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Workshops & Courses


Hands-on Data Analysis with R, University of Neuchatel, CUSO, 10 May 2016. We analyzed three data sets that lead to following topics:

  1. Welcome and Road Map
  2. Linear and Piecewise Linear Regression
  3. Prediction: GLM and Feature Selection
  4. Two Way ANOVA with Repeated Measures

and when you are interested in the R codes, simply contact me. The workshop was excellently organized and very successful  (see also Testimonials). Participants rated it with increasing number ranging from 1 to 5 with the following median (max). General rate is 4 (5), helpfulness of provided slides and R-scripts is 5 (5), presenting clarity is 4 (5).

On the 11-12 May 2015, I conducted a 2-day workshop: Applied Statistics for Computer Scientists, with R. The 2-day workshop was dedicated for Ph.D. students and postdocs of the Computer Science of CUSO (Conférence Universitaire de Suisse Occidentale), the conference of western universities in Switzerland: Bern, Fribourg, Geneva, Lausanne and Neuchâtel. It was very successful (see Testimonials). The workshop introduced the following topics, together with R implementation for statistical computing and graphics:

  1. Data Analysis & Production
  2. Introduction to Probability
  3. Foundations for Statistical Inference
  4. Two-way Table & ANOVA
  5. Multiple linear regression
  6. Multiple Logistic Regression

Courses I have taught for Coaching

Introductory Courses

  • Mathematics (the science of quantity, structure, space and change): arithmetic, algebra, geometric, analysis, optimization
  • Probability (the analysis of random phenomena)
  • Statistics (the study of collection, organization, analysis, interpretation & presentation of data)
  • Programming with R
  • Data visualization with R

Advanced Courses

  • Biostatistics
  • Quantitative Methods
  • Time Series Econometrics
  • Multivariate Analysis
  • Mathematics for Underwriter
  • Mathematics for Reinsurance
  • Probability/Measure Theory
  • Probability Modeling
  • Stochastic Processes
  • Statistical Inference
  • Computational Statistics (with R)
  • Mathematical Statistics

Special Topics

  • Hypothesis Tests (parametric & nonparametric)
  • General Linear Model (the errors follow Gaussian, the relationship is linear): Multiple Linear Regression, ANOVA
  • Generalized Linear Model (the errors are NON-Gaussian, the relationship is linear)
    • Bernoulli, Binomial, Multinomial, Poisson, Negative Binomial, Exponential, Gamma (GLM)
    • Fixed Effect, Random Effect (GLMM)
  • Generalized NON-Linear Model (the errors are NON-Gaussian, the relationship is NON-linear)
  • Discrete Time Series: ARIMA, VAR
  • Continuous Time Stochastic Processes
  • Maximum Likelihood Estimation (MLE)
  • Empirical Risk Minimization
  • Conditional Expectation
  • Statistical Learning