# Statistics.com LMS

#### Aim of Course:

This online course covers three important modeling techniques. Students will learn how to (1) construct and implement simulations to model the uncertainty in decision input variables (e.g. price, demand, etc.) and supplement the overall estimate of interest by a risk interval of possible other outcomes using risk simulation; (2) model the variability in arrivals over time (customers, cars at a toll plaza, data packets, etc.) and ensuing queues, using queuing theory; (3) how to employ decision trees to incorporate information derived from models to actually make optimal decisions.

#### Aim of Course:

In this online course, “Predictive Analytics 1 - Machine Learning Tools -
with R,” you will be introduced to the basic concepts in predictive
analytics, also called predictive modeling, the most prevalent form of
data mining. This course covers the two core paradigms that account for
most business applications of predictive modeling: classification and
prediction. In both cases, predictive modeling takes data where a
variable of interest (a target variable) is known and develops a model
that relates this variable to a series of predictor variables, also
called features. In classification, the target variable is categorical
("purchased something" vs. "has not purchased anything").

- Teacher: Online Instruction

#### Aim of Course:

This course continues the work of Predictive Analytics 1, and introduces
you to additional techniques in predictive analytics, also called
predictive modeling, the most prevalent form of data mining.

- Teacher: Online Instruction

#### Aim of Course:

This course will cover the analysis of contingency table data (tabular data in which the cell entries represent counts of subjects or items falling into certain categories).

- Teacher: Online Instruction

#### Aim of Course:

This course will show you how to use R to create statistical models and use them to analyze data.

#### Aim of Course:

This course continues the work of Predictive Analytics 1, and introduces
you to additional techniques in predictive analytics, also called
predictive modeling, the most prevalent form of data mining.

- Teacher: Online Instruction

#### Aim of Course:

This course continues the work of Predictive Analytics 1, and introduces
you to additional techniques in predictive analytics, also called
predictive modeling, the most prevalent form of data mining.

#### Aim of Course:

This course covers how to apply linear programming to complex systems to make better decisions – decisions that increase revenue, decrease costs, or improve efficiency of operations.

#### Aim of Course:

This course teaches participants how to model financial events that have uncertainties associated with them.