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.
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
Participants wishing to participate in the workshop must register before 8/12/2018.
Please contact Ms Tran Huynh at email@example.com
Alternatively, participants can register using the following google form:
Detail in Vietnamese: