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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Bayesian Statistics in R
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.
Biostatistics 1 – For Medical Science and Public Health
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.
Biostatistics 2 – For Medical Science and Public Health
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 College Credit
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.
Bootstrap Methods
This course will teach you the basic theory and application of the bootstrap family of procedures with the emphasis on applications.
Calculus Review
This course is a three-week primer on calculus. Calculus at this level concerns itself with three primary concepts: the limit, the derivative and the integral.
Categorical Data Analysis
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.
Clinical Trials – Pharmacokinetics and Bioequivalence
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.
Customer Analytics in R
In this course you will work through a customer analytics project from beginning to end, using R.
Designing Valid Statistical Studies
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.
Discrete Choice Modeling and Conjoint Analysis
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.
Diseño y Ejecución de Encuestas por Muestreo
Este curso cubre temas fundamentales relacionados a la metodología de las encuestas por muestreo con un énfasis en los procesos de diseño de muestras, selección
Epidemiologic Statistics
This course will teach you the underlying concepts and methods of epidemiologic statistics: study designs, and measures of disease frequency and treatment effect.
Financial Risk Modeling
This course will teach you how to model financial events that have uncertainties associated with financial events.
Forecasting Analytics
This course will teach you how to choose an appropriate time series model: fit the model, conduct diagnostics, and use the model for forecasting.
Forecasting Analytics
This course will teach you how to choose an appropriate time series model: fit the model, conduct diagnostics, and use the model for forecasting.
Fundamentos de Encuestas de Hogares
Este curso cubre temas fundamentales relacionados con evaluar la necesidad conducir una encuesta. En vista de la complejidad de las tareas que se llevan a
Generalized Linear Models
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.
Independent Data Monitoring Committees in Clinical Trials
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.
Integer and Nonlinear Programming and Network Flow
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.
Interactive Data Visualization with Tableau
This course will teach you the principles of the visual display of data both for presentation and analysis data.
Introduction to Bayesian Computing and Techniques
This course will teach you how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using WinBUGS software.
Introduction to Bayesian Hierarchical and Multi-level Models
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.
Introduction to Bayesian Statistics
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.
Introduction to Data Literacy
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
Introduction to Design of Experiments
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.
Introduction to Item Response Theory (IRT)
This course will teach you the statistical basis for analyzing multiple-choice survey or test data – item response theory (IRT).
Introduction to MCMC and Bayesian Regression via rstan
In this course, students learn how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using R and rstan.
Introduction to Network Analysis
This course will teach you a mix of quantitative and qualitative methods for describing, measuring, and analyzing social networks.
Introduction to NLP and Text Mining
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.
Introduction to Python Programming
This course will introduce you to the basics of programming in Python on either Windows or Mac platform.
Introduction to R Programming
This course provides an easy introduction to programming in R.
Introduction to Resampling Methods
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.
Introduction to Statistical Issues in Clinical Trials
This course will teach you the basic statistical principles in the design and analysis of randomized controlled trials.
Introduction to Structural Equation Modeling (SEM)
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.
Introductory Statistics for Credit
This course will teach you the equivalent of a semester course in introductory statistics.
Logistic Regression
This course will teach you logistic regression ordinary least squares (OLS) methods to model data with binary outcomes rather than directly estimating the value of the outcome, logistic regression allows you to estimate the probability of a success or failure.
Many-Facet Rasch Measurement
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.
Mapping in R
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.
Matrix Algebra
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.
Maximum Likelihood Estimation
This course will teach you the derivation of maximum likelihood estimates and their properties.
Meta Analysis 1
This course will explain meta analysis and the methods that are used to assess multiple statistical studies on the same subject and draw conclusions.
Meta Analysis 2
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.
Meta Analysis in R
The course covers the fundamentals of the fixed and random effects models for meta-analysis, the assessment of heterogeneity, and evaluating bias.
Mixed and Hierarchical Linear Models
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.
Modeling Count Data
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.
Modeling in R
This course will show you how to use R to create statistical models and use them to analyze data.
Multivariate Statistics
This course will teach you key multivariate procedures such as multivariate analysis of variance (MANOVA), principal components, factor analysis, and classification.
NLP and Deep Learning
This course will teach you the algorithms, techniques and software used in natural language processing (NLP).
Optimization with Linear Programming
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.
Persuasion Analytics and Targeting
This course will teach you how to apply predictive modeling methods to identify persuadable individuals and to target voters in political campaigns.
Predictive Analytics – Project Capstone
A predictive modeling practicum for the predictive analytices course program.
Predictive Analytics 1 – Machine Learning Tools
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.
Predictive Analytics 1 with Python – Machine Learning Tools
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.
Predictive Analytics 1 with R – Machine Learning Tools
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.
Predictive Analytics 2 – Neural Nets and Regression
As a continuation of Predictive Analytics 1, this course introduces to the basic concepts in predictive analytics to visualize and explore predictive modeling.
Predictive Analytics 2 with Python – Neural Nets and Regression
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.
Predictive Analytics 2 with R – Neural Nets and Regression
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.
Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules
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.
Predictive Analytics 3 with Python – Dimension Reduction, Clustering, and Association Rules
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.
Predictive Analytics 3 with R – Dimension Reduction, Clustering, and Association Rules
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.
Predictive Analytics for Healthcare
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.
Principal Components and Factor Analysis
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.
Python for Analytics
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.
R for Statistical Analysis
This course will teach you how to use R for basic statistical procedures.
R Programming – Intermediate
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.
R Programming – Introduction Part 2
This course is a continuation of the introduction to R programming.
Rasch Measurement – Core Topics
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.
Rasch Measurement – Further Topics
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.
Recorded Webinar on Content Optimization with Multi-Armed Bandits & Python
An overview of visualization in Python
Regression Analysis
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.
Responsible Data Science
Public and corporate concern about bias and other unintended harmful effects resulting from data science models has resulted in greater attention to the ethical practice
Risk Simulation and Queuing
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.
Sample Size and Power Determination
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.
Spatial Statistics for GIS Using R
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.
SQL – Introduction to Database Queries
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.
Statistical and Machine Learning Methods for Analyzing Clusters and Detecting Anomalies
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.
Structural Equation Modeling (SEM) Using R
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
Survey Design and Sampling Procedures
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.
Survival Analysis
This course will teach you the various methods used for modeling and evaluating survival data or time-to event data.
Visualization in R with ggplot2
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.