**Workshop**

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

- Welcome and Road Map
- Linear and Piecewise Linear Regression
- Prediction: GLM and Feature Selection
- 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:

- Data Analysis & Production
- Introduction to Probability
- Foundations for Statistical Inference
- Two-way Table & ANOVA
- Multiple linear regression
- 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