Program 2022-2023

• Module 1 - Getting Started with R Software for Data Analysis
• Module 2 - Design and Analysis of Randomized Clinical Trials => This course is offered as a microcredential only.
• Module 3 - Drawing Conclusions from Data: an Introduction
• Module 4 - Getting Started with Python for Data Scientists
• Module 5 - Exploiting Sources of Variation in your Data: the ANOVA Approach
• Module 6 - Getting Started with NVivo for Qualitative Data Analysis
• Module 7 - Leverage your R Skills: Data Wrangling & Plotting with Tidyverse
• Module 8 - Dynamic Report Generation with R Markdown
• Module 9 - Explaining and Predicting Outcomes with Linear Regression
• Module 10 - Mastering R Skills: Selected Topics for Successful Programming
• Module 11 - Multilevel Analysis for Grouped and Longitudinal Data
• Module 12 - Machine Learning with Python
• Module 13 - Building Interactive Apps with Shiny© in R
• Module 14 - Artificial Neural Networks: from the Ground Up
  
• Module 15 - Time-to-Event Analysis (Survival) with applications to Health sciences and Industry

Why data analysis?

The power of data and the information therein is entering the heart of almost any section of society. We are discovering that processes can be better understood and controlled, predictions made, causal effects estimated and decisions optimized. Reliable results follow when studies have been appropriately designed, data carefully gathered and analyzed. Scientists and professionals stay ahead if they keep learning from their data. They add tremendously to their market value when data analytic skills merge their subject matter expertise.

We aim to provide insight in the basics of statistical research while developing the technical skills to come to results with statistical software. Blended learning with hands-on sessions on PC’s or laptops allows participants to gain firsthand experience in applying the knowledge.

Our courses target professionals and the academically trained, who wish to become confident data analysts, refresh their knowledge or discover new areas of research. The program’s modular architecture facilitates flexible entry and adaptive training trajectories.

Microcredentials

A number of closely related trainings in this program are offered as microcredentials. Read more...

Register

Send is this registration form via e-mail no later than 3 working days before the start of the first selected training.

• Registration form

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Previous editions

With a list of the more advanced topics taught that year.

• 2021-2022
  • Single Cell Seq Data Analysis Boot Camp
  • Identifying Latent Data Structures: Structural Equation Modeling
  • High Dimensional Data Analysis
  • From Prior Belief to Data Driven Evidence: Bayesian Data Analysis in Action
  • Microbiome Data Analysis Boot Camp
  • From Language to Information: Natural Language Programming
  • Building Interactive Apps with Shiny in R
  • Artificial Neural Networks: from the Ground Up
  • Machine Learning with Python
• 2020-2021
  • Extensions to the Design and Analysis of Case-Control Studies
  • Survival Analysis and Competing Risks
  • Multilevel Analysis for Grouped and Longitudinal Data
  • Machine Learning with Python
  • Propensity Score Methods
• 2019-2020
• 2018-2019 (Experimental Design, Data Mining, Causal Inference)
• 2017-2018
  • Multilevel Analysis for Grouped and Longitudinal Data
  • Structural Equation Modeling
  • Multivariate Statistics
  • Causal Inference
• 2016-2017
  • Sample Size Calculations
  • Multilevel Analysis for Grouped and Longitudinal Data
  • Nonparametric Methods
  • Missing Data
• 2015-2016
  • Causal Mediation Analysis, Experimental Design
  • Multilevel Analysis for Grouped and Longitudinal Data
  • Applied Longitudinal Analysis
• 2014-2015
  • Bayesian Statistics
  • Survival Analysis and Competing Risks
  • Multilevel Analysis for Grouped and Longitudinal Data
  • Data Mining
• 2013-2014
  • Applied Longitudinal Analysis
  • Multilevel Analysis for Grouped and Longitudinal Data
  • Survey Analysis
  • Nonparametric Methods
• 2012-2013
  • Data Mining
  • Multilevel Analysis for Grouped and Longitudinal Data
  • Survival Analysis
• 2011-2012
  • Multivariate Statistics
  • Multilevel Analysis for Grouped and Longitudinal Data
  • Structural Equation Modeling
  • Logistic Regression
  • Applied Longitudinal Analysis
• 2010-2011
  • Causal Mediation Analysis
  • Design and Analysis of Clinical Trials
  • Multilevel Analysis for Grouped and Longitudinal Data
  • Multiple Testing for Genomic Analysis
• 2009-2010
  • Applied Longitudinal Analysis
  • Survey Analysis
• 2008-2009
  • Multilevel Analysis for Grouped and Longitudinal Data
  • Introduction to Simulation Techniques
  • Multivariate Statistics
  • Logistic Regression
• 2007-2008
  • Survival Analysis
  • Missing Data
  • Data Mining
• 2006-2007
  • Short Course in Family-Based Genetic Association Testing
  • Design and Analysis of Clinical Trials
  • Logistic Regression
  • Multilevel Analysis for Grouped and Longitudinal Data
• 2005-2006
  • Survey Analysis
  • Applied Longitudinal Analysis
  • Structural Equation Modeling
• 2004-2005
  • Short Course on Survival Analysis
  • Applied Categorical Data Analysis
• 2003-2004
  • Categorical Data Analysis
  • Structural Equation Modeling
  • Robust Statistics
• 2002-2003
  • Logistic Regression
  • Survey Analysis
  • Stochastic Simulation Methods
  • Experimental Design
• 2001-2002
  • Short Course on Survival Analysis
  • Categorical Data Analysis