Special 3-course Package Registration

Tuition savings are available when you register simultanteously for three or more courses in the same field.

To earn this tuition reduction, you must register using our online "shopping cart" system. During the registration process, place three courses from one of the groups listed below into your shopping cart. Prior to checkout, a $199 tuition reduction is applied to your order.

NOTES:

1. All three courses must be purchased at the same time. This special tuition reduction is not available retroactively, nor can it be applied if you register for courses on different occasions.
2. You must select each course and add it to the same cart, so that the cart has 3 (or more) courses, for the savings to be applied.
3. The discount shows up in the shopping cart after the credit card information has been entered but before the "confirm" order button is selected.
4. This offer is not available on registrations received by fax or puchase order.
5. If you are taking 6 or more courses in one of these packages, you can get the $199 tuition savings twice, by registering for 3 courses, completing the transaction, then registering for the additional 3 courses in a separate transaction.

Epidemiology package

• Epi 1 - Fundamentals of Epidemiology: This is an introductory epidemiology course that emphasizes the underlying concepts and methods of epidemiology. Topics covered in the course include: study designs (clinical trials, cohort studies, case-control studies, and cross-sectional), measures of disease frequency and effect.
  
• Epi 2 - Avoiding Bias in Epidemiologic Research: This is a second level epidemiology course that emphasizes the underlying concepts and methods for addressing validity and bias issues in epidemiologic research. Topics covered in the course include: overview of validity and bias, selection bias, information bias, and confounding bias.
  
• Epi 3 - Analysis of Epidemiologic Data: This is a second level epidemiology course that emphasizes methods for analyzing epidemiologic data. Topics covered in the course include: simple analysis of 2x2 tables, control of extraneous variables (including an introduction to logistic regression), stratified analysis, and matching.

Data Mining package

(Choose any three or more)

• Introduction to Data Mining: This course covers the two core paradigms that account for most business applications of data mining: classification and prediction. The course includes hands-on work with XLMiner, a data-mining add-in for Excel.
• Data Mining: Unsupervised Techniques: 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.
• Decision Trees and Rule-Based Segmentation: Rule induction is an important component of data mining, and this course covers two main styles of generating rules.
• Text Mining: 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.
• Cluster Analysis: 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.
• Natural Language Processing: This course is designed to give you an introduction to the algorithms, techniques and software used in natural language processing (NLP).

Clinical Trials package

(Choose any three or more)

• Biostatistics 1: This course covers sensitivity-specificity and predictive values of medical tests, confidence intervals, medical vs. statistical significance, and chi-square, Student's t and ANOVA F-tests, including multiple comparisons.
• Biostatistics 2: 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.
• Survival Analysis: This course describes the various methods used for modeling and evaluating survival data, also called time-to-event data.
• Introduction to Statistical Issues in Clinical Trials: This course covers the basic statistical principles in the design and analysis of randomized controlled trials.
• Clinical Trials - Practicum 1: In this course, you will apply those principles to the circumstances of actual trials, and you will also cover two topics that are central to clinical trials - pharmacokinetics (PK) and dose-response modeling.
• Clinical Trials - Practicum 2 (Drug Trials): This course provides additional practical work with actual clinical trial data.
• Avoiding Selection Bias in Randomized Clinical Trials : This course covers the essential concepts required to design rigorous randomized trials so as to ensure valid treatment comparisons.
• Bayesian Clinical Trials: This course covers the essentials of Bayesian analysis in randomized clinical trials and the evaluation of medical interventions. We will discuss the distinctions between Bayesian analysis and the more traditional frequentist analysis, and the pros and cons of each approach.
• Adaptive Clinical Trials: This course will teach you how to design, monitor and analyze clinical trials using statistically sound principles that incorporate interim looks at the data, possible early stopping, and interim re-estimation of power and required sample size. It covers group sequential designs and adaptive methods of sample-size re-estimation.
• Biostatistics 1: This course covers sensitivity-specificity and predictive values of medical tests, confidence intervals, medical vs. statistical significance, and chi-square, Student's t and ANOVA F-tests, including multiple comparisons.
• Biostatistics 2: 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.
• Survival Analysis: This course describes the various methods used for modeling and evaluating survival data, also called time-to-event data.
• Clinical Trial Safety Monitoring: This course describes how the safety of clinical trials in the pharmaceutical industry is assured through data monitoring committees (DMC's).

R Package

(Choose any 3 or more)

• Introduction to R - Statistical Analysis: This course will provide an easy introduction to R and its use in organizing and exploring data. Once you've completed this course you'll be able to enter, save, retrieve, manipulate, summarize and display data using R.
• Introduction to R - Data Handling: This course will provide an easy introduction to R and its use in organizing and exploring data. Once you've completed this course you'll be able to enter, save, retrieve, manipulate, summarize and display data using R.
• Graphics in R: The aim of this course is to teach you how to produce statistical plots of data using the R language and environment for statistical computing and graphics. The creation of standard plots such as scatterplots, bar plots, histograms, and boxplots will be covered and time will be spent on the underlying model used to produce plots in R so that you can extensively customize these plots.
• Modeling in R: This course will show you how to use R to create statistical models and use them to analyze data.
• Statistical Analysis of Microarray Data with R: This course will acquaint you with the process of microarray data mining from beginning to end. You will learn how to how to preprocess the data, estimate gene expression patterns, cluster genes to detect interesting gene expression patterns, and classify experiments (subjects) based on gene expression patterns. Illustrations of the statistical issues involved at the various stages of the analysis will use real data sets from DNA microarray experiments.

Modeling package

(Choose any 3 or more)

• Logistic Regression: Logistic regression extends ordinary least squares (OLS) methods to model data with binary (yes/no, success/failure) outcomes. Rather than directly estimating the value of the outcome, logistic regression allows you to estimate the probability of a success or failure.
• Advanced Logistic Regression: After taking this course, participants will be able to specify, implement and interpret the output of a variety of advanced logistic regression models not covered in the first course, "Logistic Regression."
• Generalized Linear Models (GLM): 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. GLM allows the modeling of responses, or dependent variables, that take the form of counts, proportions, dichotomies (1/0), positive continuous values, as well as values that follow the normal Gaussian distribution. Logistic, Poisson, and negative binomial regression models are three of the most noteworthy GLM family members.
• Modeling Longitudinal and Panel Data: 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. GLM allows the modeling of responses, or dependent variables, that take the form of counts, proportions, dichotomies (1/0), positive continuous values, as well as values that follow the normal Gaussian distribution. Logistic, Poisson, and negative binomial regression models are three of the most noteworthy GLM family members.
• Modeling Count Data: This course deals with regression models for count data; i.e. models with a response or dependent variable data in the form of a count or rate. The course will cover Poisson regression, the foundation for modeling counts, as well as 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.