem or a data set can be analyzed by several methods. It is usually a good idea to try different approaches, compare their results, and then choose a model that suits the problem well.

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There are five broad groups of analysis services we can provide:

Classification

When the outcome variable is discrete or categorical, the objective of the data mining exercise is to classify the records into the discrete classes or categories. VO offers several techniques for classification:

□ Discriminant Analysis
□  Logistic Regression with best subset selection
□  Classification Trees
□  Naive Bayes Classifier
□  Neural Networks
□  k-Nearest Neighbors

Prediction

When the outcome variable is continuous, the objective is to predict the value of the outcome variable for each of the data records. VO offers the following methods of prediction:

□  Multiple Linear Regression with best subset selection
   □  k-Nearest Neighbors
   □  Regression Trees
   □  Neural Networks

Affinity Analysis

Some problems involve detecting association among the properties of data records. VO can generate association rules for showing which attributes of the data occur frequently together. One common application is to determine groups of products customers are likely to buy together, also known as Market Basket Analysis.

Time Series

VO offers time series forecasting, with the industrial standardized exploratory and smoothing techniques, as well as ARMA and ARIMA modeling.

Data Reduction and Exploration

It is often useful or necessary to reduce the dimensionality of data into only a few attributes that matter more than others. In this situation, we do not attempt to classify or predict an outcome variable. Instead, the objective is to discover similarities in records and group them together using the available attributes. One such method involves deciding which variables matter most in explaining differences among records. Other methods categorize data into clusters that can be represented as a new categorical variable added to the data. VO offers the following methods of data exploration and reduction:

   □  Principal Components Analysis
   □  k-Means Clustering
   □  Hierarchical Clustering

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