# Visual Data Exploration

Visual data exploration is a mandatory intial step whether or not more formal analysis follows. When combined with descriptive statistics, visualization provides an effective way to identify summaries, structure, relationships, differences, and abnormalities in the data. Often times no elaborate analysis is necessary as all the important conclusions required for a decision are evident from simple visual examination of the data. Other times, data exploration will be used to help guide the data cleaning, feature selection, and sampling process. Regardless, visual data exploration is about investigating the characteristics of your data set. To do this, we typically create numerous plots in an interactive fashion. This tutorial will show you how to create plots that answer some of the fundamental questions we typically have of our data.

# Exponential Smoothing Models

Exponential forecasting models are smoothing methods that have been around since the 1950s and are extremely effective. Where niave forecasting places 100% weight on the most recent observation and moving averages place equal weight on k values, exponential smoothing allows for weighted averages where greater weight can be placed on recent observations and lesser weight on older observations. Exponential smoothing methods are intuitive, computationally efficient, and generally applicable to a wide range of time series. Consequently, exponentially smoothers are great forecasting tools to have and this tutorial will walk you through the basics.

# Support Vector Machines

The advent of computers brought on rapid advances in the field of statistical classification, one of which is the Support Vector Machine, or SVM. The goal of an SVM is to take groups of observations and construct boundaries to predict which group future observations belong to based on their measurements. The different groups that must be separated will be called “classes”. SVMs can handle any number of classes, as well as observations of any dimension. SVMs can take almost any shape (including linear, radial, and polynomial, among others), and are generally flexible enough to be used in almost any classification endeavor that the user chooses to undertake. This new tutorial will introduce you to this supervised classification technique.

# Introduction to Neural Networks

Artificial neural networks (ANNs) describe a specific class of machine learning algorithms designed to acquire their own knowledge by extracting useful patterns from data. ANNs are function approximators, mapping inputs to outputs, and are composed of many interconnected computational units, called neurons. Each individual neuron possesses little intrinsic approximation capability; however, when many neurons function cohesively together, their combined effects show remarkable learning performance. A new set of tutorials introduce you to this popular analytic technique.

# Imprecise Regression

Imprecise regression is a generalization of linear regression that gives us stronger tools for modeling uncertainty. In a typical linear regression setting, we consider the input data to be precise observations: single points. In imprecise regression, we generalize the notion of observations to intervals rather than points. This allows us to more accurately represent scenarios with measurement error or other sources of uncertainty that make our input data “fuzzy”. This tutorial is based on the imprecise regression work in Cattaneo and Wiencierz (2012) and will teach you the fundamentals of this technique.