Aim of Course:
This course teaches participants how to model financial events that have uncertainties associated with them.
- Teacher: Huybert Groenendaal
- Teacher: Greg Nolder
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.
Available courses
Aim of Course:
This course covers the basic statistical principles in the design and analysis of randomized controlled trials.
- Teacher: Vidyadhar Phadke
- Teacher: Nand Kishore Rawat
Aim of Course:
Try out our introductory statistics series for one week - you can learn stats!
- Teacher: Peter Bruce
- Teacher: Kuber Deokar
Aim of Course:
This course will introduce the essential techniques of text mining, understood here as the extension of data mining's standard predictive methods to unstructured text.
- Teacher: Anurag Bhardwaj
Aim of Course:
The aim of this course is to teach R Programming to those with little or no programming knowledge or experience.
- Teacher: Joris Meys
Aim of Course:
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: Meena Badade
Aim of Course:
This course will teach you how to use various cluster analysis methods to identify possible clusters in multivariate data. In marketing applications, clusters of customer records are called market segments (and the process is called market segmentation).
- Teacher: Anthony Babinec
Aim of Course:
Spatial analysis often uses methods adapted from conventional analysis to address problems in which spatial location is the most important explanatory variable. This course, which is directed particularly to students with backgrounds in either computing or statistics but who lack a background in the necessary geospatial concepts, will explain and give examples of the analysis that can be conducted in a geographic information system such as ArcGIS or Mapinfo.
- Teacher: David Unwin
Aim of Course:
To provide an easy introduction to statistical inference for a single variable.
- Teacher: Michelle Everson
Aim of Course:
In this course you will work through a customer analytics project from
beginning to end, using R. You will start by gaining an understanding of the
problem and the context, and continue to clean, prepare and explore the
relevant data.
In this course, you'll learn everything you need to get you started using Python for data analysis. - See more at: http://www.statistics.com/course-catalog/python/#syllabus
- Teacher: Karolis Urbonas
Aim of Course:
Rasch analysis constructs linear measures from scored observations, such as responses to multiple-choice questions, Likert scales and quality-of-life assessments. This course covers the practical aspects of data setup, analysis, output interpretation, fit analysis, differential item functioning, dimensionality and reporting.
- Teacher: Everett Smith
Aim of Course:
This course will introduce you to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining.
- Teacher: Peter Gedeck
Aim of Course:
This course will introduce you to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining.
- Teacher: Kuber Deokar
- Teacher: Inbal Yahav
Aim of Course:
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
Aim of Course:
This course extends the Bayesian modeling framework to cover hierarchical models, and to add flexibility to standard Bayesian modeling problems.
- Teacher: Peter Congdon
Aim of Course:
In this course, you will learn how to make decisions in building a factor analysis model - including what model to use, the number of factors to retain, and the rotation method to use.
- Teacher: Anthony Babinec
Aim of Course:
You will have a chance to try the first week of our course “Predictive Analytics 1 - Machine Learning
Tools.” You will be introduced to the basic concepts in predictive
analytics, also called predictive modeling, the most prevalent form of
data mining. The full course covers the two core paradigms that account for
most business applications of predictive modeling: classification and
prediction.
- Teacher: Anthony Babinec
- Teacher: Kuber Deokar
Aim of Course:
Try out our introductory statistics series for one week - you can learn stats!
- Teacher: Peter Bruce
- Teacher: Kuber Deokar
Aim of Course:
This course covers the fundamental concepts and theory of Structural Equation Modeling: model specification, model identification, model estimation, model testing, and model modification.
- Teacher: Randall Schumacker
Aim of Course:
In this online course, you will learn about the rapidly evolving field
of Deep Learning. At the end of this
course you will understand the basic concepts underlying the
representations and methods in deep learning and see some applications
where deep learning is most effective.
- Teacher: Alan Blair