Workshop on Applied Data Analytics | Ton Duc Thang University

I am delighted to announce that my colleagues and I are going to organize a workshop on Applied Data Analytics at Ton Duc Thang University, from 23/12/2018 to 29/12/2019. This workshop specifically focuses on issues related to predictive analytics in medical research and social sciences. The R language will be used in the workshop.

It is expected that after completing the workshop, participants will be able to:

· understand and use the R language for data analysis;

· visualize simple and complex data;

· conduct simple and advanced regression analyses;

· apply the logistic regression model for building predictive models;

· how to evaluate the prognostic performannce of a predictive model;

· gain basic knowledge concerning statistical learning (ie 'machine learning').

Venue: Ton Duc Thang University, 19 Nguyen Huu Tho Street, District 7, Ho Chi Minh City.

Preliminary Program

23/12/2018 - Review of R language

Lecture 1: Introduction to R language. History of the language; syntax; operators

Lecture 2: R input/output. Reading and importing data; exporting data

Lecture 3: Descriptive analyses using R. Mean, standard deviation, confidence interval, t-test, Chi squared test

Lecture 4: Handling big dataset

24/12/2018 - Data visualization

Lecture 5: Tufte's principles of graphics Lecture 6: Simple graphs. Bar plot, histogram, boxplot, scatterplot

Lecture 7: Introduction to ggplot2. New graphic language, basic syntax, options, themes

Lecture 8: Data visualization with ggplot2. Creating elegant and informative histogram, boxplot, scatterplot, network plot, etc

25/12/2018 - Simple multivariate statistics

Lecture 9: Handling missing values. Introduction to the "mice" package and multivariate imputation by chained equations.

Lecture 10: Introduction to linear discriminant analysis Lecture 11: Introduction to cluster analysis

26/12/2018 - Linear regression analysis

Lecture 12: Introductory correlation analysis and linear regression analysis. History of correlation analysis, simple linear regression, parameter estimation, residual analysis.

Lecture 13: Interpretation of linear regression model. Meaning of R squared value, residual variance, predictive interval

Lecture 14: Model selection. Introduction to AIC based procedure, LASSO, and Bayesian Model Averaging method.

Lecture 15: Prediction in multiple linear regression. Model building using multiple linear regression, evaluation of model adequacy, over-fitting, validation.

27/12/2018 - Logistic regression analysis

Lecture 16: Introduction to logistic regression. Introduction to the concept of "odds" (and odds ratio), logit function, logistic regression model for binary outcome.

Lecture 17: Multiple logistic regression model

Lecture 18: Variable selection for logistic regression model. Introduction to AIC based procedure, LASSO, and Bayesian Model Averaging method.

28/12/2018 - Prediction using logistic regression

Lecture 19: Building predictive model using logistic regression. Interpretation of metrics for model evaluation.

Lecture 20: Prognostic evaluation of logistic regression model

Lecture 21: Calbiration and discrimination

29/12/2018 - Introductory Statistical Learning

Lecture 22: Introduction to Statistical Learning

Lecture 23: Artificial Neural Network model

Lecture 24: Support Vector Machine (SVM) model

Lecture 25: Random Forest

Enrollment