#### Aim of Course:

The aim of the course is to give you the skills to work with a variety of data types and data sources in R.

- Teacher: Hongcheng Li

The Institute for Statistics Education at Statistics.com

is the leading provider of online education in statistics.

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

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

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.

The aim of the course is to give you the skills to work with a variety of data types and data sources in R.

- Teacher: Hongcheng Li

After taking this course, you will be able to install and run rjags, a program for Bayesian analysis within R. Using R and rjags, you will learn how to specify and run Bayesian modeling procedures using regression models for continuous, count and categorical data.

This course covers a number of advanced topics in optimization. Students taking this course will learn to specify and implement optimization models that solve network problems (what is the shortest path through a network, what is the least cost way to route material through a network with multiple supply nodes and multiple demand nodes).

- Teacher: Cliff Ragsdale

This course describes the various methods used for modeling and evaluating survival data, also called time-to-event data.

- Teacher: Anthony Babinec

This course covers the analysis of data gathered in surveys.

- Teacher: Anthony Babinec

After taking this course, you will be equipped to introduce natural language processing (NLP) processes into your projects and software applications.

- Teacher: Nitin Indurkhya

This course covers the basic theory and application of the bootstrap family of procedures, with the emphasis on applications.

- Teacher: Robert LaBudde

To provide an easy introduction to ANOVA and multiple linear regression through a series of practical applications.

- Teacher: Meena Badade

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: Tal Galili

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: Anuja Kulkarni

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

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: Kuber Deokar
- 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: 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: Kuber Deokar

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

This course, the second in a three-course sequence, provides an easy introduction to inference and association through a series of practical applications, based on the resampling/simulation approach.

- Teacher: Peter Bruce

After taking this course, participants will be able to design appropriate conjoint and choice studies, using surveys, panels, and designed experiments. They will also be able to analyze and interpret the resulting data.

- Teacher: Kuber Deokar
- Teacher: Shweta Jadhav

This course covers clinical trial designs including randomized controlled trials, ROC curves, CI and tests for relative risk and odds ratio, and an introduction to survival analysis.

- Teacher: Abhaya Indrayan

To provide an easy introduction to statistical inference for a single variable.

- Teacher: Meena Badade