1、培訓過程中，如有部分內容理解不透或消化不好，可免費在以后培訓班中重聽；
        2、課程完成后,授課老師留給學員手機和Email,保障培訓效果,免費提供半年的技術支持。
        3、培訓合格學員可享受免費推薦就業機會。

Introduction and preliminaries

Making R more friendly, R and available GUIs
Rstudio
Related software and documentation
R and statistics
Using R interactively
An introductory session
Getting help with functions and features
R commands, case sensitivity, etc.
Recall and correction of previous commands
Executing commands from or diverting output to a file
Data permanency and removing objects
Simple manipulations; numbers and vectors

Vectors and assignment
Vector arithmetic
Generating regular sequences
Logical vectors
Missing values
Character vectors
Index vectors; selecting and modifying subsets of a data set
Other types of objects
Objects, their modes and attributes

Intrinsic attributes: mode and length
Changing the length of an object
Getting and setting attributes
The class of an object
Arrays and matrices

Arrays
Array indexing. Subsections of an array
Index matrices
The array() function
The outer product of two arrays
Generalized transpose of an array
Matrix facilities
Matrix multiplication
Linear equations and inversion
Eigenvalues and eigenvectors
Singular value decomposition and determinants
Least squares fitting and the QR decomposition
Forming partitioned matrices, cbind() and rbind()
The concatenation function, (), with arrays
Frequency tables from factors
Lists and data frames

Lists
Constructing and modifying lists
Concatenating lists
Data frames
Making data frames
attach() and detach()
Working with data frames
Attaching arbitrary lists
Managing the search path
Data manipulation

Selecting, subsetting observations and variables
Filtering, grouping
Recoding, transformations
Aggregation, combining data sets
Character manipulation, stringr package
Reading data

Txt files
CSV files
XLS, XLSX files
SPSS, SAS, Stata,… and other formats data
Exporting data to txt, csv and other formats
Accessing data from databases using SQL language
Probability distributions

R as a set of statistical tables
Examining the distribution of a set of data
One- and two-sample tests
Grouping, loops and conditional execution

Grouped expressions
Control statements
Conditional execution: if statements
Repetitive execution: for loops, repeat and while
Writing your own functions

Simple examples
Defining new binary operators
Named arguments and defaults
The '...' argument
Assignments within functions
More advanced examples
Efficiency factors in block designs
Dropping all names in a printed array
Recursive numerical integration
Scope
Customizing the environment
Classes, generic functions and object orientation
Graphical procedures

High-level plotting commands
The plot() function
Displaying multivariate data
Display graphics
Arguments to high-level plotting functions
Basic visualisation graphs
Multivariate relations with lattice and ggplot package
Using graphics parameters
Graphics parameters list
Time series Forecasting

Seasonal adjustment
Moving average
Exponential smoothing
Extrapolation
Linear prediction
Trend estimation
Stationarity and ARIMA modelling
Econometric methods (casual methods)

Regression analysis
Multiple linear regression
Multiple non-linear regression
Regression validation
Forecasting from regression