### Analysis of Survey Data from Complex Sample Designs

This course will teach you how to estimate variances for complex surveys and how to model the results using linear and logistic regression and other generalized linear models.

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Analysis of Survey Data from Complex Sample Designs

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Analyzing and Modeling Coronavirus Data

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Anomaly Detection

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Bayesian Statistics in R

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Biostatistics 1 – For Medical Science and Public Health

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Biostatistics 2 – For Medical Science and Public Health

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Biostatistics for College Credit

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Bootstrap Methods

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Categorical Data Analysis

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Clinical Trials – Pharmacokinetics and Bioequivalence

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Cluster Analysis

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Customer Analytics in R

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Designing Valid Statistical Studies

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Discrete Choice Modeling and Conjoint Analysis

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Ecological and Environmental Sampling

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Epidemiologic Statistics

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Financial Risk Modeling

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Forecasting Analytics

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Generalized Linear Models

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Independent Data Monitoring Committees in Clinical Trials

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Integer and Nonlinear Programming and Network Flow

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Interactive Data Visualization

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Introduction to Bayesian Computing and Techniques

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Introduction to Bayesian Hierarchical and Multi-level Models

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Introduction to Bayesian Statistics

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Introduction to Data Literacy

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Introduction to Design of Experiments

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Introduction to Item Response Theory (IRT)

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Introduction to MCMC and Bayesian Regression via rstan

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Introduction to Network Analysis

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Introduction to NLP and Text Mining

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Introduction to Python Programming

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Introduction to Resampling Methods

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Introduction to Statistical Issues in Clinical Trials

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Introduction to Structural Equation Modeling (SEM)

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Introductory Statistics for College Credit

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Many-Facet Rasch Measurement

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Mapping in R

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Matrix Algebra

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Maximum Likelihood Estimation

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Meta Analysis 1

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Meta Analysis 2

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Meta Analysis in R

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Mixed and Hierarchical Linear Models

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Modeling Count Data

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Modeling in R

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Multivariate Statistics

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NLP and Deep Learning

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Optimization with Linear Programming

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Persuasion Analytics and Targeting

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Predictive Analytics – Project Capstone

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Predictive Analytics 1 – Machine Learning Tools

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Predictive Analytics 1 – Machine Learning Tools with Python

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Predictive Analytics 1 – Machine Learning Tools with R

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Predictive Analytics 2 – Neural Nets and Regression

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Predictive Analytics 2 – Neural Nets and Regression with Python

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Predictive Analytics 2 – Neural Nets and Regression with R

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Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules

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Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules with Python

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Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules with R

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Principal Components and Factor Analysis

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Python for Analytics

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R for Statistical Analysis

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R Programming – Intermediate

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R Programming – Introduction Part 1

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R Programming – Introduction Part 2

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Rasch Measurement – Core Topics

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Rasch Measurement – Further Topics

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Recorded Webinar on Content Optimization with Multi-Armed Bandits & Python

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Regression Analysis

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Risk Simulation and Queuing

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Sample Size and Power Determination

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Spatial Statistics for GIS Using R

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SQL – Introduction to Database Queries

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Statistics 1 – Probability and Study Design

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Statistics 2 – Inference and Association

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Statistics 3 – ANOVA and Regression

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Structural Equation Modeling (SEM) Using R

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Survey Design and Sampling Procedures

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Survival Analysis

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The Ethical Practice of Data Science

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Visualization in R with ggplot2

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This course will teach you how to estimate variances for complex surveys and how to model the results using linear and logistic regression and other generalized linear models.

The Covid-19 pandemic caused by the Coronavirus has produced a wealth of data to a degree not seen in any previous pandemic, and doubtless will

In this course you will learn how to examine data with the goal of detecting anomalies or abnormal instances.

This course will teach you how to specify and run Bayesian modeling procedures using regression models for continuous, count and categorical data Using R and the associated R package JAGS.

This course will teach you the principal statistical concepts used in medical and health sciences. Basic concepts common to all statistical analysis are reviewed, and those concepts with specific importance in medicine and health are covered in detail.

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

Biostatistics for Credit reviews the procedures covered in the introductory courses Biostatistics 1 and Biostatistics 2, and covers in more detail the principal statistical concepts used in medical and health sciences.

This course will teach you the basic theory and application of the bootstrap family of procedures with the emphasis on applications.

This course will teach you the analysis of contingency table data. Topics include tests for independence, comparing proportions as well as chi-square, exact methods, and treatment of ordered data. Both 2-way and 3-way tables are covered.

This course will teach you the statistical measurement and analysis methods relevant to the study of pharmacokinetics, dose-response modeling, and bioequivalence. The course provides practical work with actual/simulated clinical trial data.

This course will teach you how to use various cluster analysis methods to identify possible clusters in multivariate data. Methods discussed include hierarchical clustering, k-means clustering, two-step clustering, and normal mixture models for continuous variables.

In this course you will work through a customer analytics project from beginning to end, using R.

This course will teach you how to design studies to produce statistically valid conclusions. Topics covered in the course include: overview of validity and bias, selection bias, information bias, and confounding bias.

This course will teach you to design appropriate conjoint and choice studies using surveys, panels, designed experiments, be able to analyze and interpret the resulting data.

This course will teach you sampling methods and analyses used to study of the density and abundance of animals and plants and other important biological variables.

This course will teach you the underlying concepts and methods of epidemiologic statistics: study designs, and measures of disease frequency and treatment effect.

This course will teach you how to model financial events that have uncertainties associated with financial events.

This course will teach you how to choose an appropriate time series model: fit the model, conduct diagnostics, and use the model for forecasting.

This course will explain the theory of generalized linear models (GLM), outline the algorithms used for GLM estimation, and explain how to determine which algorithm to use for a given data analysis.

This course will teach you the statistical display and analysis methods used in monitoring clinical trials for safety, as well as the biases and pitfalls inherent in safety review.

This course will teach you a number of advanced topics in optimization: how to formulate and solve network flow problems; how to model and solve optimization problems; how to deal with multiple objectives in optimization problems, and techniques for handling optimization problems.

This course will teach you the principles of the visual display of data both for presentation and analysis data.

This course will teach you how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using WinBUGS software.

This course will teach you how to extend the Bayesian modeling framework to cover hierarchical models and to add flexibility to standard Bayesian modeling problems.

This course will teach you the basic ideas of Bayesian Statistics: how to perform Bayesian analysis for a binomial proportion, a normal mean, the difference between normal means, the difference between proportions, and for a simple linear regression model.

This course covers how to read, understand, manipulate, and use data. There is no prerequisite knowledge for this course, but it does require access to

This course will teach you the application of DOE rather than statistical theory, and teaches full and fractional factorial designs, Plackett-Burman, Box-Behnken, Box-Wilson and Taguchi designs.

This course will teach you the statistical basis for analyzing multiple-choice survey or test data – item response theory (IRT).

In this course, students learn how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using R and rstan.

This course will teach you a mix of quantitative and qualitative methods for describing, measuring, and analyzing social networks.

This course will teach you the essential techniques of text mining, understood here as the extension of data mining’s standard predictive methods to unstructured text.

This course will introduce you to the basics of programming in Python on either Windows or Mac platform.

The course introduces the basic concepts and methods of resampling methods including bootstrap procedures and permutation with little or no complex theory or confusing notation.

This course will teach you the basic statistical principles in the design and analysis of randomized controlled trials.

This course will teach you the fundamental concepts and theory of Structural Equation Modeling, including model specification, model identification, model estimation, model testing, and model modification.

This course will teach you the equivalent of a semester course in introductory statistics.

This course will teach you the analysis and interpretation of judge-intermediated ratings, like essay grading, Olympic ice-skating, therapist ratings of patient behavior, etc.

This course will teach you how spatial data may be written/read and visualized in R, and show how publication quality maps may be produced in R, based on the GISTools package, as well as providing a review of a number of other diverse methods for visually representing geographical information in R.

This course will teach you the basics of vector and matrix algebra and operations necessary to understand multivariate statistical methods, including the notions of the matrix inverse, generalized inverse and eigenvalues and eigenvectors.

This course will teach you the derivation of maximum likelihood estimates and their properties.

This course will explain meta analysis and the methods that are used to assess multiple statistical studies on the same subject and draw conclusions.

This course will teach you advanced issues in meta-analysis and the statistical analyses that are used to synthesize summary data from a series of studies.

The course covers the fundamentals of the fixed and random effects models for meta-analysis, the assessment of heterogeneity, and evaluating bias.

This course will teach you the basic theory of linear and non-linear mixed effects models, hierarchical linear models, algorithms used for estimation, primarily for models involving normally distributed errors, and examples of data analysis.

This course will teach you regression models for count data, models with a response or dependent variable data in the form of a count or rate, Poisson regression, the foundation for modeling counts, and extensions and modifications to the basic model.

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

This course will teach you key multivariate procedures such as multivariate analysis of variance (MANOVA), principal components, factor analysis, and classification.

This course will teach you the algorithms, techniques and software used in natural language processing (NLP).

This course will teach you the use of mathematical models for managerial decision making and covers how to formulate linear programming models where multiple decisions need made while satisfying a number of conditions or constraints.

This course will teach you how to apply predictive modeling methods to identify persuadable individuals and to target voters in political campaigns.

A predictive modeling practicum for the predictive analytices course program.

This course introduces to the basic concepts in predictive analytics to visualize and explore data to understand the two core paradigms that account for most business applications of predictive modeling: classification and prediction.

This course introduces to the basic concepts in predictive analytics, with a focus on Python, to visualize and explore data that account for most business applications of predictive modeling: classification and prediction.

This course introduces to the basic concepts in predictive analytics, with a focus on R, to visualize and explore data that account for most business applications of predictive modeling: classification and prediction.

As a continuation of Predictive Analytics 1, this course introduces to the basic concepts in predictive analytics to visualize and explore predictive modeling.

As a continuation of Predictive Analytics 1, this course introduces to the basic concepts in predictive analytics, with a focus on Python, to visualize and explore predictive modeling.

As a continuation of Predictive Analytics 1, this course introduces to the basic concepts in predictive analytics, with a focus on R, to visualize and explore predictive modeling.

This course will teach you key unsupervised learning techniques of association rules – principal components analysis, and clustering – and will include an integration of supervised and unsupervised learning techniques.

This course, with a focus on Python, will teach you key unsupervised learning techniques of association rules – principal components analysis, and clustering – and will include an integration of supervised and unsupervised learning techniques.

This course, with a focus on R, will teach you key unsupervised learning techniques of association rules – principal components analysis, and clustering – and will include an integration of supervised and unsupervised learning techniques.

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.

This course will teach you the basic Python skills and data structures; how to load data from different sources, rearrange, and aggregate it; and finally how to analyze and visualize it to create high-quality products.

This course will teach you how to use R for basic statistical procedures.

This course will teach experienced data analysts a systematic overview of R as a programming language, emphasizing good programming practices and the development of clear, concise code. After completing the course, students should be able to manipulate data programmatically using R functions of their own design.

This course provides an easy introduction to programming in R.

This course is a continuation of the introduction to R programming.

This course will teach you how Rasch analysis constructs linear measures from scored observations, such as responses to multiple-choice questions, Likert scales, and quality-of-life assessments. You will learn the practical aspects of data setup, analysis, output interpretation, fit analysis, differential item functioning, dimensionality and reporting.

This course will teach you how to Rasch theory and its application in the Winsteps software begun in Practical Rasch Measurement-Core Topics. The visual step-by-step tutorial propels you along the road to success. This course introduces exciting new topics and delves into earlier topics more deeply.

An overview of visualization in Python

This course will teach you how multiple linear regression models are derived, the use software to implement them, what assumptions underlie the models, how to test whether your data meet those assumptions and what can be done when those assumptions are not met, and develop strategies for building and understanding useful models.

This course will teach you modeling technique making decisions in the presence of risk or uncertainty, including risk analysis using Monte Carlo simulation, queuing theory for problems involving waiting lines, and decision trees for analyzing problems with multiple discrete decision alternatives.

This course will teach you how to make sample size determinations for various statistical tests and for confidence intervals, as needed for experimental studies such as comparison studies, as well as for other types of experiments.

This course will teach you spatial statistical analysis methods to address problems in which spatial location. This course will explain and give examples of the analysis that can be conducted in a geographic information system such as ArcGIS or Mapinfo.

This course will teach you how to extract data from a relational database using SQL and merge data into a single file in R so that you can perform statistical operations.

This course, the first of a three-course sequence, provides an introduction to statistics for those with little or no prior exposure to basic probability and statistics.

This course will teach you the use of inference and association through a series of practical applications, based on the resampling/simulation approach, and how to test hypotheses, compute confidence intervals regarding proportions or means, computer correlations, and use of simple linear regressions.

This course, the third of a three-course sequence, provides ananalysis of variance (ANOVA) and multiple linear regression through a series of practical applications.

Structural Equation Modeling (SEM) allows you to go beyond simple single-outcome models, and deal with multiple outcomes and multi-directional causation. You will learn how to

This course will teach you the crafting of survey questions, the design of surveys, and different sampling procedures that are used in practice using basic principles of survey design.

This course will teach you the various methods used for modeling and evaluating survival data or time-to event data.

There is increasing public and corporate concern about bias and other unintended harmful effects resulting from data science models. This course, for both data science

This course will teach you ggplot as an implementation of the grammar of graphics in R. ggplot combines the advantages of base and lattice graphics while maintaining the ability to build up a plot step by step from multiple data sources.

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