#### 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.

- Teacher: Cliff Ragsdale

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We offer 100+ courses in introductory and advanced statistical subjects.

Students from around the world study with leading authorities via private discussion boards

on flexible schedules.

Teaching assistants provide individual responses in practical exercises.

Statistics.com confers CEU's, Records of Completion, and Certificates in advanced statistical study.

Teaching assistants provide individual responses in practical exercises.

Statistics.com confers CEU's, Records of Completion, and Certificates in advanced statistical study.

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.

- Teacher: Cliff Ragsdale

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: Kuber Deokar

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: Peter Gedeck

This course covers key unsupervised learning techniques: association rules, principal components analysis, and clustering. The course will include an integration of supervised and unsupervised learning techniques.

- Teacher: Shweta Jadhav

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: Inbal Yahav

In this online course, “Predictive Analytics 1 - Machine Learning Tools -
with Python,” you will be introduced to the basic concepts in
predictive analytics, also called predictive modeling, the most
prevalent form of data mining.

- Teacher: Kuber Deokar
- Teacher: Peter Gedeck
- Teacher: Takis Tzetzos

This course will introduce you to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining.

- Teacher: Anthony Babinec

The aim of this course is to teach R Programming to those with little or no programming knowledge or experience.

- Teacher: Dipanjan Sarkar

In this online course, “R Programming Intro 1,” you will be introduced
to basic concepts in computer programming via R - it is for those who
have had little or no experience in programming.

- Teacher: Poonam Tribhuwan

The goal of this course is to introduce the basics of programming in
Python, on either Windows or Mac. You will use both Jupyter notebooks
and standard script editors, and work through simple arithmetic
operations, statistical operations, variables, keywords, lists, arrays,
and dictionaries. You'll use conda to install modules and close with
some data visualizations.

- Teacher: Stan Blank
- Teacher: Takis Tzetzos