文件列表:

E01 - Normal Estimates from Truncated Samples (0, 2018-10-11)
E01 - Normal Estimates from Truncated Samples\E01 - Exercise1 - Normal Estimates from Truncated Samples.R (6697, 2018-10-11)
E01 - Normal Estimates from Truncated Samples\E01 - Exercise1 - Normal Estimates from Truncated Samples.Rmd (15299, 2018-10-11)
E01 - Normal Estimates from Truncated Samples\E01_-_Exercise1_-_Normal_Estimates_from_Truncated_Samples.html (1202328, 2018-10-11)
E02 - The Central Limit Theorem (0, 2018-10-11)
E02 - The Central Limit Theorem\E02 - Exercise2 - The Central Limit Theorem.R (3282, 2018-10-11)
E02 - The Central Limit Theorem\E02 - The Central Limit Theorem.Rmd (3163, 2018-10-11)
E02 - The Central Limit Theorem\E02_-_The_Central_Limit_Theorem.html (1164950, 2018-10-11)
Introduction-to-R-for-Data-Science.Rproj (205, 2018-10-11)
S01 - Introduction to R from RStudio IDE (0, 2018-10-11)
S01 - Introduction to R from RStudio IDE\S01 - Introduction to R from RStudio IDE.nb.html (1613611, 2018-10-11)
S01 - Introduction to R from RStudio IDE\S01 - Introduction to R from RStudio IDE.rmd (15466, 2018-10-11)
S01 - Introduction to R from RStudio IDE\S01 - Introduction to R from the RStudio IDE.R (7366, 2018-10-11)
S01 - Introduction to R from RStudio IDE\a.rds (44, 2018-10-11)
S01 - Introduction to R from RStudio IDE\obj.RData (70, 2018-10-11)
S02 - Data Structures in R (0, 2018-10-11)
S02 - Data Structures in R\S02 - Data Structures in R.R (9530, 2018-10-11)
S02 - Data Structures in R\S02 - Data Structures in R.nb.html (1430537, 2018-10-11)
S02 - Data Structures in R\S02 - Data Structures in R.rmd (20449, 2018-10-11)
S03 - Control flow and Functions (0, 2018-10-11)
S03 - Control flow and Functions\S03 - Control flow and Functions.R (6613, 2018-10-11)
S03 - Control flow and Functions\S03 - Control flow and Functions.Rmd (16760, 2018-10-11)
S03 - Control flow and Functions\S03_-_Control_flow_and_Functions.html (793768, 2018-10-11)
S04 - Strings in R (0, 2018-10-11)
S04 - Strings in R\S04 - Strings in R.R (4838, 2018-10-11)
S04 - Strings in R\S04 - Strings in R.Rmd (13597, 2018-10-11)
S04 - Strings in R\S04_-_Strings_in_R.html (795593, 2018-10-11)
S05 - Data Wrangling with {dplyr} and {tidyr} (0, 2018-10-11)
S05 - Data Wrangling with {dplyr} and {tidyr}\S05 - Data Wrangling with {dplyr} and {tidyr}.R (8275, 2018-10-11)
S05 - Data Wrangling with {dplyr} and {tidyr}\S05 - Data Wrangling with {dplyr} and {tidyr}.Rmd (19601, 2018-10-11)
S05 - Data Wrangling with {dplyr} and {tidyr}\S05_-_Data_Wrangling_with_{dplyr}_and_{tidyr}.html (828299, 2018-10-11)
S06 - Exploratory Data Analysis (EDA) (0, 2018-10-11)
S06 - Exploratory Data Analysis (EDA)\S06 - Exploratory Data Analysis (EDA).Rmd (15868, 2018-10-11)
S06 - Exploratory Data Analysis (EDA)\S06 -Exploratory Data Analysis (EDA).R (4777, 2018-10-11)
S06 - Exploratory Data Analysis (EDA)\S06_-_Exploratory_Data_Analysis__EDA_.html (1168718, 2018-10-11)
S07 - Probability Functions in R (0, 2018-10-11)
S07 - Probability Functions in R\S07 - Probability Functions in R.R (11818, 2018-10-11)
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# Introduction to R for Data Science # Course Notes :: Autumn 2018 Lecturer: [Goran S. Milovanovic](http://www.exactness.net/) Organized by:[Goran S. Milovanovic](http://www.exactness.net/)+ [Startit](www.en.startit.rs), Belgrade, Autumn 2016. **Description.** The course encompasses an introduction to R data structures, control flow, and functions, and then progresses towards the basics of the Linear Model in R: linear correlation, simple and multiple linear regression, t-tests, and ANOVA. Probability functions are discussed in a separate session that preceeds the modeling phase. Elementary data vizualization with {base} graphics, {lattice}, and {ggplot2}, as well as data wrangling with {dplyr} and {tidyr} are also covered. Several exercises aim at better understanding of doing simulation and model fitting in R, encompassing the demonstration of statistical experiments (nicely explained numerical simulations) in estimation theory, Bayesian inference, and more. More advanced material on Generalized Linear Models, General Optimization Methods, and Dimensionality Reduction with PCA and MDS will be added gradually. Depending on the participant's interests, we also may add something on text-mining with {tm} and LDA from {topicmodels}, as well as more typical machine learning stuff (Clustering, Decision Trees and Random Forests, SVM, etc). ------ **Course Structure:** + The course is organized in Sessions (S01, S02, ...) and Exercises (E01, E02, ...). + Each Session/Exercise is related to three files: an .R script, an .Rmd Rmarkdown file, and a knitted .html file. These three files share a common prefix relating them to the respective Session/Exercise. ------ **Contact.** The course organized in cooperation of [Data Science Serbia](www.datascience.rs) and [Startit](www.en.startit.rs) in Belgrade is free, while Mr. Kovac and I volunteer. However, all course material and learning methods have already been applied in professional settings. If you are an individual, represent an organization, or a company, you are welcome to contact me to learn more about the commercial verion of this course, e-mail: **goran.s.milovanovic@gmail.com** ------ **Course photos** **Photo 1A. Introduction to R for Data Science** :: Startit Centre, Savska 5, Belgrade, May 2016.  **Photo 1B. Introduction to R for Data Science** :: Startit Centre, Savska 5, Belgrade, May 2016.  **Photo 1C. Introduction to R for Data Science** :: Startit Centre, Savska 5, Belgrade, May 2016.  ------ 

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